code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaT... | 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConf... | 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> bool:
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase : List[str] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowercase )
... | 366 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> Li... | 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> list:
if any(not isinstance(_lowercase , _lowercase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(_lowercase ) ):
... | 367 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : ... | 338 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : List[Any] = 9, 1_4 # noqa: F841
UpperCAmelCase : List[Any] = [
... | 368 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 338 | 0 |
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
... | 369 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="... | 338 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=5 ) -> List[Any]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.p... | 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__... | 338 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
def __init__( self , A ) -> None:
UpperCAmelCase : List[Any] = num_of_nodes
UpperCAmelCase : list[list[int]] = []
Uppe... | 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_log... | 338 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_at... | 350 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 338 | 0 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTes... | 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 338 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( __magic_name__ ... | 352 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefi... | 338 | 0 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 353 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int... | 338 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> bool:
return len(set(_lowercase ) ) == len(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 338 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a : List[str] = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfi... | 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100On... | 338 | 0 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
fr... | 356 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be... | 338 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbone... | 357 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , ... | 338 | 0 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class UpperCamelCase_ ( __magic_name__ ):... | 358 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelC... | 338 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : Tuple = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
... | 359 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
... | 338 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extrac... | 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix ... | 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 0 |
'''simple docstring'''
import random
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase = False ) -> dict:
UpperCAmelCase : dict = {i: [] for i in range(_lowercase )}
# if probability is greater or equal than 1, then generate a complete gr... | 362 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_e... | 338 | 0 |
import os
import string
import sys
a : List[Any] = 1 << 8
a : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLAG,
"""right""": 6_7 + ARROW_KEY_FLAG,
"""lef... | 363 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> ... | 338 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgum... | 364 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, An... | 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not i... | 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConf... | 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = 1 , _lowercase = 1_0_0_0 ) -> int:
UpperCAmelCase : List[str] = 1
UpperCAmelCase : Tuple = 0
for divide_by_number in range(_lowercase , digit + 1 ):
Upper... | 366 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> Li... | 338 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 367 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : ... | 338 | 0 |
'''simple docstring'''
from __future__ import annotations
a : Optional[int] = tuple[int, int, int]
a : Optional[int] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
a : Optional[Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZ""... | 368 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 338 | 0 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase_ ( __magic_name__ , unittest.TestCas... | 369 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="... | 338 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTe... | 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__... | 338 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
a : Tuple = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""http... | 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_log... | 338 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> list:
if len(_lowercase ) == 0:
return []
UpperCAmelCase : Union[str, Any] = min(_lowercase ), max(_lowercase )
UpperCAmelCase : str ... | 350 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ... | 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 338 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( __magic_name__ ):
lowercase = ['image_processor', 'feature_extractor']
lowercase = 'TvltImageProcessor'
lowercase = 'TvltFeatureExtractor'
def __init__( s... | 352 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefi... | 338 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.util... | 353 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int... | 338 | 0 |
'''simple docstring'''
import unittest
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase = None , ) -> np.ndarray:
UpperCAmelCase : Dict = np.shape(_lowercase )
UpperCAmelCase : Optional[i... | 354 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 338 | 0 |
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : ... | 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100On... | 338 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 356 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be... | 338 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> ... | 357 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , ... | 338 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_l... | 358 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelC... | 338 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
a : ... | 359 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
... | 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
if not isinstance(_lowercase , _lowercase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
UpperCAmelCase : Optional[Any] = 0
while number:
... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extrac... | 338 | 0 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self , A=None , A=None , A=None , A=None , A=None ) -> Union[str, Any]:
self.set_matricies(red=A , green=A , blue=A , red_edge=A , nir=A )
def _lowercase( self , A=No... | 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCamelCase_ ( __magic_name__ ):
def _lowercase( self , A ) -> int:
with open(A , encoding="""utf-8""" ) as input_file... | 362 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_e... | 338 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a : str = lo... | 363 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> ... | 338 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass... | 364 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, An... | 338 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCamelCase_ ( __magic_name__ ):
@require_torch
def _lowercase( self ) ->... | 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConf... | 338 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import req... | 366 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> Li... | 338 | 0 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __lowerCamelCase ( _lowerc... | 367 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : ... | 338 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 368 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 338 | 0 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __lowerCamelCase ( _lowercase , _lowercase ) -> Tuple:
