code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : int = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all V... | 8 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__A : Any = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': ... | 8 |
'''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
@... | 8 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_av... | 8 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 1 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCAmelCase ( lowerCamelCase_ :Dict , lowerCamelCase_ :List[Any]=7 ):
'''simple docstring'''
snake_case_ : Dict ... | 8 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :int , lowerCamelCase_ :Any=None , **lowerCamelCase_ :List[Any] ):
'''simple docstring'''
snake_case_ : ... | 8 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 1 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def UpperCAmelCase ( *lowerCamelCase_ :Dict ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
snake_case_ ... | 8 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 1 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_lo... | 8 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logge... | 8 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 1 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int = 10_00 ):
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = 1, 1
snake_case_ : Union[str, Any] = []
for i in range(1 , n + 1 ):
snake_case_ : ... | 8 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Dict = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_... | 8 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 1 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgument... | 8 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
return bool(re.search(lowe... | 8 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
lowercase : int
lowercase : int
class __UpperCamelCase :
def... | 8 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 1 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __UpperCamelCase :
@property
def a__ ... | 8 |
'''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, UNetaDConditionModel
from diffusers.ut... | 8 | 1 |
'''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 transformers.testing_utils im... | 8 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVec... | 8 |
'''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
i... | 8 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixi... | 8 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : List[str] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class __UpperC... | 8 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 1 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num... | 8 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 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,
)
__A : List[str] = {'configuration_opt': ['OPT_PRETRAINED_CONFIG... | 8 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Optional[Any] = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all... | 8 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 1 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __UpperCamelCase ( lowerc... | 8 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCamelCase ( lowercase__ ):
def a__ ( self :List[Any] ,_UpperCamelCase :float ):
... | 8 |
'''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
@... | 8 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def UpperCAmelCase ( lowerCamelCase_ :List[str... | 8 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiff... | 8 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 1 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nn... | 8 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def UpperCAmelCase ( lowerCamelCase_ :list , lowerCamelCase_ :list , lowerCamelCase_ :list , lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : Optional[Any] = ... | 8 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase ( lowerCamelCase_ :list[int] ): # This function is recursive
'''simple docstring'''
snake_case_ : Tuple = len(lowerCamelCase_ )
# If the array contains only one element, we return... | 8 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_... | 8 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logge... | 8 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__A : str = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument(... | 8 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : List[str] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 8 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 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,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A... | 8 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
return bool(re.search(lowe... | 8 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCamelCase ( lowercase__ , unittest.T... | 8 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common ... | 8 |
'''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, UNetaDConditionModel
from diffusers.ut... | 8 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Tuple = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
... | 8 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
__A : Union[str, Any] = tuple[int, int]
class __UpperCamelCase :
def __init__( self :Dict ,_UpperCamelCase :set[int] ,_UpperCamelCase :Mapping[EdgeT,... | 8 |
'''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
i... | 8 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : List[str] = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-... | 8 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''simple docstring'''
__A : List[Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A : Union[str, Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCAmelCase ( lowerCamelCase_ :dict[int, list[int]] , lowerCamelCase_ :int , lowerC... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : List[... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list[int] , lowerCamelCase_ :list[int] , lowerCamelCase_ :int ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__lowerCAmelCase ... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def UpperCAmelCase ( lowerCamelCase_ :Callable , lowerCamelCase_ :float , lowerCamelCase_ :float , lowerCamelCase_ :float , lowerCamelCase_ :float ):
'''simple docstring'''
sn... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase ( lowerCamelCase_ :list[int] , lowerCamelCase_ :int , lowerCamelCase_ :int , lowerCamelCase_ :int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
... | 355 |
'''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
@... | 8 | 0 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def UpperCAmelCase ( lowerCamelCase_ :Optional[int] , lowerCamelCase_ :Dict , lowerCamelCase_ :... | 356 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 0 |
'''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 : Union[str, Any] = logging.get_logger(__name__)
__A... | 357 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : str = {
"""roberta-base""": ""... | 358 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__A : Tuple = HfArgumentParser(InitializationArguments)
__A : Optional[int] = parser.parse_args()
# Load codeparrot tokenizer traine... | 359 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__... | 360 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 0 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__A : List[str] = ''
__A : Optional[Any] = ''
__A : int = ''
__A : List[str] = ''
def UpperCAmelCase ( lowerCamelCase_ :Optional[Any] ):
'''simple do... | 361 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logge... | 8 | 0 |
'''simple docstring'''
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
