code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> str:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstan... | 7 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 7 | 1 |
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 A ( _Uppe... | 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
... | 7 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPi... | 7 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _snake_case( SCREAMING_SNAKE... | 7 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import Vi... | 7 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 Toke... | 7 | 1 |
import string
import numpy
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , SCREAMING_SNAKE_CASE__ )
class A ... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> float:
'''simple docstring'''
A__ ... | 7 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import Auto... | 7 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = "cpu" , SCREAMING_SNAKE_CASE__ : Union[str, None] = None ) -> None:
'''simple docstring'''
... | 7 | 1 |
from collections import deque
from math import floor
from random import random
from time import time
class A :
"""simple docstring"""
def __init__( self : List[Any] )-> Optional[int]:
'''simple docstring'''
A__ ... | 7 |
import os
# Precomputes a list of the 100 first triangular numbers
lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _snake_case( ) -> int:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__... | 7 | 1 |
lowercase_ = {str(digit): digit**5 for digit in range(10)}
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(SCREAMING_SNAKE_CASE__ ) )
def _snake_... | 7 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 | 1 |
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> bool:
'''simple docstring'''
A__ = len(SCREAMING_SNAKE_CASE__ )
# We need to create solution object to save path.
A__ = [... | 7 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowercase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _snake_case( SCREAMING_SNAKE_CA... | 7 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple... | 7 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timest... | 7 |
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : Tuple,lowercase_ : Any,lowercase_ : List[str] )-> List[Any]:
'''simple docstring'''
A__ = name
A__ = ... | 7 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Dict,lowercase_ ... | 7 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils ... | 7 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json... | 7 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
Juma... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any ) -> Optional[int]:
'''simple docstring'''
global f # a global dp tabl... | 7 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A :
"""simple docstring"""
def __init__( self : Tuple,lowercase_ : list[tuple[float, float]] )-> Tuple:
'''simple docstring'''
... | 7 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("fixtures/spiece.model")
... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
A__ = 4
A__ = (1 << p) - 1
... | 7 |
from typing import Dict
from .base import GenericTensor, Pipeline
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : int,lowercase_ : Dict=None,lowercase_ : Tuple=None,lowercase_ : List[Any]=None,... | 7 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowercase_ = {
... | 7 |
from timeit import timeit
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
A__ = 0
while number:
number &=... | 7 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTok... | 7 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _snake_case( SCREAMING_SNAKE_CASE__ : Any ) -> ... | 7 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring"""
low... | 7 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 7 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
impor... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
A__ = 0
A__ = len(SCREAMING_SNAKE_CASE__ ) - 1
while left <= right:
... | 7 | 1 |
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
lowercase_ = False
class A ( unittest.TestCase ):
... | 7 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from da... | 7 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"vocab_file": "spiece.mo... | 7 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import Confi... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> Dict:
'''simple docstring'''
A__ = []
A__ = []
A__ = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
}... | 7 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 7 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowercase_ = 2048
lowercase_ = 4096
lowercase_ = 42
lowercase_ = os.environ.pop("PROCESS_TRAIN", "false")
lowercase_ = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def _snak... | 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
... | 7 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 7 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _snake_case( SCREAMING_SNAKE... | 7 | 1 |
from ...configuration_utils import PretrainedConfig
lowercase_ = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-f... | 7 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 Toke... | 7 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( _UpperCAmelCase ):... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> float:
'''simple docstring'''
A__ ... | 7 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowercase_ = Mapping[str, np.ndarray]
lowercase_ = Mapping[str, Any] # Is a nested dict.