asser... | 369 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="... | 338 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import On... | 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__... | 338 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class UpperCamelCase_ ( datasets.BeamBasedBuilder ):
def _lowercase( ... | 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_log... | 338 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __magic_name__ ( __lowerCAmelCase : str = "isbn/0140328726" ) -> dict:
__lowerCamelCase = olid.strip().strip('''/''' ) # Remove leading/trailing whitesp... | 339 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ : Any = list[list[int]]
# assigning initial values to the grid
SCREAMING_SNAKE_CASE__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0... | 339 |
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 AutoTokenizer, Fl... | 339 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_... | 339 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 1 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : dict , __lowerCAmelCase : str ) -> set[str]:
__lowerCamelCase , __lowerCamelCase = set(__lowerCAmelCase ), [start]
while stack:
__lowerCamelCase = stack.... | 339 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
... | 339 | 1 |
def __magic_name__ ( __lowerCAmelCase : dict ) -> set:
__lowerCamelCase = set()
# edges = list of graph's edges
__lowerCamelCase = get_edges(__lowerCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_no... | 339 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCAmelCase__ ( __lowercase ... | 339 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 339 | 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 AutoTokenizer, Fl... | 339 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase )... | 339 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_token... | 339 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_im... | 339 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common im... | 339 | 1 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __magic_name__ ( __lowerCAmelCase : List[Any] ) ... | 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 1 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __magic_name__ ( __lowerCAmelCase ... | 339 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 1 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE__ : Tuple = logging.getLogger()
... | 339 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCAmelCase__ ( __lowercase ):
def __A ( self : List[Any] ) -> Optional[int]:
return [
{"col_1": 3, "col_2": "a"},
{"col_1... | 339 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 339 | 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
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 339 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 | 1 |
def __magic_name__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int] ) -> List[Any]:
if index == r... | 339 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE__ : Optional[int] = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
SCREAMING_... | 339 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 339 | 1 |
from ...configuration_utils import PretrainedConfig
class lowerCAmelCase__ ( __lowercase ):
a__ : List[Any] = """bert-generation"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict=5_03_58 , SCREAMING_SNAKE_CASE__ : ... | 339 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 1 |
import math
import unittest
def __magic_name__ ( __lowerCAmelCase : int ) -> bool:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 339 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 1 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=... | 339 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__nam... | 339 | 1 |
def __magic_name__ ( __lowerCAmelCase : str ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCamelCase = sorted(string.lower() )
return len(__lowerCAmel... | 339 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list[int] ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import os
SCREAMING_SNAKE_CASE__ : Optional[int] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def __magic_name__ ( __lowerCAmelCase : str ) -> int:
__lowerCamelCase = 0
__lowerCamelCase = 0
while index < le... | 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():... | 339 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 339 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 1 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.pa... | 339 |
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 AutoTokenizer, Fl... | 339 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
def __A ( self : Dict ) ... | 339 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name_... | 339 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
... | 339 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from t... | 339 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowerCAmelCase__ ( unittest.TestCase ):
def __A... | 339 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 339 | 1 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : int ) -> list[int]:
__lowerCamelCase = [True] * limit
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = True
for i in range(3 , int(l... | 339 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase )... | 339 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase__ ( __lowercase ):
def __A ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : str ) -> Optional[int]:
with open(SCREAM... | 339 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( __lowercase ):
a__ : List[Any] = """ClapFeatureExtractor"""
a__ : int = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( ... | 339 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common im... | 339 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"Yi... | 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 1 |
def __magic_name__ ( __lowerCAmelCase : str ) -> bool:
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def __magic_name__ ( __lowerCAmelCase : str ) -> bool:
__lowerCamelCase ... | 339 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to tak... | 339 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( __lowercase ):
a__ : List[str] = (EulerDiscreteScheduler,)
a__ : Any = 10
def __A ( s... | 339 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 1 |
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 ConfigTester
from... | 339 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 339 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import... | 339 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-... | 339 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 339 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 339 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 1 |
import argparse
from collections import defaultdict
def __magic_name__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[str] ) -> An... | 339 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 1 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
S... | 339 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__nam... | 339 | 1 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list[int] ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSo... | 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():... | 339 | 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
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CAS... | 339 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 1 |
SCREAMING_SNAKE_CASE__ : str = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggi... | 339 |
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 AutoTokenizer, Fl... | 339 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTester... | 339 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 1 |
def __magic_name__ ( __lowerCAmelCase : list ) -> list:
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
__lowerCamelCase = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 339 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
... | 339 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get... | 339 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 1 |
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