Add files using upload-large-folder tool

chat_template.jinja

@@ -0,0 +1,159 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'time_series' in item or item.type == 'time_series' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain time series.') }}
+                {%- endif %}
+                {{- '<|ts|><TS_CONTEXT><|/ts|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,792 @@
+{
+    "architectures": [
+        "InternS2PreviewForConditionalGeneration"
+    ],
+    "auto_map": {
+        "AutoConfig": "configuration_interns2_preview.InternS2PreviewConfig",
+        "AutoModelForCausalLM": "modeling_interns2_preview.InternS2PreviewForCausalLM",
+        "AutoModel": "modeling_interns2_preview.InternS2PreviewModel",
+        "AutoModelForImageTextToText": "modeling_interns2_preview.InternS2PreviewForConditionalGeneration",
+        "AutoModelForMultimodalLM": "modeling_interns2_preview.InternS2PreviewForConditionalGeneration"
+    },
+    "eos_token_id": [
+        248046,
+        248044
+    ],
+    "image_token_id": 248056,
+    "model_file": "intern_s2_fp8_qwen3_5_moe.py",
+    "model_type": "intern_s2_preview",
+    "quantization": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine",
+        "language_model.model.layers.0.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.0.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.1.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.1.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.2.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.2.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.3.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.3.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.4.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.4.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.5.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.5.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.6.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.6.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.7.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.7.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.8.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.8.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.9.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.9.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.10.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.10.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.11.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.11.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.12.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.12.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.13.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.13.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.14.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.14.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.15.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.15.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.16.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.16.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.17.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.17.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.18.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.18.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.19.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.19.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.20.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.20.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.21.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.21.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.22.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.22.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.23.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.23.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.24.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.24.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.25.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.25.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.26.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.26.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.27.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.27.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.28.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.28.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.29.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.29.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.30.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.30.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.31.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.31.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.32.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.32.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.33.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.33.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.34.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.34.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.35.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.35.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.36.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.36.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.37.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.37.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.38.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.38.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.39.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.39.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        }
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine",
+        "language_model.model.layers.0.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.0.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.1.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.1.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.2.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.2.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.3.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.3.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.4.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.4.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.5.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.5.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.6.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.6.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.7.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.7.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.8.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.8.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.9.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.9.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.10.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.10.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.11.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.11.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.12.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.12.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.13.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.13.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
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+        "language_model.model.layers.17.mlp.shared_expert_gate": {
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+        "language_model.model.layers.18.mlp.gate": {
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+        "language_model.model.layers.18.mlp.shared_expert_gate": {
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+        "language_model.model.layers.19.mlp.gate": {
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+        "language_model.model.layers.19.mlp.shared_expert_gate": {
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+        "language_model.model.layers.20.mlp.gate": {
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+        "language_model.model.layers.22.mlp.gate": {
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+        "language_model.model.layers.24.mlp.gate": {
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+        "language_model.model.layers.24.mlp.shared_expert_gate": {
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+        "language_model.model.layers.25.mlp.gate": {
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+        "language_model.model.layers.32.mlp.shared_expert_gate": {
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+        "language_model.model.layers.34.mlp.shared_expert_gate": {
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+            "bits": 8
+        },
+        "language_model.model.layers.35.mlp.gate": {
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+            "bits": 8
+        },
+        "language_model.model.layers.35.mlp.shared_expert_gate": {
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+            "bits": 8
+        },
+        "language_model.model.layers.36.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.36.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.37.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.37.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.38.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.38.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.39.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.39.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        }
+    },
+    "text_config": {
+        "model_type": "qwen3_5_moe_text",
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "attn_output_gate": true,
+        "dtype": "bfloat16",
+        "eos_token_id": 248044,
+        "full_attention_interval": 4,
+        "head_dim": 256,
+        "hidden_act": "silu",
+        "hidden_size": 2048,
+        "initializer_range": 0.02,
+        "layer_types": [
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention"
+        ],
+        "linear_conv_kernel_dim": 4,
+        "linear_key_head_dim": 128,
+        "linear_num_key_heads": 16,
+        "linear_num_value_heads": 32,
+        "linear_value_head_dim": 128,
+        "max_position_embeddings": 262144,
+        "mlp_only_layers": [],
+        "moe_intermediate_size": 512,
+        "mtp_num_hidden_layers": 1,
+        "mtp_use_dedicated_embeddings": false,
+        "num_attention_heads": 16,
+        "num_experts": 256,
+        "num_experts_per_tok": 8,
+        "num_hidden_layers": 40,
+        "num_key_value_heads": 2,
+        "rms_norm_eps": 1e-06,
+        "router_aux_loss_coef": 0.001,
+        "shared_expert_intermediate_size": 512,
+        "use_cache": true,
+        "vocab_size": 251392,
+        "mamba_ssm_dtype": "float32",
+        "rope_parameters": {
+            "mrope_interleaved": true,
+            "mrope_section": [
+                11,
+                11,
+                10
+            ],
+            "rope_theta": 10000000,
+            "partial_rotary_factor": 0.25,
+            "type": "default"
+        },
+        "pad_token_id": null,
+        "bos_token_id": null,
+        "tie_word_embeddings": false,
+        "output_router_logits": false,
+        "partial_rotary_factor": 0.25
+    },
+    "tie_word_embeddings": false,
+    "transformers_version": "5.2.0",
+    "ts_config": {
+        "model_type": "interns2_preview_time_series",
+        "auto_map": {
+            "AutoConfig": "configuration_interns2_preview.InternS2PreviewTimeSeriesConfig",
+            "AutoModel": "modeling_interns2_preview.InternS2PreviewTimeSeriesModel"
+        },
+        "activation_dropout": 0.0,
+        "activation_function": "gelu",
+        "attention_dropout": 0.0,
+        "d_model": 768,
+        "dropout": 0.0,
+        "encoder_attention_heads": 8,
+        "encoder_ffn_dim": 3072,
+        "encoder_layerdrop": 0.0,
+        "encoder_layers": 17,
+        "max_source_positions": 1500,
+        "num_mel_bins": 80,
+        "out_hidden_size": 2048,
+        "scale_embedding": false,
+        "ts_adapt_in_dim": 256,
+        "ts_adapt_out_dim": 1024,
+        "ts_hidden_dim": 1024
+    },
+    "ts_end_id": 248092,
+    "ts_start_id": 248091,
+    "ts_token_id": 248093,
+    "video_token_id": 248057,
+    "vision_end_token_id": 248054,
+    "vision_start_token_id": 248053
+}

configuration_interns2_preview.py

@@ -0,0 +1,434 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/interns2_preview/modular_interns2_preview.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_interns2_preview.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from transformers.configuration_utils import PreTrainedConfig, layer_type_validation
+from transformers.modeling_rope_utils import RopeParameters
+
+
+class InternS2PreviewVisionConfig(PreTrainedConfig):
+    model_type = "intern_s2_preview"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=27,
+        hidden_size=1152,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=4304,
+        num_heads=16,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        out_hidden_size=3584,
+        num_position_embeddings=2304,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.out_hidden_size = out_hidden_size
+        self.num_position_embeddings = num_position_embeddings
+        self.initializer_range = initializer_range
+
+
+class InternS2PreviewTextConfig(PreTrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternS2PreviewTextModel`]. It is used to instantiate a
+    Qwen3.5-MoE model according to the specified arguments, defining the model architecture.
+    Instantiating a configuration with the defaults will yield a similar configuration to that of
+    Qwen3.5-35B-A3B-Instruct [Qwen/Qwen3.5-35B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Instruct).
+
+    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PreTrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 248320):
+            Vocabulary size of the model. Defines the number of different tokens that can be represented by the
+            `inputs_ids`.
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        num_hidden_layers (`int`, *optional*, defaults to 40):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 2):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_parameters (`RopeParameters`, *optional*):
+            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
+            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
+            with longer `max_position_embeddings`.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        head_dim (`int`, *optional*, defaults to 256):
+            Projection weights dimension in multi-head attention.
+        linear_conv_kernel_dim (`int`, *optional*, defaults to 4):
+            Kernel size of the convolution used in linear attention layers.
+        linear_key_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each key head in linear attention.
+        linear_value_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each value head in linear attention.
+        linear_num_key_heads (`int`, *optional*, defaults to 16):
+            Number of key heads used in linear attention layers.
+        linear_num_value_heads (`int`, *optional*, defaults to 32):
+            Number of value heads used in linear attention layers.
