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"""
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : str ) -> int:
'''simple docstring'''
if len(_UpperCamelCase ) != len(_UpperCamelCase ):
raise ValueError("""String lengths must match!""" )
... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : Optional[int] ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 0
while b > 0:
if b & 1:
res += a... | 320 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase ( _UpperCamelCase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
def wrapper... | 320 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""image_processor""", """tokenizer"""]
__a = """AutoImageP... | 320 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""image_processor""", """tokenizer"""]
__a = """AutoImageP... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : list , _UpperCamelCase : list , _UpperCamelCase : int ) -> int:
'''simple docstring'''
if len(_UpperCamelCase ) != len(_UpperCamelCase ):
raise ValueError("""The lengt... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opt... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , ) -> tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) ... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : List[str] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
... | 320 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase ( ) -> str:
'''simple docstring'''
__UpperCAmelCase ,__UpperCAmelCase : Tuple = 9, 1_4 # noqa: F841
... | 320 |
"""simple docstring"""
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : List[str] = 1
while len(_UpperCamelCase ) < 1E6:
constant.append... | 320 | 1 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {
'configuration_electra': ['... | 320 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
UpperCAmelCase : str = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c},... | 320 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ... | 320 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
UpperCAmelCase : Tuple = None
try:
import msvcrt
except ImportError:
UpperCAmelCase : List[Any] = None
try:
import f... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCAmelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCAmelCase : list[int] = ... | 320 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase : str = {1: (1, 1), 2: (2, 1), 3: (3, 1),... | 320 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
'microsoft/unispeech-large-1500h-cv... | 320 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 320 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from trans... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : Optional[int] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = len(_UpperCamelCase )
__UpperCAmelCase : List[Any] = sum(_UpperCamelCa... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase : int = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
... | 320 |
"""simple docstring"""
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as ... | 320 |
"""simple docstring"""
from collections.abc import Sequence
def lowerCamelCase ( _UpperCamelCase : Sequence[float] , _UpperCamelCase : float ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase ) )
def low... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowerCamelCase ( _UpperCamelCase : list[float] ) -> Optional[Any]:
'''simple docstring'''
return np.maximum(0 , _UpperCamelCase )
if __name__ == "__main__":
print(np.array... | 320 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_... | 320 | 1 |
"""simple docstring"""
import math
def lowerCamelCase ( ) -> None:
'''simple docstring'''
__UpperCAmelCase : Any = input("""Enter message: """ )
__UpperCAmelCase : int = int(input(f'''Enter key [2-{len(_UpperCamelCase ) - ... | 320 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tra... | 320 | 1 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
UpperCAmelCase : Tuple = [
'Prosecutor: "No videos were used in the crash investigation" German papers ... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(_UpperCamelCase : int , _UpperCamelCase : int )... | 320 |
"""simple docstring"""
UpperCAmelCase : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase ( _UpperCamelCase : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCamelCase__ ( A )... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( A ):
"""simple docstring"""
def _... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
__UpperCAmelCase : Dict = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def lowerCa... | 320 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 | 1 |
"""simple docstring"""
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, p... | 320 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : Dict = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : Optional[int] ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 0
while b > 0:
if b & 1:
res += a... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : int ) -> int:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n... | 320 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""image_processor""", """tokenizer"""]
__a = """AutoImageP... | 320 | 1 |
"""simple docstring"""
import math
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
'''simple docstring'''
if (
not isinstance(_UpperCamelCase , (int, float) )
or power_factor < -1
or po... | 320 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def lowerCamelCase ( _UpperCamelCase : i... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_... | 320 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vi... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opt... | 320 | 1 |
"""simple docstring"""
import argparse
UpperCAmelCase : Optional[int] = 'docs/source/_static/js/custom.js'
def lowerCamelCase ( _UpperCamelCase : str ) -> Union[str, Any]:
'''simple docstring'''
with open(_UpperCamelCase , encoding="""utf-8""" , ... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : List[str] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
... | 320 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : Dict = {
'facebook/wav2vec2-base-960h': 'https://huggingfa... | 320 |
"""simple docstring"""
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : List[str] = 1
while len(_UpperCamelCase ) < 1E6:
constant.append... | 320 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
UpperCAmelCase : str = logging.get_logger(__name__)
def lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperC... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {
'configuration_electra': ['... | 320 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common impo... | 320 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ... | 320 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase ( _UpperCamelCase : str , _Upp... | 320 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase : str = {1: (1, 1), 2: (2, 1), 3: (3, 1),... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int = 6_0_0_8_5_1_4_7_5_1_4_3 ) -> int:
