code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
_lowerCamelCase : Optional[int] = logging.get_logger(__nam... | 14 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Re... | 14 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 1 |
from __future__ import annotations
_lowerCamelCase : Union[str, Any] = 10
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
"""simple docstring"""
A__ = 1
A__ = max(lowercase_ )
while placement <= max_digit:
... | 14 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 1 |
_lowerCamelCase : Tuple = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowerCamelCase : ... | 14 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 1 |
from copy import deepcopy
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : list[int] | None = None , UpperCAmelCase__ : int | None = None) ->None:
'''simple docstring'''
if arr is... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 1 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = ['''flax''', '''transformers''']
def __init__( self : List[str] , *UpperCAmelCase__ : str , **UpperCA... | 14 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 14 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Data... | 14 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 1 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 14 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> float:
"""simple docstring"""
return np.dot(lowercase_ , lowercase_ )
class UpperCamelCase_ :
''... | 14 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase : Any = 500000
_lowerCamelCase , _lowerCamelCase : List[Any] = os.path.split(__file__)
_lowerCamelCase : Tuple ... | 14 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 1 |
from math import ceil
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
A__ = list(range(0 , lowercase_ ) )
A__ = [item for sublist in list(device_map.values() ) for item in sublist]
... | 14 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 14 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_lowerCamelCase : Tuple = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
clas... | 14 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = ''''''
for word_or_phrase in separated:
if not isinstance(lowercase_ , lowercase_ ):
raise Exception('''join() accepts only... | 14 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 1 |
import colorsys
from PIL import Image # type: ignore
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> float:
"""simple docstring"""
A__ = x
A__ = y
for step in range(lowercase_ ): # noqa: B007
... | 14 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 1 |
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_channel_dimension_format,
)
f... | 14 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 ... | 14 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[int] = {"""configuration_xlnet""": ["""XLNET_PRETRAI... | 14 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determ... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_envir... | 14 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 ... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 1 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
requ... | 14 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_lowerCamelCase : str = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : int , *UpperCAm... | 14 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> np.ndarray:
"""simple docstring"""
A__ , A__ , A__ = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0... | 14 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstring"""
A__ = int(lowercase_ )
assert noofclusters < len(lowercase_ )
... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Union[str, Any]:
"""si... | 14 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
@register_... | 14 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ) -> tuple[int, float, str]:
"""simple docstring"""
A__ = cipher_alphabet or [chr(lowercase_ ) for i in range(9... | 14 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : str = {
"""configuration_vision_text_dual_encoder""": ["""VisionTextDualEncoderConfig"""],
... | 14 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> list:
"""simple docstring"""
A__ = len(lowercase_ )
A__ = []
for i in range(len(lowercase_ ) - pat_len + 1 ):
A__ = True
for j in ra... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 1 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> list[int]:
"""simple docstring"""
A__ = [0] * no_of_processes
A__ = [0] * no_of_processes
# Copy the burs... | 14 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 1 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
_lowerCamelCase : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_S... | 14 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> bool:
"""simple docstring"""
A__ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
A__ = set()
return any(
node not in visited and depth_first_... | 14 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 14 | 1 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 14 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, l... | 14 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Union[str, Any] = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
... | 14 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 1 |
import baseaa
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
"""simple docstring"""
... | 14 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_owlvit""": [
... | 14 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from .... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 1 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gp... | 14 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = 42
UpperCAmelCase__ = No... | 14 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 ... | 14 | 1 |
from __future__ import annotations
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : str) ->List[str]:
'''simple docstring'''
A__ , A__ = te... | 14 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 1 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
U... | 14 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See ... | 14 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
A__ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def SCREAMING_SNAKE_CASE ... | 14 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCr... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_lowerCamelCase : List[Any] ... | 14 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 1 |
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> list:
