code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG... | 296 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin"... | 68 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json... | 613 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggin... | 613 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeli... | 432 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def UpperCamelCase ( a , a , a ) -> float:
'''simple docstring'''
__magic_name__ = x
__magic_name__ = y
for step in range(a ): # noqa: B007
__ma... | 432 | 1 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
... | 715 | '''simple docstring'''
def __lowerCAmelCase ( a_ = 1000 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = 2**power
SCREAMING_SNAKE_CASE : Any = str(a_ )
SCREAMING_SNAKE_CASE : int ... | 179 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_UpperCAmelCase : Optional[Any] = 'Usage of script: script_name <size_of_canvas:int>'
_UpperCAmelCase : List[str] = [0] * 1_00 + [1] * 10
random.shuffle(choic... | 72 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cl... | 575 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : str = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIV... | 720 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase_ ( lowercase__ , lowercase__ , lowercase__):
lowerCamelCase__ = list(range(len(lowercase__)))
lowerCamelCase__ = [v / w for v, w in zip(lowercase__ , lowercase__)]
index.sort(key=la... | 187 | 0 |
class __SCREAMING_SNAKE_CASE :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = graph
self._... | 619 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_... | 532 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
_... | 721 |
from __future__ import annotations
import math
def lowercase_ ( __snake_case : int , __snake_case : int , __snake_case : bool , __snake_case : list[int] , __snake_case : float ) -> int:
'''simple docstring'''
... | 57 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
... | 20 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
B... | 401 | 0 |
A : Tuple = 8.314_462 # Unit - J mol-1 K-1
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles *... | 718 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = psutil.Process()
__lowerCAmelCase = False
def ... | 282 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __lowerCAmelCase ( __UpperCamelCase : str = "" ):
'''simple docstring'''
snake_case_ : Optional[int] = ur... | 58 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 3 | 0 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelFo... | 701 |
"""simple docstring"""
from typing import Any
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if not input_list:
return []
_snake_case : List[Any] = [input_list.count(snake_case__ ) for value in input_list]
_snake_case ... | 28 | 0 |
"""simple docstring"""
from __future__ import annotations
class lowercase__ :
"""simple docstring"""
def __init__( self , _A=None ):
'''simple docstring'''
UpperCamelCase : Optional[Any] ... | 102 | import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _lowerCAmelCase :
def __init__( self : Any , __snake_case : int ):
lowerCamelCase :Union[str, Any] = data
lowerCamelCase :Optional[int] ... | 166 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class UpperCAmelCase_ ( __lowercase ):
# `task` is not a ... | 513 |
'''simple docstring'''
from __future__ import annotations
def a_ ( lowerCamelCase : list , lowerCamelCase : int ):
# Checks if the entire collection has been sorted
if len(lowerCamelCase ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase ... | 513 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
a__ = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def A__ (snake_case : str = "mumbai" ) -> Generator[tuple[str, str], No... | 279 |
def A__ (snake_case : int ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 | 1 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCamelCase__ ( __a ):
'''simple docstring'''
... | 706 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
A_ = {
# 1536-bit
5: {
"prime": int(
... | 123 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available()... | 410 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
... | 38 | 0 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : float = 1 / sqrt(2 ) ):
'''simple docstring''... | 712 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser('''Stable Diffusion script with inte... | 21 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class _snake_case :
def __init__( self : List[Any] , UpperCAmelCase : list[str] ):
__lowerCamelCase : list[dict] = []
self.adlist.append(
... | 646 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_b... | 75 | 0 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
class UpperCamelCase_ (__A ):
__magic_name__ = ... | 714 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCamelCase_ = pytest.mark.integration
@pytest.mark.parametrize("path"... | 463 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@sl... | 302 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 302 | 1 |
'''simple docstring'''
import numpy as np
__UpperCamelCase : Optional[Any] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["... | 720 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_... | 417 | 0 |
# flake8: noqa
# Lint as: python3
_lowerCAmelCase = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_pro... | 10 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig... | 560 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
if len(_lowerCAmelCase ) <= 1 or n <= 1:
return
insert_next(_lowerCAmelCase ,n - 1 )
rec_insertion_sort(_lowerCAmelCase ,n - 1 )
def _lowerCAm... | 481 |
import datasets
from .evaluate import evaluate
_lowerCAmelCase = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
... | 481 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
lowerCamelCase__ : Tuple = logging.get_logger(__name__)... | 12 | '''simple docstring'''
from numpy import exp, pi, sqrt
def __UpperCAmelCase ( a_: int, a_: float = 0.0, a_: float = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 494 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoX... | 149 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : Optional[int] = TypeVar('''T''')
class __lowerCamelCase ( Generic[T] ):
"""simple docstring"""
a_: deque[T] # Cache store of ... | 149 | 1 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_A = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors... | 158 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A_ ( __SCREAMING_SNAKE_CASE : ndarray ) -> float:
return np.dot(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
class SCREAM... | 158 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowerCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ (__lowercase ):
... | 716 |
import argparse
import os
import re
lowerCamelCase__ = '''src/transformers'''