'''simple docstring'''
try:
__UpperCAmelCase : str = int(_UpperCamelCase )
except (TypeError, ValueError):
ra... | 320 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : str , _UpperCamelCase : Optional[int] , _UpperCamelCase : str , _UpperCamelCase : Dict ) -> List[str]:
'''simp... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : Optional[int] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = len(_UpperCamelCase )
__UpperCAmelCase : List[Any] = sum(_UpperCamelCa... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : str = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Tra... | 320 |
"""simple docstring"""
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
Aut... | 320 |
"""simple docstring"""
from collections.abc import Sequence
def lowerCamelCase ( _UpperCamelCase : Sequence[float] , _UpperCamelCase : float ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase ) )
def low... | 320 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
UpperCAmelCase : Tuple = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block'... | 320 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_... | 320 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tra... | 320 | 1 |
"""simple docstring"""
import warnings
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 c... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 |
"""simple docstring"""
UpperCAmelCase : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase ( _UpperCamelCase : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class lowerCamelCase__ ( A ):
"""simple docstring"""
... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( A ):
"""simple docstring"""
def _... | 320 | 1 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_av... | 320 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase : Tuple = logging.get... | 320 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 | 1 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : Optional[int] ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 0
while b > 0:
if b & 1:
res += a... | 320 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
from torch.utils.cpp_extension import load
__UpperCAmelCase : Any = Path(_UpperCamelCase ).resolve().parent.parent.parent / ... | 320 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""image_processor""", """tokenizer"""]
__a = """AutoImageP... | 320 | 1 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase : Optional[int] = datasets.logging.get_logger(__name__)
UpperCAmelCase : Any ... | 320 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.uti... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_... | 320 | 1 |
"""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_co... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opt... | 320 | 1 |
"""simple docstring"""
import os
def lowerCamelCase ( _UpperCamelCase : str = "input.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(_UpperCamelCase ) , _UpperCamelCase ) ) as input_file:
__UpperCAmelCase ... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : List[str] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
... | 320 | 1 |
"""simple docstring"""
import re
def lowerCamelCase ( _UpperCamelCase : str ) -> bool:
'''simple docstring'''
__UpperCAmelCase : str = re.compile(R"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(_UpperCamelCase , _Up... | 320 |
"""simple docstring"""
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : List[str] = 1
while len(_UpperCamelCase ) < 1E6:
constant.append... | 320 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCAmelCase : Any = logging.get_... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {
'configuration_electra': ['... | 320 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowerCamelCase__ ( unittest.... | 320 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ... | 320 | 1 |
"""simple docstring"""
from PIL import Image
def lowerCamelCase ( _UpperCamelCase : Image , _UpperCamelCase : int ) -> Image:
'''simple docstring'''
__UpperCAmelCase : List[str] = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
d... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_... | 320 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase : str = {1: (1, 1), 2: (2, 1), 3: (3, 1),... | 320 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : str = logging.get_logger(__name__)
UpperCAmelCase : Dict = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.jso... | 320 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError("""Inductance cannot b... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : Optional[int] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = len(_UpperCamelCase )
__UpperCAmelCase : List[Any] = sum(_UpperCamelCa... | 320 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : Any = {
'SenseTime/deformable-detr':... | 320 |
"""simple docstring"""
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
import requests
UpperCAmelCase : Optional[int] = 'YOUR API KEY'
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : str = giphy_api_key ) -> list:
'''simple docstring'''
__UpperCAmelCase : List[Any] ... | 320 |
"""simple docstring"""
from collections.abc import Sequence
def lowerCamelCase ( _UpperCamelCase : Sequence[float] , _UpperCamelCase : float ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase ) )
def low... | 320 | 1 |
"""simple docstring"""
from functools import reduce
UpperCAmelCase : str = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295... | 320 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_... | 320 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : Tuple , *UpperCamelCase :... | 320 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tra... | 320 | 1 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md"""... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
UpperCAmelCase : Optional[Any] = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter... | 320 |
"""simple docstring"""
UpperCAmelCase : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase ( _UpperCamelCase : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( A ):
"""simple docstring"""
def _... | 320 | 1 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase : List[str]=None , UpperCamelCase : int=None ):
'''simple docstring... | 320 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 320 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 | 1 |
"""simple docstring"""
from typing import Any
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase : Any ):
'''simple docstring'''
