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 |
|---|---|---|---|---|
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torc... | 359 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 103 | 0 |
def UpperCamelCase ( snake_case__ : list , snake_case__ : int = 0 ) -> list:
UpperCamelCase : List[str] = length or len(snake_case__ )
UpperCamelCase : List[str] = False
for i in range(length - 1 ):
if list_data[i] > list_data[... | 360 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import j... | 103 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=a__ ):
UpperCAmelCase__ : Optional[int] = ["note_seq"]
def __init__( self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> Tuple:
requires_backends(self... | 361 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
f... | 103 | 0 |
import re
def UpperCamelCase ( snake_case__ : str ) -> str:
if len(re.findall('[ATCG]' , snake_case__ ) ) != len(snake_case__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main... | 362 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 103 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, sl... | 363 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines... | 103 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
f... | 364 |
import re
def UpperCamelCase ( snake_case__ : str ) -> str:
if len(re.findall('[ATCG]' , snake_case__ ) ) != len(snake_case__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main... | 103 | 0 |
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 ...t... | 365 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from trans... | 103 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixi... | 366 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torc... | 103 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
'''configuration_owlv... | 367 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, sl... | 103 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_c... | 368 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCAmelCase_ ( a__ ):
@staticmethod
@abstractmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
raise NotImplementedError()
@abstractmethod
def snake_c... | 103 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from... | 369 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBO... | 103 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=a__ ):
UpperCAmelCase__ : Union[str, Any] = ["onnx"]
def __init__( self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
requires_backends(se... | 370 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase__ : Union[str, Any] = JukeboxTokenizer
UpperCAmelCase__ : Optional[int] = {
... | 103 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def UpperCamelCase ( snake_case__ : int ) -> int:
UpperCamelCase : Optional[Any] = prime_factors(snake_case__ )
if is_square_free(snake_case__ ):
return -1 if ... | 371 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key... | 103 | 0 |
import math
def UpperCamelCase ( snake_case__ : int = 100 ) -> int:
UpperCamelCase : str = sum(i * i for i in range(1 , n + 1 ) )
UpperCamelCase : Optional[int] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return... | 350 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 103 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase ( snake_case__ : List[str] , snake_case__ : str )... | 351 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def UpperCamelCase ( ) -> Tuple:
UpperCamelCase : List[str] = 9
UpperCamelCase : Optional[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2,... | 103 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def UpperCamelCase ( ) -> Tuple:
UpperCamelCase : List[str] = 9
UpperCamelCase : Optional[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2,... | 352 |
import argparse
import os
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 import Accelerator, D... | 103 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_i... | 353 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, ... | 103 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__UpperCAmelCase = 0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin... | 354 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( snake_case__ : List[Any] , snake_case__ : Union[str, Any] , snake_case__ : Tuple , snake_case__ : Union[str, Any] , snake_case__ : List[Any]... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOn... | 355 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/data2vec-text-base''': '''https://hugg... | 103 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 356 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checko... | 103 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase_ ( yaml.SafeLoader ):
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
UpperCamelCase : Optional[int] = [self.co... | 357 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 103 | 0 |
from __future__ import annotations
def UpperCamelCase ( snake_case__ : str ) -> list[int]:
return [ord(snake_case__ ) - 96 for elem in plain]
def UpperCamelCase ( snake_case__ : list[int] ) -> str:
return "".join(chr(elem + 96 ) for elem in encoded ... | 358 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 103 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 359 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 103 | 0 |
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 lowerCAmelCase_ ( tf.keras.layers.Layer ):
"""simple docstring"""
... | 360 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import j... | 103 | 0 |
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 = {
'''sail/poolformer_... | 361 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
f... | 103 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INP... | 362 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 103 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_="", SCREAMING_SNAKE_CASE_="train" ) -> Tuple:
assert os.path.isdir(SCREAMING_SNAKE_CASE_ )
... | 363 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines... | 103 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, ... | 364 |
import re
def UpperCamelCase ( snake_case__ : str ) -> str:
if len(re.findall('[ATCG]' , snake_case__ ) ) != len(snake_case__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main... | 103 | 0 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 365 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from trans... | 103 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCamelCase ( snake_case__ : np.ndarray , snake_case__ : np.ndarray ) -> float:
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(snake_case__ , snake_case__ ... | 366 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torc... | 103 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaT... | 367 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, sl... | 103 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from trans... | 368 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCAmelCase_ ( a__ ):