+        moe_intermediate_size (`int`, *optional*, defaults to 512):
+            Intermediate size of the routed expert.
+        shared_expert_intermediate_size (`int`, *optional*, defaults to 512):
+            Intermediate size of the shared expert.
+        num_experts_per_tok (`int`, *optional*, defaults to 8):
+            Number of selected experts.
+        num_experts (`int`, *optional*, defaults to 256):
+            Number of routed experts.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabling this will also
+            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+        layer_types (`list[str]`, *optional*):
+            Types of each layer (attention or linear).
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*):
+            End of stream token id.
+
+    ```python
+    >>> from transformers import InternS2PreviewTextModel, InternS2PreviewTextConfig
+
+    >>> # Initializing a Qwen3.5-MoE style configuration
+    >>> configuration =  InternS2PreviewTextConfig()
+
+    >>> # Initializing a model from the Qwen3.5-35B-A3B style configuration
+    >>> model = InternS2PreviewTextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    # NOTE: `model_type` is kept as `qwen3_5_moe_text` because transformers hardcodes weight-renaming logic keyed
+    # on model_type (e.g. `model_dtype`); reusing the parent's value ensures correct weight loading via
+    # `AutoModelForCausalLM.from_pretrained`.
+    model_type = "qwen3_5_moe_text"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.shared_expert.gate_proj": "colwise",
+        "layers.*.mlp.shared_expert.up_proj": "colwise",
+        "layers.*.mlp.shared_expert.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=248320,
+        hidden_size=2048,
+        num_hidden_layers=40,
+        num_attention_heads=16,
+        num_key_value_heads=2,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        head_dim=256,
+        linear_conv_kernel_dim=4,
+        linear_key_head_dim=128,
+        linear_value_head_dim=128,
+        linear_num_key_heads=16,
+        linear_num_value_heads=32,
+        moe_intermediate_size=512,
+        shared_expert_intermediate_size=512,
+        num_experts_per_tok=8,
+        num_experts=256,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        layer_types=None,
+        pad_token_id: int | None = None,
+        bos_token_id: int | None = None,
+        eos_token_id: int | None = None,
+        **kwargs,
+    ):
+        kwargs["ignore_keys_at_rope_validation"] = {"mrope_section", "mrope_interleaved"}
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.tie_word_embeddings = tie_word_embeddings
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.head_dim = head_dim
+        self.rope_parameters = rope_parameters
+        kwargs.setdefault("partial_rotary_factor", 0.25)  # assign default for BC
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            interval_pattern = kwargs.get("full_attention_interval", 4)
+            self.layer_types = [
+                "linear_attention" if bool((i + 1) % interval_pattern) else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+        # linear attention part
+        self.linear_conv_kernel_dim = linear_conv_kernel_dim
+        self.linear_key_head_dim = linear_key_head_dim
+        self.linear_value_head_dim = linear_value_head_dim
+        self.linear_num_key_heads = linear_num_key_heads
+        self.linear_num_value_heads = linear_num_value_heads
+        self.moe_intermediate_size = moe_intermediate_size
+        self.shared_expert_intermediate_size = shared_expert_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        super().__init__(**kwargs)
+
+
+class InternS2PreviewTimeSeriesConfig(PreTrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternS2PreviewTimeSeriesModel`]. It is used to instantiate a
+    InternS2PreviewTimeSeries model according to the specified arguments, defining the model architecture.
+
+    Args:
+        ts_adapt_in_dim (`int`, *optional*, defaults to 256):
+            The input dimension of the time series adapter.
+        ts_adapt_out_dim (`int`, *optional*, defaults to 1024):
+            The output dimension of the time series adapter.
+        ts_hidden_dim (`int`, *optional*, defaults to 1024):
+            The hidden dimension of the time series model.
+        ts_cnn_channels (`list[int]`, *optional*, defaults to [1, 32, 64, 128, 128]):
+            The channels of the time series CNN.
+        ts_cnn_kernel_sizes (`list[int]`, *optional*, defaults to [3, 5, 5, 5]):
+            The kernel sizes of the time series CNN.
+        ts_cnn_strides (`list[int]`, *optional*, defaults to [2, 4, 4, 5]):
+            The strides of the time series CNN.
+        ts_cnn_paddings (`list[int]`, *optional*, defaults to [1, 2, 2, 2]):
+            The paddings of the time series CNN.
+        ts_concat_subsampling_in_channels (`int`, *optional*, defaults to 128):
+            The input channels of the time series concat subsampling.
+        ts_concat_subsampling_concat_size (`int`, *optional*, defaults to 2):
+            The concat size of the time series concat subsampling.
+        **super_kwargs:
+            Additional keyword arguments passed along to the base class `WhisperConfig`.
+    """
+
+    model_type = "interns2_preview_time_series"
+    base_config_key = "ts_config"
+
+    def __init__(
+        self,
+        activation_dropout: float = 0.0,
+        activation_function: str = "gelu",
+        attention_dropout: float = 0.0,
+        d_model: int = 768,
+        dropout: float = 0.0,
+        encoder_attention_heads: int = 8,
+        encoder_ffn_dim: int = 3072,
+        encoder_layerdrop: float = 0.0,
+        encoder_layers: int = 17,
+        max_source_positions: int = 1500,
+        num_mel_bins: int = 80,
+        out_hidden_size: int = 2048,
+        scale_embedding: bool = False,
+        ts_adapt_in_dim: int = 256,
+        ts_adapt_out_dim: int = 1024,
+        ts_hidden_dim: int = 1024,
+        **super_kwargs,
+    ):
+        super().__init__(**super_kwargs)
+
+        self.auto_map = {
+            "AutoConfig": "configuration_interns2_preview.InternS2PreviewTimeSeriesConfig",
+            "AutoModel": "modeling_interns2_preview.InternS2PreviewTimeSeriesModel",
+        }
+        self.activation_dropout = activation_dropout
+        self.activation_function = activation_function
+        self.attention_dropout = attention_dropout
+        self.d_model = d_model
+        self.dropout = dropout
+        self.encoder_attention_heads = encoder_attention_heads
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_layerdrop = encoder_layerdrop
+        self.encoder_layers = encoder_layers
+        self.max_source_positions = max_source_positions
+        self.num_mel_bins = num_mel_bins
+        self.out_hidden_size = out_hidden_size
+        self.scale_embedding = scale_embedding
+        self.ts_adapt_in_dim = ts_adapt_in_dim
+        self.ts_adapt_out_dim = ts_adapt_out_dim
+        self.ts_hidden_dim = ts_hidden_dim
+
+        assert self.ts_adapt_out_dim == self.ts_hidden_dim, "ts_adapt_out_dim should be equal to ts_hidden_dim"
+
+
+class InternS2PreviewConfig(PreTrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternS2PreviewModel`]. It is used to instantiate a
+    Qwen3.5-MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen3.5-35B-A3B-Instruct [Qwen/Qwen3.5-35B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Instruct).
+
+    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PreTrainedConfig`] for more information.
+
+
+    Args:
+        text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3_5TextConfig`):
+            The config object or dictionary of the text backbone.
+        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Qwen3_5VisionConfig`):
+            The config object or dictionary of the vision backbone.
+        image_token_id (`int`, *optional*, defaults to 248056):
+            The image token index to encode the image prompt.
+        video_token_id (`int`, *optional*, defaults to 248057):
+            The video token index to encode the image prompt.
+        vision_start_token_id (`int`, *optional*, defaults to 248053):
+            The start token index to encode the image prompt.
+        vision_end_token_id (`int`, *optional*, defaults to 248054):
+            The end token index to encode the image prompt.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie the word embeddings.