# Pattern that looks at the indentation in a line.
lowerCamelCase__ = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__ = re.compile(R'''^\s*"(... | 226 | 0 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
l... | 525 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCamelCase = 1
_lowerCamelCase = 1
while repunit:
_lowerCamelCase = ... | 650 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.6_0_9_3_4_4,
"knot": 1.8_5_2,
}
SCREAMING_SNAKE_CASE_ = {
"km/h": 1.0,
"m/s": 0.2_7_7_7_7_7_7_7_8,
"mph": 0.6_2_1_3_7_1_1_9_2,
"knot": 0.5_3_9_9_5_6_8_0_3,
}
def lower... | 573 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import B... | 573 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str ):
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 449 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipel... | 449 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import Pretraine... | 713 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__lowerCAmelCase , __lowerCAmelCa... | 40 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import Auto... | 151 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowerCamelCase_ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbel... | 151 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vi... | 705 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def __snake_case ( lowercase : Sequence[float] , lowercase : int , lowercase : int )... | 420 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : int = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if... | 672 |
'''simple docstring'''
import baseaa
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.... | 672 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : Any = HfArgumentParser(lowercase__ )
__SCREAMING_SNAKE_CASE : int = parser.parse_args_into_dataclasses()[0]
__SCREAMING_SNAKE_CASE ... | 260 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 260 | 1 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datas... | 257 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( _a ) -> List[str]:
'''simple docstri... | 257 | 1 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowerCAmelCase : Union[str, Any] = object()
# For specifying empty leaf dic... | 630 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCAmelCase : int = {
"""configuration_trocr""... | 630 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeli... | 483 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowercase_ :
"""simple docstring"""
... | 483 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra... | 717 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_... | 311 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 311 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import ... | 707 |
from __future__ import annotations
import time
import numpy as np
A : Dict = [8, 5, 9, 7]
A : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1... | 5 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 75 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
... | 597 | 0 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def __UpperCamelCase ( a : dict , a : str , a : set , a : set , a : dict , a : dict , a : PriorityQueue , a : dict ,... | 711 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase : Optional[Any] ='''\
'''
lowerCAmelCase : Dict ='''
Perp... | 172 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCAmelCase : Optional[Any] =TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=N... | 172 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = len(snake_case_ )
while cur > 1:
# Find the maximum number in arr
__SCREAMING_SNAKE_CASE = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
__SCREAMING_SN... | 719 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from... | 248 | 0 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( UpperCamelCase : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first... | 273 |
"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if no... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
__UpperCAmelCase : int = [0 for i in range(r + 1 )]
# nc0 = 1
__UpperCAmelCase : List[Any] = 1
for i in range(1 , n + 1 ):
... | 705 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSa... | 675 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.ut... | 601 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a__ : List[Any] = ... | 601 | 1 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowerCamelCase = transforms.Co... | 59 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 59 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Union[str, Any] = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve... | 278 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : List[Any] = '\nimport os\n'
snake_case__ : List[str] = '\ndef foo():\n import os\n return False\n'
snake_case__ : List[Any] = '\ndef foo():\n def bar()... | 278 | 1 |
"""simple docstring"""
import sys
def __a ( A ):
'''simple docstring'''
lowercase__ = len(A )
lowercase__ = [[0 for x in range(A )] for x in range(A )]
lowercase__ = [[0 for x in range(A )] for x in range(A )]
... | 668 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_: List[Any] = logging.get_logger(__name__)
lowerCAmelCase_: int = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json"... | 668 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = (CMStochasticIterativeScheduler,)
__magic_name__ = 10
... | 27 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase( un... | 27 | 1 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Dict = data
lowerCAmelCase : Any = None
... | 705 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 681 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
