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 |
|---|---|---|---|---|
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 299 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
i... | 299 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__snake_case = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 361 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 219 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( __UpperCamelCase , unittest.TestCase ):
... | 20 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A =""
__A =""
__A =""
__A =1 # (0 is vertical, 1 is horizontal)
def a ( ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmel... | 226 | 0 |
'''simple docstring'''
from __future__ import annotations
from random import random
class lowercase :
def __init__( self , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Tuple = value
UpperCamelCase__ :Any = ra... | 354 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''distilbert-base-unc... | 219 | 0 |
from __future__ import annotations
import numpy as np
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
return np.maximum(0 , lowerCAmelCase__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 101 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 101 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> bool:
return len(set(_lowercase ) ) == len(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 338 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 11 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-... | 108 | 0 |
'''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 = logging.get_lo... | 16 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enab... | 16 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
__a = 42 # [batch_size x 3]
__a = 42 # [batch_size x 3]
__a =... | 115 | import socket
def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE__ = socket.gethostname()
SCREAMING_SNAKE_CASE... | 219 | 0 |
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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 352 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@requi... | 306 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_... | 65 | from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__lowerCamelCase : str = input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
__lowerCamelCase : Any = BeautifulSoup(requests.get(url).conten... | 219 | 0 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __lowerCamelCase (UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version"... | 206 | 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
_lowerCamelCase : Union[str, Any] = logging.get_logger(__nam... | 206 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__lowercase : List[Any] = 4
__lowercase : Dict = 3
class __low... | 318 | from ...processing_utils import ProcessorMixin
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = ["""image_processor""", """feature_extractor"""]
UpperCAmelCase_ : Optional[int] = """TvltImageProcessor"""
UpperCAmelC... | 338 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'''huggingface/inform... | 358 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for... | 208 | 0 |
"""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
lowerCAmelCase_ = log... | 16 |
"""simple docstring"""
import argparse
import os
# New Code #
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_sched... | 16 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (PNDMScheduler,)
UpperCAmelCase : ... | 271 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a = ... | 271 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase ( tf.keras.layers.Layer ):
def __init__(self , ... | 318 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Aut... | 318 | 1 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=None ) -> Optional[int]:
'''simple... | 355 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows !... | 313 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
lowerCamelCase :Tuple = list[tuple[int, int]]
lowerCamelCase :Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],... | 206 |
'''simple docstring'''
import argparse
import struct
import unittest
class _lowerCAmelCase :
def __init__(self , lowercase ):
A_ : List[str] = data
# Initialize hash values
A_ : Tuple = [
0X6A09_E667,
0XBB67_AE85,
0X3C6E_F3... | 206 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ : List[An... | 190 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowercase__ : Dict = {
'''linear''': PIL.Image.Resampling.BILINE... | 190 | 1 |
"""simple docstring"""
import torch
from transformers import AutoModel
class UpperCamelCase ( torch.nn.Module ):
def __init__( self, lowerCAmelCase__="sayef/fsner-bert-base-uncased") -> List[Any]:
super(lowerCAmelCase__, self).__init__()
snak... | 69 |
'''simple docstring'''
from collections.abc import Sequence
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase ,_lowerCAmelCas... | 208 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase ... | 360 |
import math
def UpperCamelCase ( _A : float , _A : float )-> float:
"""simple docstring"""
if (
not isinstance(_A , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("pow... | 198 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase_ (__a : Callable[[int | float], int | float] , __a : int | float , __a : int | float , __a : int = 1_0_0 , ):
"""simple docstring"""
_a : ... | 271 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstri... | 271 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(lo... | 12 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import A... | 12 | 1 |
'''simple docstring'''
import json
import sys
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : int ):
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as f:
__a : Optional[Any] = json.load(_S... | 27 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 0 |
"""simple docstring"""
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,
exec... | 168 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
... | 168 | 1 |
'''simple docstring'''
from math import pi, sqrt, tan
def _lowerCAmelCase ( __snake_case : float ) -> float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
... | 190 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class SCREAMING_SNAKE_CASE (datasets.BuilderConfig ):
lower... | 190 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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-... | 370 |
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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltF... | 94 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
snake_case : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is the... | 94 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ , lowercase__ : Optional[int] = [], []
while len(UpperCAmelCase ) > 1:
lowercase__ , lowercase__ : List[str] = min(UpperCAmelCase ), max(UpperCAmelCase )
start.append(UpperCAme... | 198 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONA... | 361 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return base * power(SCREAMING_SNAKE_CASE , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
... | 93 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : list[int | float] , A__ : int , A__ : int ):
'''simple docstring'''
if len(A__ ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (... | 12 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase_ ... | 12 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
fr... | 352 |
from __future__ import annotations
UpperCAmelCase__ : List[str] = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 301 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('Inductan... | 168 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : Union[str, Any] = {
"config... | 168 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number | (1 << position)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number & ~(1 << position)
def _lowercase ( __lower... | 56 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
... | 40 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 94 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeat... | 16 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _A ( metaclass=__SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : List[str] = ["sentencepiece"]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> ... | 16 | 1 |
'''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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
fro... | 31 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentT... | 93 | 0 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _lowercase ( pl.LightningModule ):
"""simple docstring"""
def __init__(... | 364 | """simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 200_0000 ) ->int:
'''simple docstring'''
a : list[int] = [0]
a : int
for id... | 105 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePr... | 301 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_... | 352 | import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_... | 165 | 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_availabl... | 56 |
'''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 ... | 56 | 1 |
from __future__ import annotations
A : List[str] = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
A : Optional[Any] = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def __lowerCAmelCase ( a__ ) -> list[float]:
__a ... | 367 |
import sys
def __lowerCAmelCase ( a__ ) -> Optional[int]:
__a = len(a__ )
__a = [[0 for x in range(a__ )] for x in range(a__ )]
__a = [[0 for x in range(a__ )] for x in range(a__ )]
for chain_length in range(2 , a__ ):
for... | 33 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils im... | 16 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( s... | 16 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils im... | 364 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> List[Any]:
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase__ , ... | 113 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 78 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCamelCase_( snake_case : Any ):
'''simple docstring'''
if (
(cp >= 0X4E00 and cp... | 85 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a : Union[str, Any] ... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extrac... | 338 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 215 |
"""simple docstring"""
from math import ceil
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = list(range(0 , snake_case__ ) )
SCREAMING_SNAKE_CASE__ = [item for sublist in list(device_map.value... | 165 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :Optional[int] = 1_0 , lowerCAmelCase__ :Tuple = 1_0_0_0 , lowerCAmelCase__ :str = True ) -> int:
'''simple docstring'''
assert (
isinstance(__snake_case , __snake_case )
and... | 363 | """simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCAmelCase : Tuple ={
"""facebook/mask2former-swin-small-coco-instance""": (
""... | 32 | 0 |
"""simple docstring"""
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six... | 197 |
"""simple docstring"""
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : int = 0
lowercase_ : Optional[Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
... | 33 | 0 |
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_image_size,
rescale,
resize,
to_channel_dime... | 87 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
... | 87 | 1 |
lowercase : dict[tuple[int, int, int], int] = {}
def A_ ( A__ , A__ , A__ ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have ... | 99 |