+
+    ```python
+    >>> from transformers import InternS2PreviewForConditionalGeneration, InternS2PreviewConfig
+
+    >>> # Initializing a Qwen3.5-MoE style configuration
+    >>> configuration = InternS2PreviewConfig()
+
+    >>> # Initializing a model from the Qwen3.5-35B-A3B style configuration
+    >>> model = InternS2PreviewForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "intern_s2_preview"
+    sub_configs = {
+        "vision_config": InternS2PreviewVisionConfig,
+        "text_config": InternS2PreviewTextConfig,
+        "ts_config": InternS2PreviewTimeSeriesConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        image_token_id=248056,
+        video_token_id=248057,
+        vision_start_token_id=248053,
+        vision_end_token_id=248054,
+        tie_word_embeddings=False,
+        ts_config=None,
+        ts_token_id=248093,
+        ts_start_id=248091,
+        ts_end_id=248092,
+        **kwargs,
+    ):
+        if isinstance(ts_config, dict):
+            self.ts_config = self.sub_configs["ts_config"](**ts_config)
+        elif ts_config is None:
+            self.ts_config = self.sub_configs["ts_config"]()
+
+        self.ts_token_id = ts_token_id
+        self.ts_start_id = ts_start_id
+        self.ts_end_id = ts_end_id
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            self.text_config = self.sub_configs["text_config"]()
+
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(**kwargs)
+        self.auto_map = {
+            "AutoConfig": "configuration_interns2_preview.InternS2PreviewConfig",
+            "AutoModelForCausalLM": "modeling_interns2_preview.InternS2PreviewForCausalLM",
+            "AutoModel": "modeling_interns2_preview.InternS2PreviewModel",
+            "AutoModelForImageTextToText": "modeling_interns2_preview.InternS2PreviewForConditionalGeneration",
+            "AutoModelForMultimodalLM": "modeling_interns2_preview.InternS2PreviewForConditionalGeneration",
+        }
+        self.architectures = ["InternS2PreviewForConditionalGeneration"]
+
+
+__all__ = ["InternS2PreviewConfig", "InternS2PreviewTextConfig"]

generation_config.json

@@ -0,0 +1,13 @@
+{
+    "bos_token_id": 248044,
+    "do_sample": true,
+    "eos_token_id": [
+        248046,
+        248044
+    ],
+    "pad_token_id": 248044,
+    "temperature": 1.0,
+    "top_k": 20,
+    "top_p": 0.95,
+    "transformers_version": "4.57.0.dev0"
+}

intern_s2_fp8_qwen3_5_moe.py

@@ -0,0 +1,71 @@
+# Auto-generated adapter for Intern-S2-Preview-FP8 -> MLX.
+import mlx.core as mx
+
+from mlx_lm.models.qwen3_5_moe import Model as Qwen35MoeModel
+from mlx_lm.models.qwen3_5_moe import ModelArgs
+
+
+def _dequant_fp8_blockwise(weight, scale_inv, block_size=128):
+    dtype = mx.bfloat16
+    weight = mx.from_fp8(weight, dtype=dtype)
+
+    if scale_inv.ndim == 0:
+        return (weight * scale_inv).astype(dtype)
+
+    if weight.ndim == 2 and scale_inv.ndim == 2:
+        m, n = weight.shape
+        pad_m = (-m) % block_size
+        pad_n = (-n) % block_size
+        padded = mx.pad(weight, ((0, pad_m), (0, pad_n)))
+        padded = padded.reshape(
+            (m + pad_m) // block_size,
+            block_size,
+            (n + pad_n) // block_size,
+            block_size,
+        )
+        out = (padded * scale_inv[:, None, :, None]).reshape(m + pad_m, n + pad_n)
+        return out[:m, :n].astype(dtype)
+
+    if weight.ndim == 3 and scale_inv.ndim == 3:
+        e, m, n = weight.shape
+        pad_m = (-m) % block_size
+        pad_n = (-n) % block_size
+        padded = mx.pad(weight, ((0, 0), (0, pad_m), (0, pad_n)))
+        padded = padded.reshape(
+            e,
+            (m + pad_m) // block_size,
+            block_size,
+            (n + pad_n) // block_size,
+            block_size,
+        )
+        out = (padded * scale_inv[:, :, None, :, None]).reshape(
+            e, m + pad_m, n + pad_n
+        )
+        return out[:, :m, :n].astype(dtype)
+
+    return (weight * scale_inv).astype(dtype)
+
+
+class Model(Qwen35MoeModel):
+    def sanitize(self, weights):
+        filtered = {}
+        for key, value in weights.items():
+            if key.startswith("mtp."):
+                continue
+            if key.startswith("model.visual") or key.startswith("vision_tower"):
+                continue
+            filtered[key] = value
+
+        dequantized = {}
+        for key, value in filtered.items():
+            if key.endswith("_scale_inv"):
+                base_key = key[: -len("_scale_inv")]
+                if base_key in filtered:
+                    dequantized[base_key] = _dequant_fp8_blockwise(
+                        filtered[base_key], value
+                    )
+                continue
+            if key not in dequantized:
+                dequantized[key] = value
+
+        return super().sanitize(dequantized)

processing_interns2_preview.py

@@ -0,0 +1,423 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/interns2_preview/modular_interns2_preview.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_interns2_preview.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import importlib
+import os
+
+import numpy as np
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.utils import auto_docstring, logging
+from transformers.video_utils import VideoInput
+
+
+logger = logging.get_logger(__name__)
+
+
+class InternS2PreviewProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_token_type_ids": False,
+            "return_mm_token_type_ids": False,
+        },
+        "videos_kwargs": {"return_metadata": True},
+        "time_series_kwargs": {},
+    }
+
+
+@auto_docstring
+class InternS2PreviewProcessor(ProcessorMixin):
+    def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
+        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
+        self.image_token_id = (
+            tokenizer.image_token_id
+            if getattr(tokenizer, "image_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.image_token)
+        )
+        self.video_token_id = (
+            tokenizer.video_token_id
+            if getattr(tokenizer, "video_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.video_token)
+        )
+        super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
+        self.vision_start_token = (
+            "<|vision_start|>" if not hasattr(tokenizer, "vision_start_token") else tokenizer.vision_start_token
+        )
+        self.vision_end_token = (
+            "<|vision_end|>" if not hasattr(tokenizer, "vision_end_token") else tokenizer.vision_end_token
+        )
+        self.vision_start_token_id = (
+            tokenizer.vision_start_token_id
+            if getattr(tokenizer, "vision_start_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.vision_start_token)
+        )
+        self.vision_end_token_id = (
+            tokenizer.vision_end_token_id
+            if getattr(tokenizer, "vision_end_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.vision_end_token)
+        )
+        self.ts_token = "<TS_CONTEXT>" if not hasattr(tokenizer, "ts_token") else tokenizer.ts_token
+        self.ts_start_token = "<|ts|>" if not hasattr(tokenizer, "ts_start_token") else tokenizer.ts_start_token
+        self.ts_end_token = "<|/ts|>" if not hasattr(tokenizer, "ts_end_token") else tokenizer.ts_end_token
+        self.ts_start_token_id = (
+            tokenizer.ts_start_token_id
+            if getattr(tokenizer, "ts_start_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.ts_start_token)
+        )
+        self.ts_end_token_id = (
+            tokenizer.ts_end_token_id
+            if getattr(tokenizer, "ts_end_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.ts_end_token)
+        )
+        self.ts_token_id = (
+            tokenizer.ts_token_id
+            if getattr(tokenizer, "ts_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.ts_token)
+        )
+
+    @auto_docstring
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        videos: VideoInput = None,
+        time_series_paths: list[str] = None,
+        time_series_sampling_rates: list[int] = None,
+        **kwargs: Unpack[InternS2PreviewProcessorKwargs],
+    ) -> BatchFeature:
+        r"""
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **ts_values** -- List of time series values to be fed to a model. Returned when `time_series_paths` is not `None`.
+            - **ts_sr** -- List of time series sampling rates to be fed to a model. Returned when `time_series_sampling_rates` is not `None`.
+            - **ts_lens** -- List of time series lengths to be fed to a model. Returned when `time_series_paths` is not `None`.
+            - **num_ts_tokens** -- List of number of time series tokens to be fed to a model. Returned when `time_series_paths` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            InternS2PreviewProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        if images is not None:
+            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+            image_grid_thw = image_inputs["image_grid_thw"]
+        else:
+            image_inputs = {}
+            image_grid_thw = None
+
+        if videos is not None:
+            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+            video_grid_thw = videos_inputs["video_grid_thw"]
+            # If user has not requested video metadata, pop it
+            if not kwargs.get("return_metadata"):
+                video_metadata = videos_inputs.pop("video_metadata")
+            else:
+                video_metadata = videos_inputs["video_metadata"]
+        else:
+            videos_inputs = {}
+            video_grid_thw = None
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+
+        if time_series_paths is not None:
+            assert time_series_sampling_rates is not None, (
+                "If time_series_signals is provided, time_series_sampling_rates must also be provided."
+            )
+            assert len(time_series_paths) == len(time_series_sampling_rates), (
+                "The number of time series signals must match the number of sampling rates."
