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 |
|---|---|---|---|---|
def A (__A : int ) -> str:
"""simple docstring"""
if isinstance(__A , __A ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(__A , __A ):
raise TypeError('''\'str... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a ):
UpperCAmelCa... | 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 __sna... | 7 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiT... | 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,
StableDiffusionPano... | 7 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case_ : Dict = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, ... | 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 impor... | 7 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ... | 7 | 1 |
import string
import numpy
def A (__A : int , __A : int ) -> int:
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , __A )
class __snake_case :
UpperCAmelCase__ : ... | 7 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvail... | 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 A... | 7 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
... | 7 | 1 |
from collections import deque
from math import floor
from random import random
from time import time
class __snake_case :
def __init__( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ = {}
def ... | 7 |
from timeit import timeit
def A (__A : int ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('''the value of input must not be negative''' )
UpperCAmelCase_ = 0
while number:
number ... | 7 | 1 |
snake_case_ : int = {str(digit): digit**5 for digit in range(10)}
def A (__A : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__A ) )
def A () -> int:
... | 7 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Dict):
"""simple... | 7 | 1 |
from __future__ import annotations
def A (__A : list[list[int]] ) -> bool:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
# We need to create solution object to save path.
UpperCAmelCase_ = [[0 for _ ... | 7 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 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 TFModelTesterM... | 7 |
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 ..mod... | 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_... | 7 |
import sys
def A (__A : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
UpperCAmelCase_ = [[0 for x in ra... | 7 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __snake_case ( pl.LightningModule ):
def __init__( self : str , _snake_case : ... | 7 |
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_docstring... | 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_... | 7 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __sn... | 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,
... | 7 |
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 trans... | 7 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __snake_case :
def __init__( self : Union[str, Any] , _snake_case : list[tuple[float, float]]):
"""simple docstring"""
UpperCAmelCase_ ... | 7 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case_ : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case_ : str = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __snake_case :
UpperCAmelCa... | 7 | 1 |
def A (__A : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
UpperCAmelCase_ = 4
UpperCAmelCase_ = (... | 7 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
class __snake_case :
... | 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
snake_case_ : Opt... | 7 |
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
@require_... | 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,
B... | 7 |
from maths.prime_factors import prime_factors
def A (__A : int ) -> int:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
... | 7 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case_ : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case_ : str = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __snake_case :
UpperCAmelCa... | 7 |
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
... | 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():
i... | 7 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A (__A : BertModel , __A : str , __A : str ) -> int:
"""simple docstring"""
UpperC... | 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
snake_case_ : Optional[Any] = False
class __snake_case... | 7 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_commo... | 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
snake_case_ : Dict = logging.get_logger(__name__)
snake_case_ : int = {"voca... | 7 |
import comet # From: unbabel-comet
import torch
import datasets
snake_case_ : Tuple = datasets.logging.get_logger(__name__)
snake_case_ : str = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n tit... | 7 | 1 |
def A (__A : str ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a ):
UpperCAmelCa... | 7 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
snake_case_ : Union[str, Any] = 2048
snake_case_ : str = 4096
snake_case_ : Union[str, Any] = 42
snake_case_ : List[str] = os.environ.pop("PROCESS_TRAIN", "false")
snake_case_ : Tuple = {"... | 7 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiT... | 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 s... | 7 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case_ : Dict = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, ... | 7 | 1 |
from ...configuration_utils import PretrainedConfig
snake_case_ : Optional[Any] = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.... | 7 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ... | 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 __snake_case ... | 7 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvail... | 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
snake_case_ : List[Any] = Mapping[str, np.ndarray]
snake_case_ : Any = Mapping[str, Any] # Is a nested ... | 7 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
... | 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
i... | 7 |
from timeit import timeit
def A (__A : int ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('''the value of input must not be negative''' )
UpperCAmelCase_ = 0
while number:
number ... | 7 | 1 |
def A (__A : int , __A : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def A () -> None:
"""simple docstring"""
assert nand_gate(0 , ... | 7 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Dict):
"""simple... | 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... | 7 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 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 TFModelTesterMi... | 7 |
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 ..mod... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiT... | 7 |
import sys
def A (__A : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
UpperCAmelCase_ = [[0 for x in ra... | 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_ind... | 7 |
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_docstring... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : Optional[Any] = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 7 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __sn... | 7 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a ):