__A : Any = logging.... | 362 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 0 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__A : An... | 363 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 0 |
'''simple docstring'''
import warnings
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 Tens... | 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=lowercase__ ):
lowercase : Optional[int] = ['''keras_nlp''']
def __init__( self :Tuple ,*_UpperCamelCase :str ,... | 366 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
return bool(re.search(lowe... | 8 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class __UpperCamelCase ( _UpperCamelCase ):
lowercase : str ... | 367 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was p... | 368 |
'''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, UNetaDConditionModel
from diffusers.ut... | 8 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__A : str = logging.getLogger(__name__)
class __UpperCamelCase ( __UpperCamelCase ):
def... | 369 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int , lowerCamelCase_ :int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(UpperCAmelCase__ ... | 370 |
'''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
i... | 8 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 371 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 0 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def ... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPM... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.sta... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''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
... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__A : List[Any] = logging.get_logger(__name__)
class __UpperCamelCase ( _UpperCAmelCase ):
def __init__( self :int ,*_UpperCamelCase ... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 0 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( lowerCamelCase_ :Optional[Any] ):
'''simple docstring'''
snake_case_ : Any = a... | 355 |
'''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
@... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :Any ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def UpperCAmelCase ( lowerCamelCase_ :Dict , lowerCamelCase_ :Op... | 356 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 0 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSamp... | 357 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
class __UpperCamelCase :
def __init__( self :Dict ,_UpperCamelCase :list[list[int]] ):
snake_case_ : Optional[int] = TypeError(
"""Matrices must be formed from a list of zero... | 358 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 0 |
import datasets
from .evaluate import evaluate
__A : Union[str, Any] = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:210... | 359 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 0 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : int = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Optiona... | 360 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( __snake_case ):
lowercase : Optional[int] = (PNDMScheduler,)
lowercase : Optional[int] ... | 361 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logge... | 8 | 0 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__A : Any = 'src/transformers'
__A : List[s... | 362 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTes... | 363 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffuser... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : Tuple = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4)) | 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : str = {
'configuration_vision_encoder_decoder': ['VisionEnco... | 366 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
return bool(re.search(lowe... | 8 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__A : Optional[Any] = False
class __UpperCamelCase... | 367 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 368 |
'''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, UNetaDConditionModel
from diffusers.ut... | 8 | 0 |
'''simple docstring'''
from math import pow
def UpperCAmelCase ( lowerCamelCase_ :int , lowerCamelCase_ :int , lowerCamelCase_ :int , lowerCamelCase_ :int , lowerCamelCase_ :int , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum ... | 369 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 0 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common im... | 370 |
'''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
i... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :Dict ):
'''simple docstring'''
snake_case_ : Dict = [int(__lowerCAmelCase ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(__lowerCAmelCase ) == 4 and all(0 <= int(__lowerCAmelCase ) ... | 371 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, 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_model... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''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
i... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 0 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
__A : List[Any] = '1'
__A : Optional[int] = '0'
__A : int = '1'
__A : List[Any] = ort.SessionOptions()
__A : Dict = ort.GraphOptimizationLevel.ORT_... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Tuple , lowerCamelCase_ :Tuple , lowerCamelCase_ ... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : List[Any] = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm'... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :Tuple , lowerCamelCase_ :Any ):
'''simple docstring'''
snake_case_ : Union[str, Any] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def UpperCAme... | 355 |
'''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
@... | 8 | 0 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def UpperCAmelCase ( lowerCamelCase_ :Dict ):
'''simple docstring'''
def wrapper(*lowerCamelCase_ :Dict ... | 356 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : Optional[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'token... | 357 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 0 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __UpperCamelCase ( pl.LightningModule ):
def __init__( self :str ,_UpperCamelCase :Opt... | 358 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCAmelCase ( lowerCamelCase_ :int = 8 ):
'''simple docstring'''
snake_case_ : Optional[Any] = ascii_letters + digits + punctuat... | 359 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 0 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__A : Any = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--... | 360 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__A : Dict = True
except (ImportError, ModuleNotFoundError):
__A : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def ... | 361 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logge... | 8 | 0 |
'''simple docstring'''
from math import sqrt
def UpperCAmelCase ( lowerCamelCase_ :Optional[int] ):
'''simple docstring'''
snake_case_ : Any = 0
for i in range(1 , int(sqrt(lowercase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase__ ):
total ... | 362 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 0 |
'''simple docstring'''
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_confi... | 363 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import P... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( lowerCamelCase_ :str , lowerCamelCase_ :Dict ):
'''simple docstring'''
_validate_point(SCREAMING_SNAKE_CASE_ )
_validate_point(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise Value... | 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 0 |
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