+            )
+            time_series_inputs = self.time_series_processor(
+                ts_paths=time_series_paths, sampling_rates=time_series_sampling_rates
+            )
+            num_ts_tokens = time_series_inputs.pop("num_ts_tokens")
+            assert len(num_ts_tokens) == len(text), (
+                "The number of time series signals must match the number of text prompts."
+            )
+            for i in range(len(text)):
+                if f"{self.ts_start_token}{self.ts_token}{self.ts_end_token}" in text[i]:
+                    ts_placeholder = self.ts_start_token + self.ts_token * num_ts_tokens[i] + self.ts_end_token
+                    text[i] = text[i].replace(
+                        f"{self.ts_start_token}{self.ts_token}{self.ts_end_token}", ts_placeholder, 1
+                    )
+                elif self.ts_token in text[i]:
+                    text[i] = text[i].replace(self.ts_token, self.ts_token * num_ts_tokens[i])
+        else:
+            time_series_inputs = {}
+
+        if image_grid_thw is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    num_image_tokens = image_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if video_grid_thw is not None:
+            merge_length = self.video_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    metadata = video_metadata[index]
+                    if metadata.fps is None:
+                        logger.warning_once(
+                            "Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
+                        )
+                        metadata.fps = 24 if metadata.fps is None else metadata.fps
+
+                    # if timestamps are not provided, calculate them
+                    curr_timestamp = self._calculate_timestamps(
+                        metadata.frames_indices,
+                        metadata.fps,
+                        self.video_processor.temporal_patch_size,
+                    )
+
+                    video_placeholder = ""
+                    frame_seqlen = video_grid_thw[index][1:].prod() // merge_length
+                    for frame_idx in range(video_grid_thw[index][0]):
+                        curr_time = curr_timestamp[frame_idx]
+                        video_placeholder += f"<{curr_time:.1f} seconds>"
+                        video_placeholder += (
+                            self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
+                        )
+                    if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]:
+                        text[i] = text[i].replace(
+                            f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1
+                        )
+                    else:
+                        # vllm may input video token directly
+                        text[i] = text[i].replace(self.video_token, video_placeholder, 1)
+                    index += 1
+
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video", "ts"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+        return BatchFeature(
+            data={**text_inputs, **image_inputs, **videos_inputs, **time_series_inputs}, tensor_type=return_tensors
+        )
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = InternS2PreviewProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = InternS2PreviewProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
+    def post_process_image_text_to_text(
+        self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
+    ):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    def _calculate_timestamps(self, indices: list[int] | np.ndarray, video_fps: float, merge_size: int = 2):
+        if not isinstance(indices, list):
+            indices = indices.tolist()
+        if len(indices) % merge_size != 0:
+            indices.extend(indices[-1] for _ in range(merge_size - len(indices) % merge_size))
+        timestamps = [idx / video_fps for idx in indices]
+        # @JJJYmmm frames are merged by self.merge_size, \
+        # so we need to average the timestamps between the first/last frame within the temporal patch
+        timestamps = [
+            (timestamps[i] + timestamps[i + merge_size - 1]) / 2 for i in range(0, len(timestamps), merge_size)
+        ]
+        return timestamps
+
+    def time_series_preprocessor(self, conversation):
+        if isinstance(conversation, (list, tuple)) and (
+            isinstance(conversation[0], (list, tuple)) or hasattr(conversation[0], "content")
+        ):
+            conversations = conversation
+        else:
+            conversations = [conversation]
+
+        batch_time_series = []
+        batch_time_series_metadata = []
+        for conversation in conversations:
+            for message in conversation:
+                if message["role"] != "user":
+                    continue
+                time_series_fnames = [
+                    content["data"]
+                    for content in message["content"]
+                    if content.get("type") == "time_series" and "data" in content
+                ]
+                time_series_rates = [
+                    content.get("sampling_rate", None)
+                    for content in message["content"]
+                    if content.get("type") == "time_series"
+                ]
+                for path, rate in zip(time_series_fnames, time_series_rates):
+                    batch_time_series.append(path)
+                    batch_time_series_metadata.append(rate)
+
+        return {
+            "time_series_paths": batch_time_series or None,
+            "time_series_sampling_rates": batch_time_series_metadata or None,
+        }
+
+    def time_series_processor(
+        self,
+        ts_paths: list[str],
+        sampling_rates: list[float],
+        do_normalize=True,
+        do_truncate=True,
+    ) -> BatchFeature:
+        pd = importlib.import_module("pandas")
+        sf = importlib.import_module("soundfile")
+
+        assert len(ts_paths) == len(sampling_rates), "ts_paths and sampling_rates must have the same length"
+
+        ts_values = []
+        ts_sr = []
+        ts_lens = []
+
+        for idx, ts_path in enumerate(ts_paths):
+            sr = sampling_rates[idx]
+            ext = os.path.splitext(ts_path)[-1].lower()
+            if ext in [".wav", ".mp3", ".flac"]:
+                ts_input, sr = sf.read(ts_path)  # ts_input: np.ndarray, shape [T] or [T, C]
+            elif ext == ".csv":
+                df = pd.read_csv(ts_path, header=None)
+                ts_input = df.values  # [T, C]
+            elif ext == ".npy":
+                ts_input = np.load(ts_path)  # [T, C]
+            else:
+                raise ValueError(f"Unsupported file format: {ext}")
+
+            if not isinstance(ts_input, np.ndarray):
+                ts_input = np.array(ts_input, dtype=np.float32)
+
+            if do_normalize:
+                mean = ts_input.mean(axis=0, keepdims=True)
+                std = ts_input.std(axis=0, keepdims=True)
+                ts_input = (ts_input - mean) / (std + 1e-8)
+
+            if do_truncate and len(ts_input) > 240000:
+                ts_input = ts_input[:240000]  # truncate to 240k to avoid oom
+
+            if ts_input.ndim == 1:
+                ts_input = ts_input[:, None]  # [T,C]
+
+            ts_len = ts_input.shape[0]
+
+            if sr is None or sr == 0:  # if no sr provided
+                sr = ts_len / 4
+
+            ts_values.append(ts_input)
+            ts_sr.append(sr)
+            ts_lens.append(ts_len)
+
+        ts_lens = np.array(ts_lens)
+        ts_sr = np.array(ts_sr)
+        num_ts_tokens = self._get_num_ts_tokens(sampling_rates=ts_sr, ts_lens=ts_lens)
+        return BatchFeature(
+            data={"ts_values": ts_values, "ts_sr": ts_sr, "ts_lens": ts_lens, "num_ts_tokens": num_ts_tokens}
+        )
+
+    def _get_num_ts_tokens(self, sampling_rates, ts_lens):
+        strides = np.floor(160 / ((1 + np.exp(-sampling_rates / 100)) ** 6))
+        patch_sizes = strides * 2
+        embed_lengths = (np.ceil((ts_lens - patch_sizes) / strides) + 1).astype(np.int64)
+        num_ts_tokens = [(embed_length // 2 + 1) // 2 for embed_length in embed_lengths]
+        return num_ts_tokens
+
+
+__all__ = ["InternS2PreviewProcessor"]

tokenization_interns1.py

@@ -0,0 +1,1009 @@
+# coding=utf-8
+# Copyright 2025 The Intern team and Shanghai AI Lab team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for InternS1."""
+
+import json
+import os
+import unicodedata
+from abc import ABC, abstractmethod
+from typing import Optional, Union
+from functools import lru_cache
+
+import regex as re
+import sentencepiece as spm
+
+from transformers.tokenization_utils_base import AddedToken, TextInput
+from transformers.utils import logging
+from packaging import version
+import transformers
+if version.parse(transformers.__version__) >= version.parse("5.0.0"):
+    from transformers.tokenization_python import PreTrainedTokenizer
+else:
+    from transformers.tokenization_utils import PreTrainedTokenizer
+
+logger = logging.get_logger(__name__)
+
+try:
+    from rdkit import Chem, RDLogger
+
+    RDLogger.DisableLog("rdApp.error")
+    RDLogger.DisableLog("rdApp.*")
+    RDKIT_AVAILABLE = True
+except ImportError:
+    logger.warning_once(
+        "If tokenization with SMILES formula is of necessity, please 'pip install RDKit' for better tokenization quality."