SCREAMING_SNAKE_CASE_ = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
SCREAMING_SNAKE_CASE_ = typing.Union[np.floata... | 597 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE_ = "src/transformers"
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE_ = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-lin... | 597 | 1 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 'Tobias Carryer'
from time import time
class a__ :
def __init__( self :int , _lowerCamelCase :str , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :str=int(time() ... | 717 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common imp... | 395 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger... | 31 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : Optional[int]=2 , _lowerCAme... | 31 | 1 |
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `StableDiffusionControlNetPip... | 713 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ : Optional[int] = False
class snake_c... | 124 | 0 |
"""simple docstring"""
__A = 9.8_0665
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = g ) ->float:
"""simple docstring"""
if fluid_density <= 0:
raise ValueError('Impossible fluid density' )
if volume < 0:
raise ValueError... | 93 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 ):
'''simple docstring'''
lowerCamelCase : Dict = None
if token is n... | 340 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCH... | 137 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hpara... | 137 | 1 |
from __future__ import annotations
import math
def lowercase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : bool , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : float ... | 381 |
import math
def lowercase_ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
return math.pow(SCREAMING_SNAKE_CASE , 2 ) - a
def lowercase_ ( SCREAMING_SNAKE_CASE : float ):
... | 381 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 531 |
def __lowerCAmelCase (SCREAMING_SNAKE_CASE = 6008_5147_5143 )-> int:
"""simple docstring"""
try:
snake_case_ = int(SCREAMING_SNAKE_CASE )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
... | 531 | 1 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRCon... | 506 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase_ (UpperCamelCase__ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even number... | 506 | 1 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user... | 718 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 59 | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_snake_case : Optional[int] = (3, 9, -11, 0, 7, 5, 1, -1)
_snake_case : Optional[Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __SCRE... | 693 |
import math
def _lowercase ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 386 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _UpperCAmelCase (UpperCamelCase_ : str , UpperCamelCase_ : float | Decimal , UpperCamelCase_ : float = 10**-10 ):
'''simple docstring'''
_lowerCAmel... | 196 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Data... | 196 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( __A ,__A ,__A ,__A ) -> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
lowercase , lowercase ... | 607 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __snake_case ( __A ) -> Any:
lowercase : List[str] = os.path.join(args.tf_model_dir ,"""para... | 607 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_nu... | 705 |
import requests
from bsa import BeautifulSoup
def _a ( __SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE ).text , 'html.parser' )
_lowerC... | 585 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowercase_ ( _lowerCamelCase: str ) -> Dict:
''... | 646 | """simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTe... | 646 | 1 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Union[str, Any] = num - 1
snake_case_ : List[str] = 0
while s % 2 == 0:
snake_case_ : ... | 48 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thread... | 48 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ... | 19 |
"""simple docstring"""
import heapq
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
_UpperCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq... | 19 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCamelCase : Any = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''... | 707 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
snake_case_ : Any = {
... | 656 | 0 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 669 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
... | 131 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStr... | 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase :... | 630 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ :Any = logging.get_logger(__name__)
a_ :List[Any] = {
'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',... | 35 |
def UpperCamelCase ( __lowerCamelCase : int = 1 , __lowerCamelCase : int = 1000 ):
snake_case : int = 1
snake_case : int = 0
for divide_by_number in range(__lowerCamelCase , digit + 1 ):
snake_case ... | 204 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/... | 149 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 149 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieO... | 237 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl... | 237 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_... | 713 |
from ...processing_utils import ProcessorMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = ['image_processor', 'feature_extractor']
__UpperCAmelCase = 'TvltImageProcessor'
__UpperCAmelCase = 'TvltFeatureExtractor'
def __i... | 252 | 0 |