UpperCAmelCase__ : int = (KDPMaDiscreteScheduler,)
UpperCAmelCase__ : List[str]... | 7 |
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 trans... | 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,
)
snake_case_ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('''... | 7 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case_ : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case_ : str = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __snake_case :
UpperCAmelCa... | 7 | 1 |
from timeit import timeit
def A (__A : int ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('''the value of input must not be negative''' )
UpperCAmelCase_ = 0
while number:
number ... | 7 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
class __snake_case :
... | 7 | 1 |
import argparse
import json
import subprocess
def A (__A : List[str] , __A : Dict ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = (
F"""curl -H \... | 7 |
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
@require_... | 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
fro... | 7 |
from maths.prime_factors import prime_factors
def A (__A : int ) -> int:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
... | 7 | 1 |
from __future__ import annotations
import requests
snake_case_ : Any = 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 crea... | 7 |
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
... | 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 P... | 7 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A (__A : BertModel , __A : str , __A : str ) -> int:
"""simple docstring"""
UpperC... | 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_... | 7 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_commo... | 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_docstring... | 7 |
import comet # From: unbabel-comet
import torch
import datasets
snake_case_ : Tuple = datasets.logging.get_logger(__name__)
snake_case_ : str = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n tit... | 7 | 1 |
snake_case_ : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def A (__A : str ) -> int:
"""simple docstring"""
UpperCAmelCase_ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a ):
UpperCAmelCa... | 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... | 7 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiT... | 7 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def A (__A : List[str] , __A : int , __A : Optional[Any] , __A : int=1024 ) -> List[str]:
... | 7 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case_ : Dict = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, ... | 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
snake_case_ : List[str] = datasets... | 7 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ... | 7 | 1 |
def A (__A : dict ) -> set:
"""simple docstring"""
UpperCAmelCase_ = set()
# edges = list of graph's edges
UpperCAmelCase_ = get_edges(__A )
# While there are still elements in edges list, take an arbi... | 7 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvail... | 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
#
# Unles... | 7 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
... | 7 | 1 |
def A (__A : int | float | str ) -> tuple[int, int]:
"""simple docstring"""
try:
UpperCAmelCase_ = float(__A )
except ValueError:
raise ValueError('''Please enter a valid number''' )
UpperCAmelCase_ ... | 7 |
from timeit import timeit
def A (__A : int ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('''the value of input must not be negative''' )
UpperCAmelCase_ = 0
while number:
number ... | 7 | 1 |
def A (__A : int = 600851475143 ) -> int:
"""simple docstring"""
try:
UpperCAmelCase_ = int(__A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n... | 7 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Dict):
"""simple... | 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,
)
snake_case_ : Any = {
"configuration_xlm_... | 7 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 7 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
snake_case_ : List[Any] = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_c... | 7 |
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 ..mod... | 7 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A (*__A : List[str] , __A : Optional[Union[Dict, Any]] = None , __A : str=True , __A : str=2 ) -> str:
... | 7 |
import sys
def A (__A : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
UpperCAmelCase_ = [[0 for x in ra... | 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_t... | 7 |
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_docstring... | 7 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
snake_case_ : Optional[Any] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
snake_case_ : Tuple ... | 7 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __sn... | 7 | 1 |
from collections import deque
class __snake_case :
def __init__( self : Tuple , _snake_case : str , _snake_case : int , _snake_case : int):
"""simple docstring"""
UpperCAmelCase_ = process_na... | 7 |
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 trans... | 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_... | 7 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case_ : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case_ : str = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __snake_case :
UpperCAmelCa... | 7 | 1 |
from __future__ import annotations
import time
snake_case_ : Union[str, Any] = list[tuple[int, int]]
snake_case_ : Dict = [
[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],
... | 7 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
class __snake_case :
... | 7 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A () -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = ArgumentP... | 7 |
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
@require_... | 7 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
snake_case_ : int = logging.get_logger(__name__)
class __snake_case ( a ):
def __init__( self : Any , *_snake_case : Union[str, An... | 7 |
from maths.prime_factors import prime_factors
def A (__A : int ) -> int:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
... | 7 | 1 |
def A (__A : int = 10**12 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = 1
UpperCAmelCase_ = 0
UpperCAmelCase_ = 1
UpperCAmelCase_ = 1
while numerator <= 2 * m... | 7 |
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
... | 7 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def A (__A : str , __A : str = "cpu" , __A : Union[str, None] = None ) -> None:
"""simple docstring"""
UpperCAmelCase_ = ... | 7 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A (__A : BertModel , __A : str , __A : str ) -> int:
"""simple docstring"""
UpperC... | 7 | 1 |
def A (__A : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__A ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod... | 7 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_commo... | 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 .tokenizati... | 7 |
