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 warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead."""
)
| 338 |
'''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 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fr... | 338 |
'''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 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbone... | 338 |
'''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 | 1 |
'''simple docstring'''
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 +... | 338 |
'''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 | 1 |
'''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 ) ->... | 338 |
'''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 | 1 |
'''simple docstring'''
from math import factorial
class UpperCamelCase_ :
def __init__( self , A , A ) -> Optional[Any]:
UpperCAmelCase : Dict = real
if isinstance(A , A ):
UpperCAmelCase : Optional[Any] = [1] *... | 338 |
'''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 | 1 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = JukeboxTokenizer
lowercase = {
'artist': 'Zac Brown Band',
'genres... | 338 |
'''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 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline... | 338 |
'''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 | 1 |
'''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... | 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Optional[Any] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try... | 338 |
'''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 | 1 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lowerCamelCase ( _lowerc... | 338 |
'''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 | 1 |
'''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""... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
return 1 if input_a == input_a else 0
def __lowerCamelCase ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 ... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCamelCase_ ( nn.Module ):
def __init__( self , A = 16 , A = 88 , A = None , A = 1 , A = 0.0 , A = 32 , A = None ... | 338 |
'''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 | 1 |
'''simple docstring'''
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... | 338 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a : Optional[Any] = {
"""configuration_efficientformer""": [
"""EFFICIENTFORME... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a : Optional[int] = [
# tf -> hf
("""/""", """."""),
("""layer_""", """la... | 338 |
'''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 | 1 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thr... | 338 |
'''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 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a : Union[str, Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and ... | 338 |
'''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 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : int = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and ... | 338 |
'''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 | 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... | 338 |
'''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 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : int = logging.get_logger(__name__)
a : int = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a : List[str] = (3, 9, -1_1, 0, 7, 5, 1, -1)
a : Any = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class UpperCamelCase_ :
lowercase ... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
a : ... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
if digit_amount > 0:
return round(number - int(_lowercase ) , _lowercase )
return number - int(_lowercase )
if __name__ == "__main__":
print(decimal_isolate(1.5_3... | 338 |
'''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 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCamelCase_ :
lowercase = None
lowercase = False
lowercase = False
lowercase = False
lowercase ... | 338 |
'''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 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Tuple = logging.get_logger(__n... | 338 |
'''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 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, 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... | 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 1 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCamelCase_ ( __magic_name_... | 338 |
'''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 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
a : int = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any mem... | 338 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 338 |
'''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 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a : Union[str, Any] ... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class UpperCamelCase_ ( __magic_name__ ):
lowercas... | 338 |
'''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 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
a : str = TypeVar("""T""")
a : Any = TypeVar("""U""")
class UpperCamelCase_ ( Generic[T, U] ):
def __init__( self , A ,... | 338 |
'''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 | 1 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : Tuple = get_tests_dir("""fixtures/test_sentenc... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
a : Optional[Any] = """src/transformers"""
# Matches is_xxx_available()
a : int = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
a : Any ... | 338 |
'''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 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Optional[int] = logging.get_logger... | 338 |
'''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 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def __lowerCamelCase ( _lowercase = None ) -> int:
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
UpperCAmelCase : List[str] = nums[0]
for ... | 338 |
'''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 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__magic_name__... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''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
@r... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> str:
if isinstance(_lowercase , _lowercase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(_lowercase , _lowercase ):
raise TypeErro... | 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 1 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
UpperCAmelCase : int = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowercase )
def __lowerCamelCase ( _lowercase = 1 / 1_2_3_4_5 )... | 338 |
'''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 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""vocab_file""": """voca... | 338 |
'''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 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( __magic_name__ ):
lowercase = (DDPMScheduler,)
def _lowercase( self , **A ) -> int:
UpperCAmelCase ... | 338 |
'''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 | 1 |
'''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 : ... | 338 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def __lowerCam... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
from math import sqrt
def __lowerCamelCase ( _lowercase = 1_0_0_0_0_0_0 ) -> int:
UpperCAmelCase : int = 0
UpperCAmelCase : int = 0
UpperCAmelCase : int
while num_cuboids <= limit:
max_cuboi... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> list:
if len(_lowercase ) == 0:
return []
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = min(_lowercase ), max(_lowercase )
UpperC... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : Any = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Con... | 338 |
'''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 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCamelCase ( _l... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_p... | 338 |
'''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 | 1 |
'''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:... | 338 |
'''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 | 1 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_util... | 338 |
'''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 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditi... | 338 |
'''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 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requi... | 338 |
'''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 | 1 |
'''simple docstring'''
# Imports
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( s... | 338 |
'''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 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipe... | 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 1 |
'''simple docstring'''
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""" )
... | 338 |
'''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 | 1 |
'''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... | 338 |
'''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 | 1 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( __magic_name__ ):
lowercase = (CMStochasticIterativeScheduler,)
lowercase = 10
def _lowercase( ... | 338 |
'''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 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCamelCase_ :
lowercase = field(
default='codeparrot/codeparrot' , metadata={'help': 'Model name or path of model to be trained.'} )
lowercase = fiel... | 338 |
'''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 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tr... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> str:
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed t... | 338 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
a : Tuple = list[tuple[int, int]]
a : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0,... | 338 |
'''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 | 1 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_... | 338 |
'''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 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepen... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Tuple = len(_lowercase )
for i in range(length - 1 ):
UpperCAmelCase : Tuple = i
for k in range(i + 1 , _lowercase ):
... | 338 |
'''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 | 1 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a : List[str] = get_logger(__name__)
class UpperCamelCase_ ( enum.Enum ):
lowercase = 'all_checks'
lowercas... | 338 |
'''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 | 1 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as m... | 338 |
'''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 | 1 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..u... | 338 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''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 |
'''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 | 1 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
UpperCAmelCase : str = math.sqrt(_lowercase )
U... | 338 |
'''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 | 1 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a : Tuple = re.compile(R"""\b(a|an|the)\b""", re.UNICODE)
a : int = None
def __lowerCamelCase ( ) -> Optional[Any]:
Upper... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> float:
return round(float(moles / volume ) * nfactor )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> float:
return round(float((mo... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCAmelCase : Tuple ... | 338 |
'''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 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
if n == 1 or not isinstance(_lowercase , _lowercase ):
return 0
elif n == 2:
return 1
else:
UpperCAmelCase : Any = [0, 1]
for i i... | 338 |
'''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 | 1 |
'''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 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 338 |
'''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 | 1 |
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