code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBer... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase : Optional[Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from b... | 690 |
"""simple docstring"""
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 __a ( self : Unio... | 690 | 1 |
"""simple docstring"""
def __snake_case ( ) -> Dict:
"""simple docstring"""
A = []
A = 1
while len(UpperCamelCase__ ) < 1E6:
constant.append(str(UpperCamelCase__ ) )
i += 1
A = ''.join(UpperCamelCase__ )
... | 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[... | 690 | 1 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={p... | 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase__ :
def __init__( self : Dict , _lowercase : int ):
A = value
A = None
A = None
... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 690 | 1 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCamelCase : List[str] = logging.getLogger(__name__)
... | 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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_param... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCamelCase : Union[str, Any] = {"UserAgent": UserAgent().random}
def __snake_case ( UpperCamelCase__ ) -> dict:
... | 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_... | 690 |
"""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 (
enable_full_deter... | 690 | 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/licenses/LICENSE-2.0... | 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.c... | 690 | 1 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __snake_case ( UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
if not is_accelerate_available():
ret... | 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 1 |
"""simple docstring"""
# 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 Ver... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
... | 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={p... | 690 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import ... | 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ), f'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
A ... | 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> bool:
"""simple docstring"""
if num < 0:
return False
A = num
A = 0
while num > 0:
A = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
... | 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's th... | 690 | 1 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeach... | 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.fl... | 690 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : List[Any] = logging.get_logger(__name__)
UpperCamelCase : Tuple = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/reso... | 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = ... | 690 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : List[Any] ... | 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 690 | 1 |
"""simple docstring"""
import math
from datetime import datetime, timedelta
def __snake_case ( UpperCamelCase__ ) -> datetime:
"""simple docstring"""
A = year % 19
A = year % 4
A = year % 7
A = math.floor(year... | 690 |
"""simple docstring"""
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 __a ( self : Unio... | 690 | 1 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase : Tuple = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def __snake_case ( ) -> Any:
"""simple docstring"""
A = os.path.dirname(os.path.rea... | 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[... | 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Union[str, Any] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Al... | 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_availa... | 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 | 1 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
Uppe... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 690 | 1 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase : str = logging.getLogger(__name__)
class lowerCamelCase__ :
de... | 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 | 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,
normalize,
rescale,
r... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 | 1 |
"""simple docstring"""
def __snake_case ( ) -> Tuple:
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def __snake_case ( UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = 1
A ... | 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : str = logging.get_logger(__name__)
UpperCa... | 690 |
"""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 (
enable_full_deter... | 690 | 1 |
"""simple docstring"""
from math import factorial
def __snake_case ( UpperCamelCase__ = 100 ) -> int:
"""simple docstring"""
return sum(int(UpperCamelCase__ ) for x in str(factorial(UpperCamelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(i... | 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.c... | 690 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
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_backbone_common import BackboneTes... | 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def __snake_case ( UpperCamelCase__ = None ) -> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A = nums[0]
for i in rang... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 690 | 1 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--... | 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={p... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> tuple[int, int]:
"""simple docstring"""
if b == 0:
return (1, 0)
((A) , (A)) = extended_euclid(UpperCamelCase__ , ... | 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Any = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
tr... | 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's th... | 690 | 1 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCamelCase__ (... | 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.fl... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> list[list[int]]:
"""simple docstring"""
A = []
A = []
A = 0
A = sum(UpperCamelCase__... | 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = ... | 690 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention... | 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ... | 690 | 1 |
"""simple docstring"""
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : Optional[Any] ):
# we need a list not a string, so do something to change the type
A = arr.split(',' )
def __a ( self : ... | 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 | 1 |
"""simple docstring"""
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_avai... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 690 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
f... | 690 |
"""simple docstring"""
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 __a ( self : Unio... | 690 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_pro... | 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[... | 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase : List[Any] = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_av... | 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 | 1 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenc... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 690 | 1 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@m... | 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( se... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 | 1 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMode... | 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 1 |
"""simple docstring"""
import numpy as np
UpperCamelCase : List[Any] = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class lowerCamelCase__ :
def __init__( ... | 690 |
"""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 (
enable_full_deter... | 690 | 1 |
"""simple docstring"""
class lowerCamelCase__ :
def __init__( self : Tuple , _lowercase : list ):
A = set_counts
A = max(_lowercase )
A = len(_lowercase )
A = [1] * num_sets
... | 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.c... | 690 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch""", """transformers""", """onnx"""]
def __init__( self : Optional[Any] , *_lowercase : str , ... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 690 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.j... | 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={p... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = 8.988E9 # units = N * m^s * C^-2
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> dict[str, float]:
"""simple d... | 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> list[float]:
"""simple... | 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logg... | 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's th... | 690 | 1 |
"""simple docstring"""
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils impor... | 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.fl... | 690 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = ... | 690 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None ) -> None:
"""simple docstring"""
if start is None:
A = 0
if end is None:
A ... | 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ... | 690 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Dict = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"JukeboxPriorC... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 690 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logg... | 690 |
"""simple docstring"""
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 __a ( self : Unio... | 690 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase : int = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/c... | 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[... | 690 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaF... | 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 | 1 |
"""simple docstring"""
import functools
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = len(UpperCamelCase__ )
A = len(UpperCamelCase__ )
@functools.cache
def min_distance(Upper... | 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 690 | 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
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : Optional[A... | 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = ArgumentParser(
... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Optional[int] = {
"configuration_blenderbo... | 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_senten... | 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mo... | 690 |
"""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 (
enable_full_deter... | 690 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __snake_case ( UpperCamelCase__ ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
A ... | 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.c... | 690 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 1 |
"""simple docstring"""
import math
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
"""simple docstring"""
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial ... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 690 | 1 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
A = int(UpperCamelCase__ )
assert noofclusters < len(Uppe... | 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={p... | 690 | 1 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyaz... | 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 | 1 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[Any] = "T5Config"... | 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase : Any = logging.get_logger(__name__)
UpperCamelCase : Optional[int] = {
"ut/deta": "https://huggingface.co... | 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's th... | 690 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from... | 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.fl... | 690 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/ma... | 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = ... | 690 | 1 |
"""simple docstring"""
import math
class lowerCamelCase__ :
def __a ( self : Union[str, Any] , _lowercase : list[list[float]] , _lowercase : list[int] ):
A = 0.0
A = 0.0
for i in range(len(_lowerca... | 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ... | 690 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusio... | 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 | 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,
)
UpperCamelCase : Dict = {"confi... | 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 690 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 690 |
"""simple docstring"""
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 __a ( self : Unio... | 690 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase : List[Any] = False
class low... | 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[... | 690 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.uti... | 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 | 1 |
"""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 __snake_case ( UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
... | 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 | 1 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 690 | 1 |
"""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 import (
... | 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 | 1 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> np.ndarray:
"""simple docstring"""
A = math.sqrt(UpperCamelCase__ )
A = 1 / (sigma * math.sqrt... | 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
UpperCamelCase : Optional[Any] = "docs/source/en/_toctree.yml"
def __snake_case ( UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
A = defaul... | 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 1 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCamelCase : str = "src/transformers"
# This is to make sure the t... | 690 |
"""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 (
enable_full_deter... | 690 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : List[Any] = logging.get_logger(__name__)
UpperCamelCase : int = ... | 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.c... | 690 | 1 |
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