__UpperCAmelCase : List[str] = data
... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : Optional[int] ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 0
while b > 0:
if b & 1:
res += a... | 320 | 1 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : Optional[Any] = HfApi()
UpperCAmelCase : Tuple = {}
# fmt: off
UpperCAmelCase : Tuple = torch.tensor([
-0.7515... | 320 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""image_processor""", """tokenizer"""]
__a = """AutoImageP... | 320 | 1 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase : Optional[int] = logging.getLogger()
@unit... | 320 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 | 1 |
"""simple docstring"""
import os
def lowerCamelCase ( ) -> Optional[int]:
'''simple docstring'''
with open(os.path.dirname(_UpperCamelCase ) + """/p022_names.txt""" ) as file:
__UpperCAmelCase : List[str] = str(file.readlines()[0] ... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_... | 320 | 1 |
"""simple docstring"""
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,
)... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opt... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_avail... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : List[str] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : Dict ) -> str:
'''simple docstring'''
__UpperCAmelCase : str = [1]
for i in range(2 , _UpperCamelCase ):
factorials.append(... | 320 |
"""simple docstring"""
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : List[str] = 1
while len(_UpperCamelCase ) < 1E6:
constant.append... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
'''simple docstring'''
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(_UpperCa... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {
'configuration_electra': ['... | 320 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int ) -> str:
'''simple docstring'''
if isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinsta... | 320 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ... | 320 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : List[Any] ... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase : List[Any] = {
# 1536-bit
5... | 320 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase : str = {1: (1, 1), 2: (2, 1), 3: (3, 1),... | 320 | 1 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
UpperCAmelCase : List[Any] = {
'facebook/... | 320 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 320 | 1 |
"""simple docstring"""
import argparse
import json
import subprocess
def lowerCamelCase ( _UpperCamelCase : Optional[Any] , _UpperCamelCase : int ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : str = []
__UpperCAmelCase ... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : Optional[int] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = len(_UpperCamelCase )
__UpperCAmelCase : List[Any] = sum(_UpperCamelCa... | 320 | 1 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = """M-CLIP"""
def __init__( self : Tuple , UpperCamelCase : str=1_0... | 320 |
"""simple docstring"""
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Tup... | 320 |
"""simple docstring"""
from collections.abc import Sequence
def lowerCamelCase ( _UpperCamelCase : Sequence[float] , _UpperCamelCase : float ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase ) )
def low... | 320 | 1 |
"""simple docstring"""
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate imp... | 320 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_... | 320 | 1 |
"""simple docstring"""
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_ch... | 320 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tra... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXL... | 320 |
"""simple docstring"""
UpperCAmelCase : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase ( _UpperCamelCase : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ... | 320 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""flax"""]
def __init__( self : Tuple , *UpperCamelCase : List[str] , **UpperCamelCase :... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( A ):
"""simple docstring"""
def _... | 320 | 1 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = """EncodecFeatureExtractor"""
__a ... | 320 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 | 1 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase : int = 6378137.0
UpperCAmelCase : Tuple = 6356752.314245
UpperCAmelCase : List[str] = 637_8137
def lowerCamelCa... | 320 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase__ ... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : Optional[int] ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 0
while b > 0:
if b & 1:
res += a... | 320 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = """MCTCTFeatureExtractor"""
__a = """AutoTokenizer"""
... | 320 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""image_processor""", """tokenizer"""]
__a = """AutoImageP... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Any = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
... | 320 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, preci... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , UpperCamelCase : Optional[int]=None ):
'''simple docstring'''
__UpperCAmelCase : ... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opt... | 320 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCamelCase ( _UpperCamelCase : Union[str, Any]="ro" , _UpperCamelCase : Dict="en" , _UpperCamelCase : Dict="wmt16" , _UpperCamelCase : Optional[int]=None ) -> None:
... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : List[str] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
... | 320 | 1 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class lowe... | 320 |
"""simple docstring"""
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : List[str] = 1
while len(_UpperCamelCase ) < 1E6:
constant.append... | 320 | 1 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lo... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {
'configuration_electra': ['... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , ) -> tuple:
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) !=... | 320 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ... | 320 | 1 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowerCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : T... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : int ... | 320 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase : str = {1: (1, 1), 2: (2, 1), 3: (3, 1),... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {'configuration_opt': ['OPT_PRETR... | 320 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 320 | 1 |
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