"""simple docstring"""
A__ = 0
# Number of processes finished
A__ = 0
# Displays the... | 14 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 1 |
import collections
import importlib.util
import os
import re
from pathlib import Path
_lowerCamelCase : str = """src/transformers"""
# Matches is_xxx_available()
_lowerCamelCase : Optional[int] = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = ... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 1 |
from __future__ import annotations
import queue
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase__ : str) ->Tuple:
'''simple docstring'''
A__ = data
A__ = None
A... | 14 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ = 600_851_475_143 ) -> int:
"""simple docstring"""
try:
A__ = int(lowercase_ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
... | 14 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE ( self : Optional[in... | 14 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 14 | 1 |
_lowerCamelCase : int = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""": ... | 14 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowerCamelCase : List[str] = {
... | 14 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 1 |
_lowerCamelCase : Tuple = {
0: """0""",
1: """1""",
2: """2""",
3: """3""",
4: """4""",
5: """5""",
6: """6""",
7: """7""",
8: """8""",
9: """9""",
10: """a""",
11: """b""",
12: """c""",
13: """d""",
14: """e""",
15: """f""",
}
... | 14 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 1 |
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
_lowerCamelCase : Tuple = logging.get_logger(__name__)
... | 14 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 1 |
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 SCREAMING_SNAKE_CASE ( lowercase_ , lower... | 14 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 1 |
from random import randint, random
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ = False , lowercase_ = False , lowercase_ = 5 , ) -> list:
"""simple docstring"""
A__ = [[-1] * number_of_cells] # Create a highway w... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_lowerCamelCase : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Tuple , *UpperCA... | 14 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = [[0 for _ in range(lowercase_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
A__ = 1
for n in range(m + 1 ):
for ... | 14 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 ... | 14 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class ... | 14 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 1 |
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
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
... | 14 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 1 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 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 l... | 14 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/sw... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Opt... | 14 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 1 |
import datasets
from .evaluate import evaluate
_lowerCamelCase : Any = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year=... | 14 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
_lo... | 14 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.j... | 14 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase_ ( UpperCAmelCase__ ):
... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase__ : list[tuple[float, float]]) ->List[str]:
'''simple docstring'''
... | 14 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ = "" ) -> dict[str, float]:
"""simple docstring"""
A__ = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
A__... | 14 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset,... | 14 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
assert column_title.isupper()
A__ = 0
A__ = len(lowercase_ ) - 1
A__ = 0
while index >= 0:
A__ = (ord(column_title[index... | 14 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 1 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 14 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
if len(lowercase_ ) != len(lowercase_ ):
raise ValueError('''String lengths must match!''' )
A__ = 0
for chara, chara in zip(lowercase... | 14 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils im... | 14 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[float]]:
"""simple docstring"""
A__ = []
for data in source_data:
for i, el in enumerate(lowercase_ ):
if len(lowercase_ ) < i + 1:
d... | 14 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAme... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 ... | 14 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Optional[Any]:
"""simple docstring"""
A__ = FileLock(str(tmpdir / '''foo.lock''' ) )
A__ = FileLoc... | 14 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impo... | 14 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 1 |
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,
SkipBatchSampler,
SkipDataLoader,
... | 14 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def SCREAMING_SNAKE_CASE ( ) -> tuple[list[int], int]:
"""simple docstring"""
A__ = [randint(-1_000 , 1_000 ) for i in range(10 ... | 14 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Optional[Any] =... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 1 |
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_configuration_common import Confi... | 14 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 1 |
from __future__ import annotations
_lowerCamelCase : Optional[Any] = 1.60_21E-19 # units = C
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , ) -> tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobilit... | 14 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutpu... | 14 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 1 |
import argparse
from collections import defaultdict
import yaml
_lowerCamelCase : List[str] = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = defaultdict(lowercase_ )
... | 14 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 1 |
from collections.abc import Iterable
from typing import Any
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase__ : int | None = None) ->Union[str, Any]:
'''simple docstring'''
A__ = v... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 1 |
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 14 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self : int ,... | 14 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 1 |
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