lowercase_ ... | 7 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = "cpu" , SCREAMING_SNAKE_CASE__ : Union[str, None] = None ) -> None:
'''simple docstring'''
... | 7 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_... | 7 |
import os
# Precomputes a list of the 100 first triangular numbers
lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _snake_case( ) -> int:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _snake_case( ) -> None:
'''simple docstring... | 7 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_se... | 7 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowercase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _snake_case( SCREAMING_SNAKE_CA... | 7 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, i... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
t... | 7 |
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : Tuple,lowercase_ : Any,lowercase_ : List[str] )-> List[Any]:
'''simple docstring'''
A__ = name
A__ = ... | 7 | 1 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "Conv... | 7 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json... | 7 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = (KDPMaDiscreteScheduler,)
lo... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any ) -> Optional[int]:
'''simple docstring'''
global f # a global dp tabl... | 7 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ = pytest.mark.integration
@pytest.mark.parametrize('path' , ['paws', 'c... | 7 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("fixtures/spiece.model")
... | 7 | 1 |
from timeit import timeit
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
A__ = 0
while number:
number &=... | 7 |
from typing import Dict
from .base import GenericTensor, Pipeline
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : int,lowercase_ : Dict=None,lowercase_ : Tuple=None,lowercase_ : List[Any]=None,... | 7 | 1 |
import argparse
import json
import subprocess
def _snake_case( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str ) -> List[Any]:
'''simple docstring'''
A__ = []
A__ = (
f'curl -H "Accept: applicatio... | 7 |
from timeit import timeit
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
A__ = 0
while number:
number &=... | 7 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...t... | 7 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _snake_case( SCREAMING_SNAKE_CASE__ : Any ) -> ... | 7 | 1 |
from __future__ import annotations
import requests
lowercase_ = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc down... | 7 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 7 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImag... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
A__ = 0
A__ = len(SCREAMING_SNAKE_CASE__ ) - 1
while left <= right:
... | 7 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from da... | 7 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_docstrings.py
... | 7 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import Confi... | 7 | 1 |
lowercase_ = "Alexander Joslin"
import operator as op
from .stack import Stack
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> int:
'''simple docstring'''
A__ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
A_... | 7 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 7 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql ... | 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
... | 7 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _snake_case( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ ... | 7 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _snake_case( SCREAMING_SNAKE... | 7 | 1 |
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
lowercase_ = datasets.utils.logging.get... | 7 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 Toke... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : dict ) -> set:
'''simple docstring'''
A__ = set()
# edges = list of graph's edges
A__ = get_edges(SCREAMING_SNAKE_CASE__ )
# While there are still elements in edges list, ... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> float:
'''simple docstring'''
A__ ... | 7 | 1 |
# 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
#
# Unless requ... | 7 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = "cpu" , SCREAMING_SNAKE_CASE__ : Union[str, None] = None ) -> None:
'''simple docstring'''
... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int | float | str ) -> tuple[int, int]:
'''simple docstring'''
try:
A__ = float(SCREAMING_SNAKE_CASE__ )
except ValueError:
raise ValueError('Please enter a valid number' ... | 7 |
import os
# Precomputes a list of the 100 first triangular numbers
lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _snake_case( ) -> int:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 600851475143 ) -> int:
'''simple docstring'''
try:
A__ = int(SCREAMING_SNAKE_CASE__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable... | 7 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 | 1 |
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,
)
lowercase_ = {
"configuration_xlm_roberta": [
... | 7 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowercase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _snake_case( SCREAMING_SNAKE_CA... | 7 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowercase_ = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1000... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple... | 7 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case( *SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Union[Dict, Any]] = None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREA... | 7 |
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : Tuple,lowercase_ : Any,lowercase_ : List[str] )-> List[Any]:
'''simple docstring'''
A__ = name
A__ = ... | 7 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_au... | 7 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowercase_ = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowercase_ = requests.get(url, heade... | 7 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json... | 7 | 1 |
from collections import deque
class A :
"""simple docstring"""
def __init__( self : str,lowercase_ : str,lowercase_ : int,lowercase_ : int )-> None:
'''simple docstring'''
A__ = process... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any ) -> Optional[int]:
'''simple docstring'''
global f # a global dp tabl... | 7 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
... | 7 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("fixtures/spiece.model")
... | 7 | 1 |
from __future__ import annotations
import time
lowercase_ = list[tuple[int, int]]
lowercase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0... | 7 |
from typing import Dict
from .base import GenericTensor, Pipeline
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : int,lowercase_ : Dict=None,lowercase_ : Tuple=None,lowercase_ : List[Any]=None,... | 7 | 1 |
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(
... | 7 |
from timeit import timeit
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
A__ = 0
while number:
number &=... | 7 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase_ = logging.get_logger(__name__)
class A ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Any,*lowercase_ : ... | 7 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _snake_case( SCREAMING_SNAKE_CASE__ : Any ) -> ... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 10**12 ) -> int:
'''simple docstring'''
A__ = 1
A__ = 0
