code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for e in env_keys:
_snake_case = int(os.environ.get(SCREAMING_SNAKE_CASE__ , -1 ... | 672 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.co... | 638 | 0 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( _lowerCamelCase: Any = "matrix.txt" ):
with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) ) as in_file:
__SCREAMING_SNAKE_CASE : Optional[int] = in_file.read()
... | 578 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective... | 638 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Tuple = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnn... | 497 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig... | 638 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
def __mag... | 458 |
'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.u... | 638 | 0 |
'''simple docstring'''
import math
def __snake_case (__UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : List[Any] = len(SCREAMING_SNAKE_CASE__ )
lowerCamelCase_ : Any = int(math.floor(math.sqrt(SCREAMING_SNAKE_CASE__ ... | 501 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
... | 638 | 0 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase_ = 0B1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1... | 384 |
'''simple docstring'''
from __future__ import annotations
import queue
class snake_case__ :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Optional[int] ) -> Dict:
"""simple docstring"""
... | 638 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( lowerCAmelCase__ ):
if len(SCREAMING_SNAKE_CASE__ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for... | 260 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
... | 638 | 0 |
import numpy as np
def lowerCAmelCase__ ( a__ ) ->np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( a__ ) ->np.ndarray:
'''simple docstring'''
return vector * sigmoid(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
... | 547 |
'''simple docstring'''
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
fro... | 638 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __lowerCAmelCase ( lowercase : ArgumentParser ):
'''simple docstring'''
... | 595 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTes... | 638 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[str] = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 231 |
'''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
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 638 | 0 |
'''simple docstring'''
__magic_name__ = {}
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : List[str] , lowerCamelCase : Tuple):
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
... | 665 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCo... | 638 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingS... | 672 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 638 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__ : Tuple = []
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: str , _lowerCamelCase: Optional[int] ):
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
if board[row][i] == ... | 578 |
'''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... | 638 | 0 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , __A : str , __A : str ):
"""simple docstring"""
_lowercase = text, patter... | 497 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
lowercase__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, ... | 638 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_F... | 458 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase__ = TypeVar("T")
class snake_case__ ( Generic[T] ):
"""simple docstring"""
def __init__( se... | 638 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__lowerCamelCase : List[Any] = False
class ... | 501 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowercase__ = get_logger(__name__)
class snake_case__ ( enum.Enum ):
"""simple docstring... | 638 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : List[str] , _lowerCamelCase : str ) -> Optional[int]:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(SCREAMING_SNAKE_CASE__ , n - 1 ,... | 384 |
'''simple docstring'''
import numpy as np
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simp... | 638 | 0 |
"""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 ... | 260 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
lowercase__ = logging.get_logger(__name__)
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def _... | 638 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCAmelCase__ ( a__ ) ->Any:
'''simple docstring'''
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def lowerCAmelC... | 547 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 200_0000 ) -> int:
'''simple docstring'''
snake_case : list[int] = [0]
snake_case : int
for id... | 638 | 0 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone i... | 595 |
'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_fea... | 638 | 0 |
"""simple docstring"""
import re
from ..utils import cached_file
# docstyle-ignore
__A : List[Any] = '''\nHuman: <<task>>\n\nAssistant: '''
__A : Optional[int] = '''huggingface-tools/default-prompts'''
__A : Union[str, Any] ... | 231 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
lowercase__ = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={a... | 638 | 0 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaT... | 665 |
'''simple docstring'''
import os
from collections.abc import Iterator
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE__ ):
snake_case : Optiona... | 638 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : str = logging.get_logger(__name__)
__magic_name__ : int = {
"""jun... | 672 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.co... | 638 | 0 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] ):
__SCREAMING_SNAKE_CASE : List[str] = [True] * n
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : Any = False
__SCREAMI... | 578 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective... | 638 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import ... | 497 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig... | 638 | 0 |
from typing import List
import numpy as np
def __magic_name__ ( lowercase ) -> int:
"""simple docstring"""
lowercase_ : List[Any] = {key: len(SCREAMING_SNAKE_CASE__ ) for key, value in gen_kwargs.items() if isinstance(SCREAMING_SNAKE_CASE__ ,... | 458 |
'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.u... | 638 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __snake_case (__UpperCAmelCase = 2000000 ):
"""simple docstring"""
lowerCamelCase_ : list[int] = [0]
lowerCamelCase_ : int
for idx in range(1 , ceil(sqrt(targ... | 501 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
... | 638 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def _UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Any , _lowerCamelCase : Union[str, Any] ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) !=... | 384 |
'''simple docstring'''
from __future__ import annotations
import queue
class snake_case__ :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Optional[int] ) -> Dict:
"""simple docstring"""