+    )
+    RDKIT_AVAILABLE = False
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "merges_file": "merges.txt",
+    "sp_model_SMILES": "tokenizer_SMILES.model",
+    "sp_model_PROT": "tokenizer_PROT.model",
+    "sp_model_XNA": "tokenizer_XNA.model",
+}
+
+PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+
+
+class InternS1CheckModuleMixin(ABC):
+    """
+    Basic auto-detection module.
+
+    Note that short strings are ignored by this module.
+    """
+
+    def __init__(self, *, min_length: int):
+        self.min_length = min_length
+        self.REGEX = self._build_regex()
+        self.all_auto_detect_token_start = ["<SMILES_AUTO_DETECT>", "<PROT_AUTO_DETECT>", "<XNA_AUTO_DETECT>"]
+        self.all_auto_detect_token_end = ["</SMILES_AUTO_DETECT>", "</PROT_AUTO_DETECT>", "</XNA_AUTO_DETECT>"]
+        self.auto_detect_token = []
+        self.truncation = False
+
+    @abstractmethod
+    def _build_regex(self):
+        pass
+
+    @abstractmethod
+    def check_legitimacy(self, candidate: str) -> bool:
+        pass
+
+    def re_split(self, texts: Union[str, list[str]]) -> list[str]:
+        if isinstance(texts, str):
+            texts = [texts]
+
+        total_results = []
+
+        no_split_flag = 0
+
+        for text in texts:
+            if text in self.all_auto_detect_token_start:
+                total_results.append(text)
+                no_split_flag += 1
+                continue
+            elif text in self.all_auto_detect_token_end:
+                total_results.append(text)
+                no_split_flag = max(0, no_split_flag - 1)
+                continue
+
+            if no_split_flag > 0:
+                total_results.append(text)
+                continue
+
+            results = []
+            current_pos = 0
+            for match in self.REGEX.finditer(text):
+                candidate = match.group(1)
+
+                if len(candidate) >= self.min_length:
+                    match_start, match_end = match.span(1)
+
+                    if not self.check_legitimacy(candidate):
+                        continue
+
+                    if not self.truncation:
+                        if match_start > 0 and text[match_start - 1].encode("UTF-8").isalpha():
+                            continue
+                        if match_end < len(text) and text[match_end].encode("UTF-8").isalpha():
+                            continue
+
+                    if match_start > current_pos:
+                        non_candidate_part = text[current_pos:match_start]
+                        results.append(non_candidate_part)
+                else:
+                    continue
+
+                results.extend([self.auto_detect_token[0], candidate, self.auto_detect_token[1]])
+                current_pos = match_end
+
+            if current_pos < len(text):
+                remaining_part = text[current_pos:]
+                results.append(remaining_part)
+
+            total_results.extend(results)
+
+        return total_results
+
+
+class XnaCheckModule(InternS1CheckModuleMixin):
+    """
+    XNA sequence auto-detection module.
+
+    Automatically detects XNA sequence using regex patterns.
+    """
+    def __init__(self, *, min_length: int = 27):
+        super().__init__(min_length=min_length)
+        self.auto_detect_token = ["<XNA_AUTO_DETECT>", "</XNA_AUTO_DETECT>"]
+        self.truncation = True
+
+    def _build_regex(self):
+        return re.compile(r"([ATCGU]{" + str(self.min_length) + r",})")
+
+    def check_legitimacy(self, candidate: str):
+        return True
+
+
+class ProtCheckModule(InternS1CheckModuleMixin):
+    """
+    Protein sequence auto-detection module.
+
+    Automatically detects protein sequence using regex patterns.
+    """
+    def __init__(self, *, min_length: int = 27):
+        super().__init__(min_length=min_length)
+        self.auto_detect_token = ["<PROT_AUTO_DETECT>", "</PROT_AUTO_DETECT>"]
+        self.truncation = True
+        self._xna_pattern = re.compile(r"^[ATCGU]+$")
+
+    def _build_regex(self):
+        return re.compile(r"([A-Z]{" + str(self.min_length) + r",})")
+
+    def check_legitimacy(self, candidate: str):
+        if self._xna_pattern.match(candidate):
+            return False
+        return True
+
+
+# fmt: off
+bonds = ["-", "=", "#", ":", "/", "\\", ".", "$"]
+organic_symbols = ["B", "C", "N", "O", "P", "S", "F", "Cl", "Br", "I"]
+other_allows = bonds + ["[", "]", "(", ")", ";"]
+aromatic_symbols = ["b", "c", "n", "o", "s", "p"]
+elements = [
+    "H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne",
+    "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca",
+    "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn",
+    "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr",
+    "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn",
+    "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd",
+    "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb",
+    "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg",
+    "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th",
+    "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm",
+    "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt", "Ds",
+    "Rg", "Cn", "Nh", "Fl", "Mc", "Lv", "Ts", "Og"
+]
+# fmt: on
+
+
+class SmilesCheckModule(InternS1CheckModuleMixin):
+    """
+    SMILES molecular sequence auto-detection module.
+
+    Automatically detects and validates SMILES strings in text using regex patterns
+    or chemical syntax rules. Uses RDKit for precise validation when available,
+    otherwise falls back to rule-based validation.
+    """
+
+    def __init__(self, *, min_length: int = 10):
+        super().__init__(min_length=min_length)
+        self.auto_detect_token = ["<SMILES_AUTO_DETECT>", "</SMILES_AUTO_DETECT>"]
+        self._SQ_BRACKET_BAN_1 = re.compile(r"(?:[A-GI-Z]|[a-z]){3,}")
+        self._SQ_BRACKET_BAN_2 = re.compile(r"\d{4,}")
+
+    def _build_regex(self):
+        # fmt: off
+        _two_letter_elements = [
+            'Ac', 'Ag', 'Al', 'Am', 'Ar', 'As', 'At', 'Au', 'Ba', 'Be', 'Bh', 'Bi', 'Bk', 'Br', 'Ca', 'Cd',
+            'Ce', 'Cf', 'Cl', 'Cm', 'Cn', 'Co', 'Cr', 'Cs', 'Cu', 'Db', 'Ds', 'Dy', 'Er', 'Es', 'Eu', 'Fe',
+            'Fl', 'Fm', 'Fr', 'Ga', 'Gd', 'Ge', 'He', 'Hf', 'Hg', 'Ho', 'Hs', 'In', 'Ir', 'Kr', 'La', 'Li',
+            'Lr', 'Lu', 'Lv', 'Mc', 'Md', 'Mg', 'Mn', 'Mo', 'Mt', 'Na', 'Nb', 'Nd', 'Ne', 'Nh', 'Ni', 'No',
+            'Np', 'Og', 'Os', 'Pa', 'Pb', 'Pd', 'Pm', 'Po', 'Pr', 'Pt', 'Pu', 'Ra', 'Rb', 'Re', 'Rf', 'Rg',
+            'Rh', 'Rn', 'Ru', 'Sb', 'Sc', 'Se', 'Sg', 'Si', 'Sm', 'Sn', 'Sr', 'Ta', 'Tb', 'Tc', 'Te', 'Th',
+            'Ti', 'Tl', 'Tm', 'Ts', 'Xe', 'Yb', 'Zn', 'Zr'
+        ]
+        _single_letter_elements = [
+            "B", "C", "F", "H", "I", "K", "N", "O", "P", "S", "U", "V", "W", "Y", 'b', 'c', 'n', 'o', 'p', 's'
+        ]
+        # fmt: on
+        all_elements_sorted = sorted(_two_letter_elements + _single_letter_elements, key=lambda x: (-len(x), x))
+        elements_pattern_str = "|".join(all_elements_sorted)
+
+        bracket_atom_pattern_str = r"\[[^\]]+\]"
+        other_single_chars_pattern_str = r"[\(\)\.=\-#@\d\$\%\*:\+\-\/\\]"
+        smiles_unit_pattern = (
+            r"(?:"
+            + bracket_atom_pattern_str
+            + r"|"
+            + elements_pattern_str
+            + r"|"
+            + other_single_chars_pattern_str
+            + r")"
+        )
+        core_sequence_pattern = rf"(?>{smiles_unit_pattern}){{10,}}"
+        constrained_core_sequence_pattern = rf"(?![:.=]){core_sequence_pattern}(?<![:.=])"
+
+        final_regex_str = rf"({constrained_core_sequence_pattern})"
+
+        COMPILED_REGEX = re.compile(final_regex_str)
+        return COMPILED_REGEX
+
+    def check_legitimacy_slow(self, candidate: str) -> bool:
+        """Check legitimacy with RDKit"""
+        if sum(1 for char in candidate if char.encode("UTF-8").isalpha()) < 5:
+            return False
+
+        mol = Chem.MolFromSmiles(candidate)
+        if mol is None:
+            return False
+        else:
+            return True
+
+    def check_legitimacy_fast(self, candidate: str) -> bool:
+        """Check legitimacy with hard rules"""
+        if sum(1 for char in candidate if char.encode("UTF-8").isalpha()) < 5:
+            return False
+
+        if not self.check_rings_and_brackets(candidate):
+            return False
+        else:
+            return True
+
+    def check_legitimacy(self, candidate: str) -> bool:
+        if RDKIT_AVAILABLE:
+            return self.check_legitimacy_slow(candidate)
+        else:
+            return self.check_legitimacy_fast(candidate)
+
+    def check_brackets(self, text):
+        matches = re.findall(r"\[([^\[\]]*)\]", text)
+        for part in matches:
+            if "(" in part or ")" in part:
+                return False
+            if len(part) == 0:
+                return False
+            if part[0] in elements or part[0] in aromatic_symbols or part[:2] in elements:
+                return True
+        return True
+
+    def check_rings_and_brackets(self, text):
+        rings = {}