"""simple docstring"""
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__Upper... | 113 | 0 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_SCREAMING_SNAKE_CASE = numpy.array([0, 0])
_SCREAMING_SNAKE_CASE = numpy.array([0.5, 0.8_6_6_0_2_5_4])
_SCREAMING_SNAKE_... | 217 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
B... | 217 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_lowerCAmelCase = logging.get_logger(__name__)
class A ( UpperCamelCase_ ):
'''simple docstring'''
def _... | 298 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
... | 338 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_f... | 176 |
from math import pow
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
... | 176 | 1 |
"""simple docstring"""
from math import sqrt
def _lowerCamelCase ( _UpperCamelCase = 100_0000 ):
'''simple docstring'''
__lowerCAmelCase = 0
__lowerCAmelCase = 0
__lowerCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shorte... | 57 |
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : str ) -> list[int]:
"""simple docstring"""
a_ : Any = int(__A )
# Initialize Result
a_ : Tuple = []
# Traverse through all denomination
for denomination ... | 32 | 0 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCamelCase = 4
UpperCamelCase = 3
class snake_case_ ( __A ):
pass
... | 357 | def lowercase_ ( _lowerCamelCase : int = 10 , _lowerCamelCase : int = 1000 , _lowerCamelCase : bool = True):
assert (
isinstance(_lowerCamelCase , _lowerCamelCase)
and isinstance(_lowerCamelCase , _lowerCamelCase)
and isinstance(_lowerC... | 333 | 0 |
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_configuration_common imp... | 87 | 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 lowercase_ ( _lower... | 87 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import t... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase = {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}
try:
if not is_visio... | 234 | 0 |
"""simple docstring"""
from math import factorial
__A = {str(d): factorial(d) for d in range(10)}
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(__SCREAMING_SNAKE_CASE ) )
def a__ ( ) -> int:
__lowerCAmelCas... | 217 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: Optional[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: List[str] ... | 217 | 1 |
UpperCAmelCase_ = '''Tobias Carryer'''
from time import time
class lowerCamelCase__:
def __init__( self: List[str] , UpperCamelCase_: List[str] , UpperCamelCase_: List[Any] , UpperCamelCase_: int , UpperCamelCase_: Any=int(time() ) ): # noqa: B... | 369 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'n... | 29 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class lowercase__ ( _UpperCAmelCase ):
... | 176 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_ver... | 176 | 1 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim ... | 246 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
... | 246 | 1 |
__A : Dict = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''s... | 138 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeni... | 333 | 0 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase :Tuple = "Muhammad Umer Farooq"
__UpperCAmelCase :Tuple = "MIT"
__UpperCAmelCase :Union[str, Any] = "1.0.0"
__UpperCAmelCase :Optional[int] = "Muhammad Umer F... | 240 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_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_comm... | 240 | 1 |
"""simple docstring"""
import numpy
# List of input, output pairs
lowercase__ : List[str] = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
lowercase__ : Optional[int] = (((5_1_5, 2_2,... | 224 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_... | 234 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if no... | 231 |
from __future__ import annotations
import queue
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : Dict) ->Any:
'''simple docstring'''
A__ = data
A__ = None
... | 231 | 1 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, ... | 18 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..model... | 29 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nic... | 352 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[Any] , *__A: Union[str, An... | 75 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipe... | 246 |
"""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 ..... | 246 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffus... | 142 |
'''simple docstring'''
from manim import *
class _a ( __lowerCAmelCase ):
def _lowercase ( self ) -> Optional[int]:
_snake_case = Rectangle(height=0.5 ,width=0.5 )
_snake_case = Rectangle(height=0.4_6 ,width=0.4_6 ... | 142 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
'''huggingface/autoformer-tourism-monthly''': '''http... | 240 |
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
snake_case : List[Any] = datasets.logging.get_logger(__name__)
snake_case : List[str] = '''\
@InPro... | 240 | 1 |
"""simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ) -> List[Any]:
_lowerCAmelCase ... | 353 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP... | 341 | 0 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_A = [
"word_embeddings_layernorm.weight",
"word_embeddings_... | 231 |
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 ( __a , unittest.TestCase ):
_lo... | 231 | 1 |
"""simple docstring"""
import itertools
import os
import re
a : str = re.compile(R'''([A-Z]+)([A-Z][a-z])''')
a : Any = re.compile(R'''([a-z\d])([A-Z])''')
a : str = re.compile(R'''(?<!_)_(?!_)''')
a : Optional[int] ... | 362 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 50 ) ->int:
'''simple docstring'''
