code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a_ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a_... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int = 4000000 ) ->int:
'''simple docstring'''
__A : Optional[int] = [0, 1]
__A : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
from typing import Any
class __snake_case :
"""simple docstring"""
def __init__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Union[str, Any] = data
__A : Any = None
def __repr__( self ... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
import argparse
import os
import re
a_ = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_ = re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that mat... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, 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... | 358 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 291 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
cla... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __snake_case ( _lowercase ):
"""simple docstring"""
_lowerCamelCase = """philschmid/bart-large-cnn-samsum"""
_lowerCamelCase = (
"""This is a tool... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = ... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
from math import pow
def __lowercase ( snake_case_ : Union[str, Any] ,snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : Union[str, Any] ,snake_case_ : Tuple ,) ->tuple[int, int]:
'''simple docstring'''
... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace 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
... | 291 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=_A ):
"""simple docstring"""
_lowerCamelCase = ["""torch"""]
def __init__( self , *__lowerCamelCase , **__lowerCamelCase ):
'''simple docstring'''
... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __lowercase ( snake_case_ : Union[str, Any] ) ->int:
'''simple docstring'''
if not postfix_notation:
return 0
__A : List[str] = {'''+''', '''-'... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffu... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
#
# ... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
from timeit import timeit
a_ = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal pana... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ve... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
import re
from filelock import FileLock
try:
import nltk
a_ = True
except (ImportError, ModuleNotFoundError):
a_ = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __lowercase ( s... | 350 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 291 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
from itertools import count
def __lowercase ( snake_case_ : int = 50 ) ->Dict:
'''simple docstring'''
__A : Tuple = [1] * min_block_length
for n in count(UpperCamelCase__ ):
fill_count_functions.append(1 )
for blo... | 352 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 291 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
a_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __snake_case (... | 353 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_ca... | 291 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import ... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__( self ):
'''simple... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_commo... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartToken... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
... | 358 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 291 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMulti... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pip... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
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_ = logging.get_logger(__name__)
a_ = {
"""facebook/data2ve... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __lowercase ( snake_case_ : Dict ) ->np.ndarray:
'''simple docstring'''
__A : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_98... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace 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
... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int = 100 ) ->int:
'''simple docstring'''
__A : Optional[int] = n * (n + 1) * (2 * n + 1) / 6
__A : Optional[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - s... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_util... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
a_ = {
"""SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __snake_case ( datasets.BeamBasedBuilder ):
"""simple docstring"""
de... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_lowerCamelCase = ["""image_processor""", """tokenizer"""]
_lowerCamelCase = """ViTImagePr... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : Optional[Any] = 10 ) ->str:
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_ ) or n < 0:
raise ValueError('''Invalid input''' )
__A : Any = 10**n
... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
import heapq
import sys
import numpy as np
a_ = tuple[int, int]
class __snake_case :
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
__A : Any = []
__A : ... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = '▁'
a_ = {'vocab_file': 'prophe... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __lowercase ( snake_case_ : int ,snake_case_ : int = 2 ,snake_case_ : int = 1 ,snake_case_ : int = 3 ,) ->Optional[Any]:
'''simple docstring'''
if num < 2:
... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_case ( snake_case_ ):
... | 350 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : str ) ->Any:
'''simple docstring'''
__A : Any = 0
for ch in input_str:
__A : Any = ord(A__ )
__A : Optional[int] = pow(2 ,A__ ... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
a_ = """scheduler_config.json"""
class __snake_case ( __lowercase ):
... | 352 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int = 1000000 ) ->List[Any]:
'''simple docstring'''
__A : Optional[Any] = 1
__A : Optional[Any] = 1
__A : Optional[int] = {1: 1}
for inputa in ... | 353 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_ca... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int = 100 ) ->Any:
'''simple docstring'''
__A : Tuple = (n * (n + 1) // 2) ** 2
__A : Optional[int] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
i... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
# Copyright 2023 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... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __snake_case :
"""simple docstring"""
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCame... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"configuration_efficientnet": [
"EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientNetConfig",
... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
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_ = logging.get_logger(__name__)
a_ = {
'google/mobilenet_v... | 358 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 291 | 0 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
a_ = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto acc... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class __snake_case ( __a ):
"""simple docstring"""
_lowerCamelCase = field(default=""... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def __lowercase ( snake_case_ : str = "" ,) ->bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' ,'''''' ).lower() ).values() ... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
import torch
def __lowercase ( ) ->Tuple:
'''simple docstring'''
if torch.cuda.is_available():
__A : int = torch.cuda.device_count()
else:
__A : int = 0
print(F"""Successfully ran on ... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace 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
... | 291 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILIma... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : list[list[float]] ) ->Tuple:
'''simple docstring'''
__A : List[Any] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
if len(SCREAMING_SN... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
a_ = """Tobias Carryer"""
from time import time
class __snake_case :
"""simple docstring"""
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=int(time() ) ): # noqa: B008
'''simple docs... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __lowercase ( snake_case_ : int ,snake_case_ : int = 2 ,snake_case_ : int = 1 ,snake_case_ : int = 3 ,) ->int | None:
'''simple docstring'''
if num < 2:
rai... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase ( snake_case_ : List[Any] ,snak... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __lowercase ( snake_case_ : Any ) ->Union[str, Any]:
'''simple docstring'''
__A : str = tf.convert_to_tensor(lowerC... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
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_ = logging.get_logger(__name__)
a_ = {
'''facebook/data2ve... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
a_ = logging.get_logger(__name__)
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamel... | 350 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( snake_case_ : list[list[int]] ) ->bool:
'''simple docstring'''
__A : Dict = len(snake_case_ )
# We need to create solution object to save path.
__A : ... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils imp... | 352 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""... | 353 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_ca... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ) ->int:
'''simple docstring'''
def update_area_of_max_square(snake_case_ : int ,snake_case_ : int ) -> int:
# BASE CASE
... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a_ = logging.get_logger(... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_case ( SCREAMING_SNAKE_CASE... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a_ = abspath(join(dirname(dirname(dirname(__file_... | 358 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 291 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, 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 ...tes... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
from PIL import Image
def __lowercase ( snake_case_ : Image ) ->Image:
'''simple docstring'''
__A : Any = image.size
__A : Tuple = 0
__A : List[str] = image.load()
for i... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace 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
... | 291 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
i... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLTokeniz... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..o... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {"""voc... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_uti... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __lowercase ( snake_case_ : bool = True ,*snake_case_ : Optional[int] ,**snake_case_ : Union[str, Any] ) ... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a_ = 10
def __lowercase ( snake_case_ : int ,snake_case_... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttenti... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from t... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
from scipy.stats import spearmanr
import datasets
a_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as dat... | 350 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 291 | 0 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
fr... | 352 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 291 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __lowercase ( ) ->List[str]:
'''simple docstring'''
__A : List[Any] =... | 353 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_ca... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : float ) ->float:
'''simple docstring'''
if edge <= 0 or not isinstance(snake_case_ ,snake_case_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
import qiskit
def __lowercase ( snake_case_ : int = 2 ) ->qiskit.result.counts.Counts:
'''simple docstring'''
__A : Tuple = qubits
# Using Aer's simulator
__A : List[str] = qiskit.Aer.get_bac... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int = 1000000 ) ->int:
'''simple docstring'''
__A : Union[str, Any] = 1
__A : int = 1
__A : Union[str, Any] = {1: 1}
for inputa in range(2 ,sna... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelC... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig"... | 358 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 291 | 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(
[
# ... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__( self ):
'''simple docstring'''
... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace 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
... | 291 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""YituTech/conv-bert-base""": """https://huggingf... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowercase ... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
import numpy as np
def __lowercase ( snake_case_ : np.ndarray ) ->np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowercase ( snake_case_ : np.ndarray ) ->np.ndarray:
... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
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