"""simple docstring"""
import math
import os
import sys
def __UpperCamelCase ( snake_case__ ):
A_ : Optional[Any] = """"""
try:
with open(snake_case__ , """rb""" ) as binary_file:
A_ : Union[str, Any] = binary_file.read()
for dat in data:
A_ ... | 180 |
"""simple docstring"""
_lowerCAmelCase = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD,... | 180 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Dif... | 498 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 498 | 1 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Optional[Any] ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CON... | 664 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def snake_case ( A__ = 2_00_00_00 ):
UpperCAmelCase_ : list[int] = [0]
UpperCAmelCase_ : int
for idx in range(1 ,ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.a... | 463 |
"""simple docstring"""
from itertools import count
def snake_case ( A__ = 50 ):
UpperCAmelCase_ : Any = [1] * min_block_length
for n in count(A__ ):
fill_count_functions.append(1 )
for block_length in range(A__ ,n + 1 ):
for block_start in range(n - ... | 463 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fla... | 110 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCamelCase ( lowerCamelCase_ ):
"""simple docstring"""
a_: List[Any] = """"""
a_: str = (
Non... | 710 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( lowercase__ : Any , lowercase__ : List[str] , lowercase__ : List[... | 149 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,... | 182 |
"""simple docstring"""
import requests
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> None:
UpperCAmelCase__ : List[str] = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : List[str] = requests.post(lowerCAmelCase , json={"""text"""... | 182 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ = 0
snake_case_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
snake_case_ = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 537 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_imag... | 260 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import ... | 260 | 1 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.b... | 716 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokeniz... | 253 | 0 |
'''simple docstring'''
import requests
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> None:
"""simple docstring"""
snake_case_ : Union[str, Any] = {"Content-Type": "application/json"}
snake_case_ : str = r... | 653 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tenso... | 653 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class _a ( __SCREAMING_... | 705 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .loggin... | 429 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __magic_name__ :
UpperCamelCase__ = 42
UpperCamelCase__ ... | 72 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not ... | 328 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCAmelCase__ ( A__ = 8 ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__ = ascii_letters + digits + punct... | 700 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state ... | 274 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.m... | 32 |
def lowerCAmelCase__ ( UpperCamelCase_ : int )-> int:
A__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCAmelCase__ ( UpperCamelCase_ : int )-> int:
A__ = 0
while number > 0:
... | 632 | 0 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def a__ ( a : Optional[Any] = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def a__ ( a : Optional[Any] ... | 718 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ ):
_snake_case , _snake_case : Dict = text, pattern
_snake_case , _snake_case : int = len(sna... | 87 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impor... | 175 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a__ ( _UpperCamelCase : List[Any] ):
if "cls_token" in name:
__lowerCamelCase = name.replace('''cls_token''' ,'''vit... | 175 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class snake_case :
def __init__( self ,UpperCAmelCase_ ) -> List[str]:
lowercase__ = data
lowercase__ ... | 539 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,... | 539 | 1 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger(__name__)
def _UpperCAmelCase ( UpperCamelCase: Optional[int] ... | 611 |
from sklearn.metrics import recall_score
import datasets
UpperCamelCase_ = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.... | 611 | 1 |
'''simple docstring'''
from copy import deepcopy
class snake_case__ :
"""simple docstring"""
def __init__( self : int, _snake_case : List[str] = None, _snake_case : List[str] = None ) ->Optional[int]:
if arr is None and size... | 701 |
from __future__ import annotations
def lowercase_ (A : list[int] ):
return len(set(A ) ) == len(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 243 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_UpperCamelCase : List[Any] = 0
_UpperCamelCase : Union[str, Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas... | 396 | import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
__a : Tuple = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
__a : List[str] = requests.get(url, ... | 534 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a_ = logging.get_logger(__name__)
a_ = OrderedDict(
[
... | 349 |
"""simple docstring"""
def UpperCAmelCase_ ( __a : int = 10_00 ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : Dict = 1, 1
_lowerCamelCase : Optional[Any] = 2
while True:
_lowerCamelCase : str =... | 349 | 1 |
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