a : Any = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for ... | 79 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 89 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = lo... | 75 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> float:
SCREAMING_SNAKE_CASE = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
... | 38 |
"""simple docstring"""
from __future__ import annotations
def lowercase (SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
return len(set(SCREAMING_SNAKE_CASE_ ) ) == len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 142 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : str = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __init__( self : Dict , *A : Any , **A : List[... | 142 | 1 |
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
_a : Tuple= datasets.logging.get_logger(__name__)
_a : Optional[int]= '\\n@InProceedings{moosavi2019minimum,\n author... | 367 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_rofo... | 95 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Any = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConf... | 91 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
... | 341 | 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 FlaxGen... | 203 | """simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowercase__ = logging.get_logger(__name__)... | 203 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requir... | 10 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 79 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[int] = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Ch... | 252 |
from itertools import count
def UpperCamelCase ( _a = 5_0 ) -> int:
'''simple docstring'''
lowercase_ :Dict = [1] * min_block_length
for n in count(_a ):
fill_count_functions.append(1 )
for block_length in... | 252 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mas... | 38 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCoche... | 38 | 1 |
"""simple docstring"""
import random
class _UpperCAmelCase :
'''simple docstring'''
@staticmethod
def __lowerCAmelCase ( A ) -> tuple[list[int], list[int]]:
_UpperCAmelCase : Dict = [ord(A ) for i in text]
_UpperCAmelCase ... | 68 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from .... | 68 | 1 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowerCAmelCase = Lock()
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCam... | 37 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json"""... | 95 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
a_ = logging.get_logger(__name__)
a_ = r'\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n ... | 50 | a_ = '\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
a_ = [{'type': 'code', 'content': INSTALL_CONTENT}]
a_ = {
'{processor_class}': 'FakeProcessorClass',
'{model... | 50 | 1 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __lowerCAmelCase ( lowercase : str ) -> Any:
"""simple docstring"""
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"... | 203 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCAmelCase ( unittest.TestCase ):
__UpperCAmelCase : Union[str, Any] = JukeboxTokenizer
__UpperCAmelCase : Union[str, Any] ... | 203 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils imp... | 362 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def a_ ( __snake_case : Any ) -> Tuple:
"""simple do... | 6 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowercase ( a_ ):
"""simple docstring"""
UpperCamelCase : Tuple = (EulerDiscreteScheduler,)
UpperCamelCase : An... | 252 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"vocab_file": "vocab.json",
"merges_... | 252 | 1 |
from math import isqrt
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 370 | import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __snake_case ( unittest.TestCase ):
def __a ( self : Dict ):
"""simple docstring"""
... | 204 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxCon... | 68 |
import copy
import random
from transformers import CLIPTokenizer
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowercase , **lowe... | 68 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel... | 18 | '''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 18 | 1 |
_UpperCAmelCase : Dict = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDow... | 50 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=() , _UpperCAmelCase=None , _UpperCAmelCas... | 50 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_... | 365 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( self) -> Tuple:
... | 84 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Tuple = (PNDMScheduler,)
lowerCAmelCase_ : Optional[int] = (("num_inference... | 138 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A( a ):
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''ChineseCLIPImageProcessor'''
snake_case_ = ('''BertTokeni... | 6 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor... | 351 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transfor... | 26 | 0 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_path... | 59 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A... | 204 | 0 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowercase__ = TypeVar("T")
class A_ ( Generic[T] ):
... | 280 |
'''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
lowercase__ = {
"facebook/maskformer-s... | 280 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
def _snake_case ( lowerCAmelCase : Tuple ):
"""simple docstring"""... | 18 | import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran... | 18 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a... | 365 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a : List[str] = """▁"""
_a : Optional[int] = {"""vocab_file""... | 46 | 0 |
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