A__ = 1
A__ = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * ... | 7 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 7 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = "cpu" , SCREAMING_SNAKE_CASE__ : Union[str, None] = None ) -> None:
'''simple docstring'''
... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
A__ = 0
A__ = len(SCREAMING_SNAKE_CASE__ ) - 1
while left <= right:
... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(SCREAMING_SNAKE_CASE__ ) )
if txt[a].isalpha()
]
if __name__ =... | 7 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from da... | 7 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_bi... | 7 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import Confi... | 7 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 7 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 7 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common imp... | 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
... | 7 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
if "img_en... | 7 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _snake_case( SCREAMING_SNAKE... | 7 | 1 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowercase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _snake_case( SCREAMING_SNAKE_CA... | 7 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 Toke... | 7 | 1 |
from math import pow, sqrt
def _snake_case( *SCREAMING_SNAKE_CASE__ : float ) -> bool:
'''simple docstring'''
A__ = len(SCREAMING_SNAKE_CASE__ ) > 0 and all(value > 0.0 for value in values )
return result
def _snake_case( ... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> float:
'''simple docstring'''
A__ ... | 7 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _snake_case( SCREAMING_SNAKE_CASE__ : Any ) -> ... | 7 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = "cpu" , SCREAMING_SNAKE_CASE__ : Union[str, None] = None ) -> None:
'''simple docstring'''
... | 7 | 1 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowercase_ = logging.get_logger(__name__)
class A :
"""simple docstring"""
... | 7 |
import os
# Precomputes a list of the 100 first triangular numbers
lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _snake_case( ) -> int:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__... | 7 | 1 |
import math
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : int = 0 , SCREAMING_SNAKE_CASE__ : int = 0 ) -> list:
'''simple docstring'''
A__ = end or len(SCREAMING_SNAKE_CASE__ )
for i in range(S... | 7 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 | 1 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowercase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _snake_case( SCREAMING_SNAKE_CA... | 7 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softm... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple... | 7 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingfac... | 7 |
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : Tuple,lowercase_ : Any,lowercase_ : List[str] )-> List[Any]:
'''simple docstring'''
A__ = name
A__ = ... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:... | 7 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json... | 7 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json... | 7 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _snake_case( SCREAMING_SNAKE_CASE__ : BertModel , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ) -> List[Any]:
'''... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any ) -> Optional[int]:
'''simple docstring'''
global f # a global dp tabl... | 7 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case( SCREAMING_SNAKE_CASE__ : bool = True , *SCREAMING_SNAKE_CASE__ : List[str] , **SCREAMING_SNAKE_CASE__ : Union[str, Any] ) ... | 7 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("fixtures/spiece.model")
... | 7 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
lowercase_ = {
... | 7 |
from typing import Dict
from .base import GenericTensor, Pipeline
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : int,lowercase_ : Dict=None,lowercase_ : Tuple=None,lowercase_ : List[Any]=None,... | 7 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> float:
'''simple docstring'''
A__ = 0
while len(SCREAMING_SNAKE_CASE__ ) > 1:
A__ = 0
# Consider two files with minimum cost to be merged
for _ in... | 7 |
from timeit import timeit
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
A__ = 0
while number:
number &=... | 7 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : Any )-> ... | 7 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _snake_case( SCREAMING_SNAKE_CASE__ : Any ) -> ... | 7 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( _UpperCAmelCase ):
"""sim... | 7 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 7 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
A__ = 0
A__ = len(SCREAMING_SNAKE_CASE__ ) - 1
while left <= right:
... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
... | 7 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from da... | 7 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import... | 7 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import Confi... | 7 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 7 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 7 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'M-CLIP'
def __init__( self : Optional[int],lowercase_ : Union[str, Any]... | 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
... | 7 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
... | 7 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _snake_case( SCREAMING_SNAKE... | 7 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSp... | 7 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 Toke... | 7 | 1 |
import sys
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
A__ = len(SCREAMING_SNAKE_CASE__ )
A__ = [[0 for x in range(SCREAMING_SNAKE_CASE__ )] for x in range(SCREAM... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> float:
'''simple docstring'''
A__ ... | 7 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 7 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = "cpu" , SCREAMING_SNAKE_CASE__ : Union[str, None] = None ) -> None:
'''simple docstring'''
... | 7 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_... | 7 |
import os
# Precomputes a list of the 100 first triangular numbers
lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _snake_case( ) -> int:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__... | 7 | 1 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transformer... | 7 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "tokenizer... | 7 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowercase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _snake_case( SCREAMING_SNAKE_CA... | 7 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor impo... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple... | 7 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _snake_case( SCREAMING_SNAKE_CASE__ : Namespace ) -> str:
'''simple docstring'''
return ConvertCommand(
args.model_type ... | 7 |
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : Tuple,lowercase_ : Any,lowercase_ : List[str] )-> List[Any]:
'''simple docstring'''
A__ = name
A__ = ... | 7 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray ) -> float:
'''simple docstring'''
return math.sqrt(sum... | 7 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 | 1 |
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