... | 638 | 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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetCon... | 260 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
... | 638 | 0 |
def lowerCAmelCase__ ( a__ , a__ ) ->float:
'''simple docstring'''
def get_matched_characters(a__ , a__ ) -> str:
_UpperCamelCase = []
_UpperCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_UpperCa... | 547 |
'''simple docstring'''
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
fro... | 638 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case_ : List[str] = logging.get_logger(__name__)
snake_case_... | 595 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTes... | 638 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import... | 231 |
'''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
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 638 | 0 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = None
a_ = None
def lowerCamelCase ( lowerCamelCase : int):
def is_... | 665 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCo... | 638 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_avail... | 672 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 638 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: int ):
__SCREAMING_SNAKE_CASE : List[Any] = [0] * len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , len(SCREAMING_SNAKE_CASE__ ) ):
# use last results for better performance - dynamic programming
... | 578 |
'''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... | 638 | 0 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__magic_name__ : Any = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''... | 497 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
lowercase__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, ... | 638 | 0 |
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ) -> list[str]:
"""simple docstring"""
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_b... | 458 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase__ = TypeVar("T")
class snake_case__ ( Generic[T] ):
"""simple docstring"""
def __init__( se... | 638 | 0 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF... | 501 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowercase__ = get_logger(__name__)
class snake_case__ ( enum.Enum ):
"""simple docstring... | 638 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] ) -> Union[str, Any]: # noqa: E741
_lowerCAmelCase : Any = len(SCREAMING_SNAKE_CASE__ )
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Tuple = [0] * n
_lo... | 384 |
'''simple docstring'''
import numpy as np
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simp... | 638 | 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'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
lowercase__ = logging.get_logger(__name__)
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def _... | 638 | 0 |
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(), gbellmf(), ga... | 547 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 200_0000 ) -> int:
'''simple docstring'''
snake_case : list[int] = [0]
snake_case : int
for id... | 638 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : int = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""Mgps... | 595 |
'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_fea... | 638 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase ( __snake_case : str , __snake_case : Optional[int] = False ):
if not arr:
return 0
lowercase_ : Any = 0 if allow_empty_subarrays else float... | 231 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
lowercase__ = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={a... | 638 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_electra': ['ELECTRA_PRETRAINED_CON... | 665 |
'''simple docstring'''
import os
from collections.abc import Iterator
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE__ ):
snake_case : Optiona... | 638 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : int = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolF... | 672 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.co... | 638 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 578 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective... | 638 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_co... | 497 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig... | 638 | 0 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fas... | 458 |
'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.u... | 638 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase__ ( __SCREAMING_SNAKE_CASE ):
def __UpperCamelCase ( self : str , UpperCamelCase_ : str ) -> Optional[Any]:
"... | 501 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
... | 638 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import Flax... | 384 |
'''simple docstring'''
from __future__ import annotations
import queue
class snake_case__ :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Optional[int] ) -> Dict:
"""simple docstring"""
... | 638 | 0 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
'... | 260 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
... | 638 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from trans... | 547 |
'''simple docstring'''
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
fro... | 638 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowercase_ ( _lowercas... | 595 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTes... | 638 | 0 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 231 |
'''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
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 638 | 0 |
'''simple docstring'''
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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decode... | 665 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCo... | 638 | 0 |
'''simple docstring'''
from typing import Any
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , lowerCamelCase ):
_snake_case = data
_snake_case = None
def __repr__( self ):
return F'''N... | 672 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 638 | 0 |
'''simple docstring'''
UpperCamelCase__ : int = {
'''joule''': 1.0,
'''kilojoule''': 10_00,
'''megajoule''': 1_00_00_00,
'''gigajoule''': 10_00_00_00_00,
'''wattsecond''': 1.0,
'''watthour''': 36_00,
'''kilowatthour''': 3_60_00_00,
'''newtonmeter''': 1.0,
''... | 578 |
'''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... | 638 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : Optional[Any] = logging.get_logger(__name__)
__magic_name__ : str = {
... | 497 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
lowercase__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, ... | 638 | 0 |
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_mvp import MvpToken... | 458 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase__ = TypeVar("T")
class snake_case__ ( Generic[T] ):
"""simple docstring"""
def __init__( se... | 638 | 0 |
'''simple docstring'''
__lowerCamelCase : Any = range(2, 20 + 1)
__lowerCamelCase : Union[str, Any] = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase : Any = {}
def __snake_case (__UpperCAmelCase , __UpperCAmelCase , __UpperCA... | 501 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowercase__ = get_logger(__name__)