@staticmethod
@abstractmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
raise NotImplementedError()
@abstractmethod
def snake_c... | 103 | 0 |
__UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( snake_case__ : dict[int, list[int]] , snake_case__ : int , snake_case__ : list[bool] ) -> list[int]:
Up... | 369 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBO... | 103 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCamelCase ( ) -> Dict:
UpperCamelCase : List[Any] = ArgumentParser(
description=(
'PyTorch T... | 370 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase__ : Union[str, Any] = JukeboxTokenizer
UpperCAmelCase__ : Optional[int] = {
... | 103 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCAmelCase_ ( a__ ):
@staticmethod
@abstractmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
raise NotImplementedError()
@abstractmethod
def snake... | 371 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key... | 103 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( snake_case__ : str = "AAPL" ) -> str:
UpperCamelCase : Union[str, Any] = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Dict = BeautifulSoup(requests.get(... | 350 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 103 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowerCAmelCase_ ( unittest.TestCase , a__ ):
def snake_case_ ( self ) -> Union[str, Any]:
UpperCamelCase : str = load_tool('text-classification' )
... | 351 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def UpperCamelCase ( ) -> Tuple:
UpperCamelCase : List[str] = 9
UpperCamelCase : Optional[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2,... | 103 | 0 |
def UpperCamelCase ( snake_case__ : str ) -> str:
UpperCamelCase : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase : str = ''
UpperCamelCase : Dict = ''
# append each character + "|" ... | 352 |
import argparse
import os
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 import Accelerator, D... | 103 | 0 |
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 transformers.testing_utils i... | 353 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, ... | 103 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UN... | 354 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( snake_case__ : List[Any] , snake_case__ : Union[str, Any] , snake_case__ : Tuple , snake_case__ : Union[str, Any] , snake_case__ : List[Any]... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 355 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/data2vec-text-base''': '''https://hugg... | 103 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Optional[Any] = ["image_processor", "tokenizer"]
UpperCAmelCase__ : int = "CL... | 356 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checko... | 103 | 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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCamelCase ( snake_case__ : int ) ->... | 357 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 103 | 0 |
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> None:
UpperCamelCase : Any = size
UpperCamelCase : str = [0] * size
UpperCamelCase : Any = [0] * size
@staticmethod
def sn... | 358 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 103 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_enviro... | 359 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_torch_available():
... | 360 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import j... | 103 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 361 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
f... | 103 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import ... | 362 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 103 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''google/bigbird-roberta-base''': '''https://hugg... | 363 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines... | 103 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCAmelCase = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCAmelCase = [file for file in filepaths if file !... | 364 |
import re
def UpperCamelCase ( snake_case__ : str ) -> str:
if len(re.findall('[ATCG]' , snake_case__ ) ) != len(snake_case__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main... | 103 | 0 |
from collections.abc import Callable
import numpy as np
def UpperCamelCase ( snake_case__ : Callable , snake_case__ : float , snake_case__ : float , snake_case__ : float , snake_case__ : float ) -> np.array:
UpperCamelCase : Optional[int] = int(... | 365 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from trans... | 103 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConf... | 366 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torc... | 103 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation im... | 367 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, sl... | 103 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''google/bit-50''': '''https://huggingface.c... | 368 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCAmelCase_ ( a__ ):
@staticmethod
@abstractmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
raise NotImplementedError()
@abstractmethod
def snake_c... | 103 | 0 |
import numpy as np
def UpperCamelCase ( snake_case__ : str , snake_case__ : Any , snake_case__ : Tuple , snake_case__ : Optional[int] , snake_case__ : Dict ) -> Tuple:
UpperCamelCase : int = int(np.ceil((x_end - xa) / h ) )
UpperCamelC... | 369 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBO... | 103 | 0 |
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_available, is_vision_available... | 370 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase__ : Union[str, Any] = JukeboxTokenizer
UpperCAmelCase__ : Optional[int] = {
... | 103 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''... | 371 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key... | 103 | 0 |
'''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")
... | 104 |
'''simple docstring'''
from timeit import timeit
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
_a = 0
while nu... | 104 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a_ : List[str] ... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
... | 104 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("KT")
a_ : List[str] = TypeVar("VT")
class a ( Generic[KT, VT] ):
def __init__(... | 104 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 104 | 1 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _A (lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :str ) -> List[Any]:
'''simple docstrin... | 104 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a_ : Optional[Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *__magic_name__ ... | 104 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Dict ... | 104 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base impor... | 104 | 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,
convert_to_rgb,
get_resize_output_image_size,
normal... | 104 |
'''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... | 104 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : Any = logging.get_logger(__... | 104 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 1 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
f... | 104 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> int:
_a = [
'safety_checker/pytorch_model.bin... | 104 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 | 1 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ ... | 104 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoCo... | 104 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class a ( nn.Module ):
_lowerCAmelCase = 42
_lowerCAmelCase =... | 104 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 1 |
'''simple docstring'''
from __future__ import annotations
a_ : List[str] = 1_0
def _A (lowerCAmelCase__ :list[int] ) -> list[int]:
'''simple docstring'''
_a = 1
_a = max(lowerCAmelCase__ ... | 104 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUE... | 104 | 1 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def _A (lowerCAmelCase__ :bytes ) -> bytes:
'''simple docstring'''
if len(lowerCAmelCase__ ) != 32:
raise ValueError('Input must be of length 3... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
a_ : Dict = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallo... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : int = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "G... | 104 | 1 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstr... | 104 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CO... | 104 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class a :
def __init__( self , __magic_name__ , __magic_name__ ) -> None:
if len(__magic_name__ ) != degree + 1:
raise ValueError(
... | 104 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a_ : List[str] ... | 104 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ..... | 104 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> float:
'''simple docstring'''
_a = 0
while len(lowerCAmelCase__ ) > 1:
_a = 0
# Consider two files with minimum cost to be merged
... | 104 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, s... | 104 |
'''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_ : Union[str, Any] = logging.get_logger(__... | 104 | 1 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _A (lowerCAmelCase__ :str = "laptop" ) -> DataFrame:
'''simple docstring'''
_a = f'https:/... | 104 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, 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_com... | 104 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any... | 104 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("T")
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
r... | 104 | 1 |
'''simple docstring'''
from __future__ import annotations
class a :
def __init__( self , __magic_name__ , __magic_name__ ) -> int:
_a , _a = text, pattern
_a , _a = len(__magic_name__ ), len(__magic_name__ )
def __Upp... | 104 |
'''simple docstring'''
from timeit import timeit
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
_a = 0
while nu... | 104 | 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,
... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
... | 104 | 1 |
'''simple docstring'''
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 a ... | 104 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 104 | 1 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information' )
for cell_n in range(1 , len(grid[0] ... | 104 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a_ : Optional[Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *__magic_name__ ... | 104 | 1 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
... | 104 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base impor... | 104 | 1 |
'''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/licen... | 104 |
'''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... | 104 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_prop... | 104 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 1 |
'''simple docstring'''
import argparse
import os
import re
a_ : Optional[Any] = "src/transformers"
# Pattern that looks at the indentation in a line.
a_ : List[Any] = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
... | 104 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> int:
_a = [
'safety_checker/pytorch_model.bin... | 104 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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_configurat... | 104 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransforme... | 104 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionMode... | 104 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
a_ : Tuple = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
a_ : Li... | 104 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_av... | 104 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUE... | 104 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CO... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 | 1 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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:/... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : int = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "G... | 104 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
... | 104 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstr... | 104 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ : List[str] = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torc... | 104 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CO... | 104 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class a :
_lowerCAmelCase = None
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_low... | 104 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a_ : List[str] ... | 104 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> float:
'''simple docstring'''
_a = 0
while len(lowerCAmelCase__ ) > 1:
_a = 0
# Consider two files with minimum cost to be merged
... | 104 | 1 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a ( _SCREAMING_SNAKE_CASE ):
... | 104 |
'''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_ : Union[str, Any] = logging.get_logger(__... | 104 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Any = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_visi... | 104 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, 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_com... | 104 | 1 |
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