+        left_sq_bracket, right_sq_bracket = 0, 0
+        left_pt_bracket, right_pt_bracket = 0, 0
+        all_lower = True
+        digits_cnt = 0
+        pos = 0
+        while pos < len(text):
+            step = 0
+            c = text[pos]
+            if ord(c) >= 65 and ord(c) <= 90:
+                all_lower = False
+            if (pos == len(text) - 1 or pos == 0) and c in bonds:
+                return False
+            if pos > 0 and text[pos - 1] in bonds and text[pos] in bonds:
+                return False
+            if c == "[":
+                step = 1
+                left_sq_bracket += 1
+                if left_sq_bracket > right_sq_bracket + 1:
+                    return False
+                if pos == len(text) - 1:
+                    return False
+                if "]" not in text[pos + 1 :]:
+                    return False
+                bracket_span = text[pos + 1 : text.find("]")]
+
+                if self._SQ_BRACKET_BAN_1.search(bracket_span) or self._SQ_BRACKET_BAN_2.search(bracket_span):
+                    return False
+
+                matches = re.findall(r"\d+", bracket_span)
+                if len(matches) > 2:
+                    return False
+            if c == "]":
+                step = 1
+                right_sq_bracket += 1
+                if right_sq_bracket > left_sq_bracket:
+                    return False
+
+            if c == "(":
+                step = 1
+                left_pt_bracket += 1
+            if c == ")":
+                step = 1
+                right_pt_bracket += 1
+                if right_pt_bracket > left_pt_bracket:
+                    return False
+
+            if left_sq_bracket == right_sq_bracket:
+                if c.isdigit():
+                    digits_cnt += 1
+                    step = 1
+                    if (
+                        pos == 0
+                        or (pos == 1 and text[pos - 1] != "%")
+                        or (pos > 1 and text[pos - 1] != "%" and text[pos - 2] != "%")
+                    ):
+                        if c in rings:
+                            if rings[c] == "unclosed":
+                                rings[c] = "closed"
+                            else:
+                                rings[c] = "unclosed"
+                        else:
+                            rings[c] = "unclosed"
+                if c == "%":
+                    if pos >= len(text) - 2 or not text[pos + 1].isdigit() or not text[pos + 2].isdigit():
+                        return False
+                    step = 3
+                    digits_cnt += 1
+                    num = text[pos + 1 : pos + 3]
+                    if num in rings:
+                        if rings[num] == "unclosed":
+                            rings[num] = "closed"
+                        else:
+                            rings[num] = "unclosed"
+                    else:
+                        rings[num] = "unclosed"
+                if step == 0:
+                    if (
+                        pos < len(text) - 1
+                        and text[pos : pos + 2] in organic_symbols + aromatic_symbols + other_allows
+                    ):
+                        step = 2
+                    elif c in organic_symbols + aromatic_symbols + other_allows:
+                        step = 1
+                    else:
+                        return False
+
+            if step == 0:
+                step = 1
+            pos += step
+
+        if left_sq_bracket != right_sq_bracket or any(v == "unclosed" for v in rings.values()):
+            return False
+        if all_lower and digits_cnt < 2:
+            return False
+        return self.check_brackets(text)
+
+
+@lru_cache
+# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+    characters the bpe code barfs on.
+
+    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
+    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
+    tables between utf-8 bytes and unicode strings.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+# Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs
+def get_pairs(word):
+    """
+    Return set of symbol pairs in a word.
+
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+# @requires(backends=("sentencepiece",))
+class InternS1Tokenizer(PreTrainedTokenizer):
+    """
+    Construct an InternS1 tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
+    be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+    ```python
+    >>> from transformers import AutoTokenizer
+
+    >>> tokenizer = AutoTokenizer.from_pretrained("InternS1Tokenizer", trust_remote_code=True)
+    >>> tokenizer("Hello world")["input_ids"]
+    [9707, 1879]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [21927, 1879]
+    ```
+    This is expected.
+
+    Include custom extension to support better domain-specific text tokenization, leveraging a separately trained tokenizer model.
+
+    ```python
+    >>> from transformers import AutoTokenizer
+
+    >>> tokenizer = AutoTokenizer.from_pretrained("InternS1Tokenizer", trust_remote_code=True)
+    >>> tokenizer.tokenize("Describe <SMILES>C1=CC=C(C=C1)C=O</SMILES> and CC1=CC=CC=C1C=O")
+    ["Describe ", "<SMILES>", "C1=CC=C(C=C1)C=O", "</SMILES>", " and ", "<SMILES_AUTO_DETECT>",
+        "CC1=CC=CC=C1C=O", "</SMILES_AUTO_DETECT>"]
+    >>> token_ids = tokenizer("Describe <SMILES>C1=CC=C(C=C1)C=O</SMILES> and CC1=CC=CC=C1C=O")["input_ids"]
+    >>> token_ids
+    [74785, 220, 151925, 151854, 151860, 151698, 151707, 151860, 151690, 151726, 151926, 323, 220, 151672, 151860, 151701, 151860, 151854, 151726]
+
+    >>> tokenizer.convert_ids_to_tokens(token_ids)
+    ['Describe', 'Ġ', '<SMILES>', 'C', '1', '=CC=C(', 'C=C', '1', ')C', '=O', '</SMILES>', 'Ġand', 'Ġ', 'CC', '1', '=CC=CC=C', '1', 'C', '=O']
+    ```
+
+    Users should refer to this superclass [`PreTrainedTokenizer`] for more information regarding those overloaded methods
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        merges_file (`str`):
+            Path to the merges file.
+        errors (`str`, *optional*, defaults to `"replace"`):
+            Paradigm to follow when decoding bytes to UTF-8. See
+            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str`, *optional*):
+            The beginning of sequence token. Not applicable for this tokenizer.
+        eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The end of sequence token.
+        pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not the model should cleanup the spaces that were added when splitting the input text during the
+            tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
+        split_special_tokens (`bool`, *optional*, defaults to `False`):
+            Whether or not the special tokens should be split during the tokenization process. The default behavior is
+            to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
+            ['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<|endoftext|>")` will be give `['<',
+            '|', 'endo', 'ft', 'ext', '|', '>']`. This argument is only supported for `slow` tokenizers for the moment.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        merges_file,
+        errors="replace",
+        unk_token="<|endoftext|>",
+        bos_token=None,
+        eos_token="<|endoftext|>",
+        pad_token="<|endoftext|>",
+        clean_up_tokenization_spaces=False,
+        split_special_tokens=False,
+        special_tokens_pattern="none",
+        **kwargs,
+    ):
+        bos_token = (
+            AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
+            if isinstance(bos_token, str)
+            else bos_token
+        )
+        eos_token = (
+            AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
+            if isinstance(eos_token, str)
+            else eos_token
+        )
+        unk_token = (
+            AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
+            if isinstance(unk_token, str)
+            else unk_token
+        )
+        pad_token = (
+            AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
+            if isinstance(pad_token, str)
+            else pad_token
+        )
+
+        with open(vocab_file, encoding="utf-8") as vocab_handle:
+            self.encoder = json.load(vocab_handle)
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.errors = errors  # how to handle errors in decoding
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        bpe_merges = []
+        with open(merges_file, encoding="utf-8") as merges_handle:
+            for i, line in enumerate(merges_handle):
+                line = line.strip()
+                if (i == 0 and line.startswith("#version:")) or not line:
+                    continue
+                bpe_merges.append(tuple(line.split()))
+        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+        # NOTE: the cache can grow without bound and will get really large for long running processes
+        # (esp. for texts of language that do not use space between word, e.g. Chinese); technically
+        # not a memory leak but appears as one.