class snake_case__ ( enum.Enum ):
"""simple docstring... | 638 | 0 |
import requests
from bsa import BeautifulSoup
def lowercase_ ( _UpperCamelCase = "AAPL" ):
'''simple docstring'''
__lowercase = F'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
__lowercase = BeautifulSoup(requests.get(_UpperCamelCase ).text , '''html.parse... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_token... | 639 |
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
if is_torch_available():
impo... | 639 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 639 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 1 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if n_term == "":
return []
__lowercase = []
for temp in range(int(_UpperCamelCase ) ):
series.append(F'1/{temp + 1}' if series else '''1''' )
return series
if __name__ == "__main__":
a : List[Any] ... | 639 |
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... | 639 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_ ( lowerC... | 639 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__l... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a : Dict = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHI... | 639 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a :... | 639 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
a : List[str] = TypeVar('''KT''')
a : List[Any] = TypeVar('''VT''')
class lowerCamelCase_ ( Generic[KT, VT] ):
'''simple docstring'''
def __... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
import os
import sys
import unittest
a : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 639 |
from maths.prime_factors import prime_factors
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCamelCase )
... | 639 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from t... | 639 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 1 |
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 transformers import AutoT... | 639 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 1 |
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
@require_tokenizers
class lowerC... | 639 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tran... | 639 | 1 |
from __future__ import annotations
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
e... | 639 |
a : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 639 | 1 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get... | 639 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 639 | 1 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = len(_UpperCamelCase )
__lowercase = len(matrix[0] )
__lowercase = min(_UpperCamelCase , _UpperCamelCase )
for row in range(_UpperCamelCase ):
# Check if diagonal element is... | 639 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 639 | 1 |
def lowercase_ ( _UpperCamelCase , _UpperCamelCase = False ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Expected string as input, found {type(_UpperCamelCase )}'
raise ValueError(_UpperCamelCase )
if n... | 639 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 639 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
tr... | 639 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : Dict = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz... | 639 |
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, BartTokenize... | 639 | 1 |
from itertools import product
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = sides_number
__lowercase = max_face_number * dice_number
__lowercase = [0] * (max_total + 1)
__lowercase = 1
__lower... | 639 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
... | 639 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from... | 639 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCAmelCase = None
__UpperCAmelCase = False
__UpperCAmelCase = F... | 639 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''s... | 639 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__Up... | 639 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 639 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 639 | 1 |
import json
import pathlib
import unittest
import numpy as np
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
... | 639 |
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
if is_torch_available():
impo... | 639 | 1 |
from math import factorial
def lowercase_ ( _UpperCamelCase = 20 ):
'''simple docstring'''
__lowercase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase = n // 2
return int(factorial(_UpperCamelCase ) / (factorial(_... | 639 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 1 |
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
@require_torch
class lo... | 639 |
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... | 639 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase_ ( lowerCAmelCase__ , unittest.TestCase ... | 639 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__l... | 639 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
a : str = {name: getattr(transformers, name + '''Fast''') for nam... | 639 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
a : List[Any] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'... | 639 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 | 1 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
''... | 639 |
from maths.prime_factors import prime_factors
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCamelCase )
... | 639 | 1 |
from __future__ import annotations
import queue
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> List[Any]:
'''simple docstring'''
__lowercase = data
__lowercase = None
_... | 639 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.uti... | 639 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 1 |
import re
from filelock import FileLock
try:
import nltk
a : Dict = True
except (ImportError, ModuleNotFoundError):
a : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def lowercase_ ( _UpperCa... | 639 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tran... | 639 | 1 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common ... | 639 |
a : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 639 | 1 |
import numpy as np
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = int(np.ceil((x_end - xa) / h ) )
__lowercase = np.zeros((n + 1,) )
... | 639 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 639 | 1 |
from scipy.stats import spearmanr
import datasets
a : Optional[int] = '''
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 correl... | 639 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 639 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataC... | 639 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Union[str, Any] = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
a : Dict = _LazyModule(__name__, glo... | 639 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
tr... | 639 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common imp... | 639 |
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, BartTokenize... | 639 | 1 |
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