import comet # From: unbabel-comet
import torch
import datasets
snake_case_ : Tuple = datasets.logging.get_logger(__name__)
snake_case_ : str = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n tit... | 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 i... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a ):
UpperCAmelCa... | 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_comm... | 7 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiT... | 7 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A (__A : str ) -> List[Any]:
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 7 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case_ : Dict = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, ... | 7 | 1 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
snake_case_ : str = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A (__A ... | 7 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ... | 7 | 1 |
from math import pow, sqrt
def A (*__A : float ) -> bool:
"""simple docstring"""
UpperCAmelCase_ = len(__A ) > 0 and all(value > 0.0 for value in values )
return result
def A (__A : float , _... | 7 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvail... | 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 A (__A : List[str] ) -> Any:
... | 7 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
... | 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
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
class __snake_case :
... | 7 |
from timeit import timeit
def A (__A : int ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('''the value of input must not be negative''' )
UpperCAmelCase_ = 0
while number:
number ... | 7 | 1 |
import math
def A (__A : list , __A : int = 0 , __A : int = 0 ) -> list:
"""simple docstring"""
UpperCAmelCase_ = end or len(__A )
for i in range(__A , __A ):
UpperCAmelCa... | 7 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Dict):
"""simple... | 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_ind... | 7 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 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_s... | 7 |
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 ..mod... | 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.huggin... | 7 |
import sys
def A (__A : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
UpperCAmelCase_ = [[0 for x in ra... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Optional[int] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 7 |
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_docstring... | 7 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : List[Any] = {
"t5-small": "https://huggingface.co/t5-small/... | 7 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __sn... | 7 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A (__A : BertModel , __A : str , __A : str ) -> int:
"""simple docstring"""
UpperC... | 7 |
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 trans... | 7 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def A (__A : bool = True , *__A : Optional[Any] , **__A : Any ) -> Union[str, Any]:
... | 7 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case_ : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case_ : str = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __snake_case :
UpperCAmelCa... | 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
snake_case_ : List[... | 7 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
class __snake_case :
... | 7 | 1 |
def A (__A : list ) -> float:
"""simple docstring"""
UpperCAmelCase_ = 0
while len(__A ) > 1:
UpperCAmelCase_ = 0
# Consider two files with minimum cost to be merged
for _ in range(2... | 7 |
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
@require_... | 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 __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Dict):
"""simple... | 7 |
from maths.prime_factors import prime_factors
def A (__A : int ) -> int:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
... | 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 __snake_case ( a ):
UpperCAme... | 7 |
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
... | 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 ..mod... | 7 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A (__A : BertModel , __A : str , __A : str ) -> int:
"""simple docstring"""
UpperC... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : int = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerCo... | 7 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_commo... | 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 ... | 7 |
import comet # From: unbabel-comet
import torch
import datasets
snake_case_ : Tuple = datasets.logging.get_logger(__name__)
snake_case_ : str = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n tit... | 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_... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a ):
UpperCAmelCa... | 7 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __snake_case ( a ):
UpperCAmelCase__ : Optional[int] = '''M-CLIP'''
def __init__( self : Any , _snake_case : List[Any]=1024 , _s... | 7 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiT... | 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,
MobileViTImageProces... | 7 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case_ : Dict = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, ... | 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, Patch... | 7 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ... | 7 | 1 |
import sys
def A (__A : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
UpperCAmelCase_ = [[0 for x in ra... | 7 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvail... | 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 req... | 7 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
... | 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
... | 7 |
from timeit import timeit
def A (__A : int ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('''the value of input must not be negative''' )
UpperCAmelCase_ = 0
while number:
number ... | 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 trans... | 7 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Dict):
"""simple... | 7 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : Optional[int] = {
"vocab_file": "vocab.json",
"... | 7 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 7 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processo... | 7 |
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 ..mod... | 7 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def A (__A : Namespace ) -> Dict:
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_c... | 7 |
import sys
def A (__A : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
UpperCAmelCase_ = [[0 for x in ra... | 7 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def A (__A : np.ndarray , __A : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2 ) fo... | 7 |
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_docstring... | 7 | 1 |
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