+        # GPT2Tokenizer has the same problem, so let's be consistent.
+        self.cache = {}
+
+        self.pat = re.compile(PRETOKENIZE_REGEX)
+
+        if kwargs.get("add_prefix_space", False):
+            logger.warning_once(
+                f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect."
+            )
+
+        super().__init__(
+            vocab_file=vocab_file,
+            merges_file=merges_file,
+            errors=errors,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            split_special_tokens=split_special_tokens,
+            special_tokens_pattern=special_tokens_pattern,
+            **kwargs,
+        )
+
+        self.prepare_extra_tokenizers(vocab_file)
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self.encoder)
+
+    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.get_vocab
+    def get_vocab(self):
+        return dict(self.encoder, **self.added_tokens_encoder)
+
+    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                except ValueError:
+                    new_word.extend(word[i:])
+                    break
+                else:
+                    new_word.extend(word[i:j])
+                    i = j
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def prepare_extra_tokenizers(self, vocab_file: str) -> None:
+        """
+        Prepare domain-specific tokenizers.
+        
+        Define variables/maps here which guide domain-specific tokenization later.
+        """
+        # Load extra tokenizers with SentencePiece model
+        dir_name = os.path.dirname(vocab_file)
+
+        self.sp_model_SMILES = spm.SentencePieceProcessor()
+        self.sp_model_SMILES.Load(os.path.join(dir_name, "tokenizer_SMILES.model"))
+        self.sp_model_SMILES.offset = self.init_kwargs["offset_SMILES"]
+
+        self.sp_model_PROT = spm.SentencePieceProcessor()
+        self.sp_model_PROT.Load(os.path.join(dir_name, "tokenizer_PROT.model"))
+        self.sp_model_PROT.offset = self.init_kwargs["offset_PROT"]
+
+        self.sp_model_XNA = spm.SentencePieceProcessor()
+        self.sp_model_XNA.Load(os.path.join(dir_name, "tokenizer_XNA.model"))
+        self.sp_model_XNA.offset = self.init_kwargs["offset_XNA"]
+
+        base_mapping = {
+            "SMILES": self.sp_model_SMILES,
+            "protein": self.sp_model_PROT,
+            "dna": self.sp_model_XNA,
+            "rna": self.sp_model_XNA,
+        }
+        auto_detect_mapping = {
+            "SMILES": self.sp_model_SMILES,
+            "PROT": self.sp_model_PROT,
+            "XNA": self.sp_model_XNA,
+        }
+        # Guiding tokens of domain-specific tokenization
+        self.ex_begin_mapping = {f"<{key}>": value for key, value in base_mapping.items()}
+        self.ex_end_mapping = {f"</{key}>": value for key, value in base_mapping.items()}
+        # Transient markers for auto-detection, these tokens will not be assigned token ids
+        self.ex_auto_begin_mapping = {f"<{key}_AUTO_DETECT>": value for key, value in auto_detect_mapping.items()}
+        self.ex_auto_end_mapping = {f"</{key}_AUTO_DETECT>": value for key, value in auto_detect_mapping.items()}
+        # Token markers to prevent unwanted auto-detection
+        self.ex_protect_begin_tokens = ["<MOLFORMULA>"]
+        self.ex_protect_end_tokens = ["</MOLFORMULA>"]
+        # For simplicity
+        self.ex_protect_tokens = self.ex_protect_begin_tokens + self.ex_protect_end_tokens
+        self.ex_all_begin_mapping = self.ex_begin_mapping | self.ex_auto_begin_mapping
+        self.ex_all_end_mapping = self.ex_end_mapping | self.ex_auto_end_mapping
+
+        # Update encoder & decoder with extra tokenizers
+        for tokenizer_name, sp_model in [
+            ("SMILES", self.sp_model_SMILES),
+            ("PROT", self.sp_model_PROT),
+            ("XNA", self.sp_model_XNA),
+        ]:
+            self.decoder.update(
+                {i + sp_model.offset: sp_model.id_to_piece(i) for i in range(sp_model.get_piece_size())}
+            )
+            # Not really used, only to fill holes in encoder, to keep methods like `add_tokens` working
+            self.encoder.update(
+                {
+                    f"<|{tokenizer_name}_{sp_model.id_to_piece(i)}|>": i + sp_model.offset
+                    for i in range(sp_model.get_piece_size())
+                }
+            )
+
+        # protect-tokens should keep complete temporarily to guide later tokenization
+        # it will be segmented later
+        for token in self.ex_protect_tokens:
+            self.tokens_trie.add(token)
+
+        self._unk_token = "<unk>"  # Fall-back
+        self.check_module_list = [SmilesCheckModule(), ProtCheckModule(), XnaCheckModule()]
+
+    def _pop_logical_sp_token(self, extra_tokenizer_stack: list, mapping_name: str) -> None:
+        """Switch tokenizer when it comes to an end sp token"""
+        extra_tokenizer = extra_tokenizer_stack.pop()
+        if extra_tokenizer != self.ex_all_end_mapping[mapping_name]:
+            logger.warning_once(
+                f"Encounter incorrect nesting of extra tokenizer: {self.ex_all_end_mapping[mapping_name]} and {extra_tokenizer}"
+            )
+            logger.warning_once("This may lead to unexpected behaviour of the tokenizer, please check your input.")
+
+    def tokenize(self, text: TextInput, **kwargs) -> list[str]:
+        """
+        Converts a string into a sequence of tokens, using the tokenizer.
+
+        It will switch to domain-specific tokenizer once encountering extra/logical sp tokens.
+
+        Args:
+            text: TextInput
+        """
+        split_special_tokens = kwargs.pop("split_special_tokens", self.split_special_tokens)
+
+        text, kwargs = self.prepare_for_tokenization(text, **kwargs)
+
+        if hasattr(self, "do_lower_case") and self.do_lower_case:
+            # convert non-special tokens to lowercase. Might be super slow as well?
+            escaped_special_toks = [re.escape(s_tok) for s_tok in (self.all_special_tokens)]
+            escaped_special_toks += [
+                re.escape(s_tok.content)
+                for s_tok in (self._added_tokens_decoder.values())
+                if not s_tok.special and s_tok.normalized
+            ]
+            pattern = r"(" + r"|".join(escaped_special_toks) + r")|" + r"(.+?)"
+            text = re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), text)
+
+        if split_special_tokens:
+            no_split_token = []
+            tokens = [text]
+        else:
+            no_split_token = self._added_tokens_encoder.keys()  # don't split on any of the added tokens
+            # "This is something<special_token_1>  else"
+            tokens = self.tokens_trie.split(text)
+
+        # ["This is something", "<special_token_1>", "  else"]
+        for i, token in enumerate(tokens):
+            if token in no_split_token:
+                tok_extended = self._added_tokens_decoder.get(self._added_tokens_encoder[token], None)
+                left = tokens[i - 1] if i > 0 else None
+                right = tokens[i + 1] if i < len(tokens) - 1 else None
+                if isinstance(tok_extended, AddedToken):
+                    if tok_extended.rstrip and right:
+                        # A bit counter-intuitive but we strip the left of the string
+                        # since tok_extended.rstrip means the special token is eating all white spaces on its right
+                        tokens[i + 1] = right.lstrip()
+                    # Strip white spaces on the left
+                    if tok_extended.lstrip and left:
+                        tokens[i - 1] = left.rstrip()  # Opposite here
+                    if tok_extended.single_word and left and left[-1] != " ":
+                        tokens[i - 1] += token
+                        tokens[i] = ""
+                    elif tok_extended.single_word and right and right[0] != " ":
+                        tokens[i + 1] = token + tokens[i + 1]
+                        tokens[i] = ""
+                else:
+                    raise ValueError(
+                        f"{tok_extended} cannot be tokenized because it was not properly added"
+                        f" to the tokenizer. This means that it is not an `AddedToken` but a {type(tok_extended)}"
+                    )
+
+        # ["This is something", "<special_token_1>", "else"]
+        tokenized_text = []
+
+        # Codes for automatically detecting domain-specific content
+        # All parts that have been marked by domain-specific or protection tokens will not be subject to auto detection
+        # See transformers/tests/models/intern_s1/test_tokenization_intern_s1.py::test_auto_detection() for more details
+        new_tokens = []
+        not_split_flag = 0
+        for token in tokens:
+            if not token:
+                continue
+            if token in no_split_token or token in self.ex_protect_tokens:
+                new_tokens.append(token)
+                if token in self.ex_begin_mapping or token in self.ex_protect_begin_tokens:
+                    not_split_flag += 1  # In case nested sp tokens
+                elif token in self.ex_end_mapping or token in self.ex_protect_end_tokens:
+                    not_split_flag = max(0, not_split_flag - 1)
+            else:
+                if not_split_flag:
+                    new_tokens.append(token)
+                else:
+                    for check_module in self.check_module_list:
+                        token = check_module.re_split(token)
+
+                    new_tokens.extend(token)
+        tokens = new_tokens
+
+        # Use stack to maintain which tokenizer should be used, considering the possibility of nested extra tokenizer
+        extra_tokenizer_stack = []
+        for token in tokens:
+            # Need to skip eventual empty (fully stripped) tokens
+            if not token:
+                continue
+            # protect-tokens are not assigned token ids, should be segmented here
+            if token in self.ex_protect_tokens:
+                tokenized_text.extend(self._tokenize(token))
+            # push tokenizer to stack when encountering begin token
+            elif token in self.ex_all_begin_mapping:
+                tokenized_text.append(token)
+                extra_tokenizer_stack.append(self.ex_all_begin_mapping[token])
+            # pop tokenizer from stack when encountering end token
+            elif token in self.ex_all_end_mapping:
+                tokenized_text.append(token)
+                if extra_tokenizer_stack:
+                    self._pop_logical_sp_token(extra_tokenizer_stack, token)
+            # other special tokens
+            elif token in no_split_token:
+                tokenized_text.append(token)
+            else:
+                tokenized_text.extend(self._tokenize(token, extra_tokenizer_stack=extra_tokenizer_stack))
+
+        # ["This", " is", " something", "<special_token_1>", "else"]
+        return tokenized_text
+
+    def _tokenize(self, text, **kwargs):
+        """
+        Modified from `transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize`.
+
+        This adaptation supports domain-specific tokenizers.
+        """
+        extra_tokenizer_stack = kwargs.pop("extra_tokenizer_stack", False)
+        if extra_tokenizer_stack:
+            tokenized_text = extra_tokenizer_stack[-1].encode(text, out_type=str)
+            tokenized_id = extra_tokenizer_stack[-1].encode(text, out_type=int)
+            final_tokenized_text = []
+            for text_piece, id_piece in zip(tokenized_text, tokenized_id):
+                if id_piece == 0:
+                    final_tokenized_text.extend(self._bpe_tokenize(text_piece))
+                else:
+                    final_tokenized_text.append(text_piece)
+            return final_tokenized_text
+        else:
+            return self._bpe_tokenize(text)
+
+    def _bpe_tokenize(self, text, **kwargs):
+        text = text.replace(
+            "▁", " "
+        )  # This discrepancy stems from differing whitespace treatment in SentencePiece versus BPE tokenization.
+        bpe_tokens = []
+        for token in re.findall(self.pat, text):
+            token = "".join(
+                self.byte_encoder[b] for b in token.encode("utf-8")
+            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+        return bpe_tokens
+
+    def convert_tokens_to_ids(self, tokens: Union[str, list[str]]) -> Union[int, list[int]]:
+        """
+        Modified from `transformers.tokenization_utils.PreTrainedTokenzier.convert_tokens_to_ids`.
+
+        Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
+        vocabulary.
+
+        This adaptation supports domain-specific tokenizers.
+
+        Args:
+            tokens (`str` or `List[str]`): One or several token(s) to convert to token id(s).
+
+        Returns:
+            `int` or `List[int]`: The token id or list of token ids.
+        """
+        if tokens is None:
+            return None
+
+        if isinstance(tokens, str):
+            return self._convert_token_to_id_with_added_voc(tokens)
+
+        ids = []
+        extra_tokenizer_stack = []
+
+        for token in tokens:
+            if token not in self.ex_auto_begin_mapping and token not in self.ex_auto_end_mapping:
+                ids.append(
+                    self._convert_token_to_id_with_added_voc(token, extra_tokenizer_stack=extra_tokenizer_stack)
+                )
+            if token in self.ex_all_begin_mapping:
+                extra_tokenizer_stack.append(self.ex_all_begin_mapping[token])
+            elif token in self.ex_all_end_mapping:
+                if extra_tokenizer_stack:
+                    self._pop_logical_sp_token(extra_tokenizer_stack, token)
+        return ids
+
+    def _convert_token_to_id_with_added_voc(self, token, **kwargs):
+        """
+        Modified from `transformers.tokenization_utils.PreTrainedTokenzier._convert_token_to_id_with_added_voc`.
+
+        This adaptation supports domain-specific tokenizers.
+        """
+        if token is None:
+            return None
+
+        if token in self._added_tokens_encoder:
+            return self._added_tokens_encoder[token]
+        return self._convert_token_to_id(token, **kwargs)
+
+    def _convert_token_to_id(self, token, **kwargs):
+        """
+        Modified from `transformers.tokenization_utils.PreTrainedTokenzier._convert_token_to_id`.
+
+        Converts a token (str) in an id using the vocab.
+
+        Fall back to original tokenizer once OOV.
+        """
+        extra_tokenizer_stack = kwargs.pop("extra_tokenizer_stack", False)
+        if extra_tokenizer_stack:
+            token_id = extra_tokenizer_stack[-1].piece_to_id(token)
+            if token_id == extra_tokenizer_stack[-1].unk_id():
+                return self.encoder.get(token, self.encoder.get(self._unk_token))
+            else:
+                return token_id + extra_tokenizer_stack[-1].offset
+        else:
+            return self.encoder.get(token, self.encoder.get(self._unk_token))
+
+    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        text = "".join(tokens)
+        text = text.replace(
+            "▁", "Ġ"
+        )  # This discrepancy stems from differing whitespace treatment in SentencePiece versus BPE tokenization.
+        text = text.replace("\n", "Ċ")
+        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+        return text
+
+    def decode(
+        self,
+        token_ids,
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = False,
+        spaces_between_special_tokens: bool = False,
+        **kwargs,
+    ) -> str:
+        # `spaces_between_special_tokens` defaults to True for _decode in slow tokenizers
+        # and cannot be configured elsewhere, but it should default to False for InternS1Tokenizer
+        return super().decode(
+            token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            **kwargs,
+        )
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+        """
+        Modified from `transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.save_vocabulary` to support saving custom extension.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+        merge_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+        )
+        sp_model_smiles = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["sp_model_SMILES"]
+        )
+        sp_model_prot = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["sp_model_PROT"]
+        )
+        sp_model_xna = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["sp_model_XNA"]
+        )
+
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+        index = 0
+        with open(merge_file, "w", encoding="utf-8") as writer:
+            writer.write("#version: 0.2\n")
+            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+                        " Please check that the tokenizer is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(" ".join(bpe_tokens) + "\n")
+                index += 1
+
+        with open(sp_model_smiles, "wb") as f:
+            f.write(self.sp_model_SMILES.serialized_model_proto())
+
+        with open(sp_model_prot, "wb") as f:
+            f.write(self.sp_model_PROT.serialized_model_proto())
+
+        with open(sp_model_xna, "wb") as f:
+            f.write(self.sp_model_XNA.serialized_model_proto())
+
+        return vocab_file, merge_file
+
+    def prepare_for_tokenization(self, text, **kwargs):
+        text = unicodedata.normalize("NFC", text)
+        return (text, kwargs)
+
+
+__all__ = ["InternS1Tokenizer"]

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tokenizer_XNA.model

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+size 15451

tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "248044": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "248045": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "248046": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "248047": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "248048": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "248049": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "248050": {
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+      "single_word": false,
+      "special": true
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+      "special": true
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+      "special": true
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+      "special": true
+    },
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
+    "248056": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+    },
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+  },
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+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_interns1.InternS1Tokenizer",
+      null
+    ]
+  },
+  "backend": "custom",
+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+  "local_files_only": false,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
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+    "audio_eos_token": "<|audio_end|>",
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+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
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+  "offset_XNA": 250150,
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "InternS1Tokenizer",
+  "tool_parser_type": "qwen3_coder",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}