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 |
|---|---|---|---|---|
'''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 transfo... | 97 |
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 __lowerCamelCase (_a ):
_lowercase ... | 310 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class A_ ( unitt... | 22 |
import math
def _A ( _lowercase ) -> int:
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
__UpperCamelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowercase ... | 310 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple ... | 10 |
import torch
from transformers import AutoModel
class __lowerCamelCase (torch.nn.Module ):
def __init__( self: Union[str, Any],A_: Tuple="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(A_,self... | 310 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 187 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase (_a , unittest.TestCa... | 310 | 0 |
def lowerCamelCase__ ( a ) -> list:
def merge(a , a ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len... | 121 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__snake_case = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read... | 310 | 0 |
"""simple docstring"""
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
... | 217 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __lowerCamelCase (_a ):
_lowercase = """M-CLIP"""
def __init__( self: int,A_: Any=1024,A_: Union[str, Any]=768,**A_: str ):
... | 310 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> bool:
snake_case__ : Any = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case__ : List[str] = set()
return any(
node not ... | 35 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 310 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : List[str] =prime_factors(_lowercase )
if is_square_free(_lowercase ):
... | 348 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case = get_tes... | 310 | 0 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [0] * len(_lowercase )
lowerCAmelCase_ = []
lowerCAmelCase_ = [1] * len(_lowercase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(... | 278 |
def _A ( _lowercase ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(_lowercase , _lowercase ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(_lowercase )]
i... | 310 | 0 |
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 274 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 310 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class a ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_lowerCAmelCase = ... | 168 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.m... | 310 | 0 |
'''simple docstring'''
__snake_case = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def a ( __a , __a , __a , __a ) -> int:
'''simple docstring'''
... | 97 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 310 | 0 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__SCREAMING_SNAKE_CASE :Tuple = 6378137.0
__SCREAMING_SNAKE_CASE :Union[str, Any] = 6356752.314245
__SCREAMING_SNAKE_CASE :List[Any] = 6378137
def U... | 22 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''xlm-roberta-base''': '''https://huggin... | 310 | 0 |
__A = "Input must be a string of 8 numbers plus letter"
__A = "TRWAGMYFPDXBNJZSQVHLCKE"
def lowerCAmelCase_ ( __a ) -> bool:
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
lowerCamelCase__: Union[str, Any] =F"""Expec... | 10 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__snake_case = logging.getLogg... | 310 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( _a ):
... | 187 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_en... | 310 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require... | 121 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTok... | 310 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
... | 217 |
import os
def _A ( ) -> Tuple:
"""simple docstring"""
with open(os.path.dirname(_lowercase ) + '/p022_names.txt' ) as file:
__UpperCamelCase = str(file.readlines()[0] )
__UpperCamelCase = names.replace... | 310 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"p... | 35 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import ... | 310 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ( _a ):
__lowerCamelCase : L... | 348 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
... | 310 | 0 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = generate_pascal_triangle(_lowercase )
for row_idx in range(_lowercase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
# Print row values
... | 278 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__snake_case = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def _A ( _lowercase = "mumbai" ) -> Generator[tuple[str, s... | 310 | 0 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ) -> str:
"""simple docstring"""
raise RuntimeErr... | 274 |
def _A ( _lowercase ) -> list:
"""simple docstring"""
def merge(_lowercase , _lowercase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 310 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Uni... | 168 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_mul... | 310 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__snake_case = 3
def a ( __a ) -> int:
'''simple docstring'''
print('''Generating primitive root of p''' )
while True:
... | 97 |
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 __lowerCamelCase (_a ):
_lowercase ... | 310 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : int , __lowercase : List[Any] ) -> dict[str, float]:
'''simple docstring'''
if (induc... | 22 |
import math
def _A ( _lowercase ) -> int:
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
__UpperCamelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowercase ... | 310 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowercase_ = 42
lowercase_ ... | 10 |
import torch
from transformers import AutoModel
class __lowerCamelCase (torch.nn.Module ):
def __init__( self: Union[str, Any],A_: Tuple="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(A_,self... | 310 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase__ : str = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
imp... | 187 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase (_a , unittest.TestCa... | 310 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
UpperCAmelCase__ : List[str] = TypeVar('T')
class UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
__UpperCamelCase : str = 42 # Cache store of k... | 121 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__snake_case = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read... | 310 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"roberta-base": "https://huggin... | 217 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __lowerCamelCase (_a ):
_lowercase = """M-CLIP"""
def __init__( self: int,A_: Any=1024,A_: Union[str, Any]=768,**A_: str ):
... | 310 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from tra... | 35 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 310 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_f... | 348 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case = get_tes... | 310 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {}
class A ( _a ):
__snake_case = 'llama'
__snake_case = ['past_key_values']
def __init__( self, UpperCamelCase__=3_2000, UpperCamelCase__... | 278 |
def _A ( _lowercase ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(_lowercase , _lowercase ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(_lowercase )]
i... | 310 | 0 |
def __lowerCamelCase ( __a :List[str] ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("""input must be a negative integer""" )
A__ = len(bin(_lowercase )[3:] )
A__ = bin(abs(_lowercase ) - (1 << binary_number... | 274 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 310 | 0 |
'''simple docstring'''
class a :
def __init__( self , __magic_name__ ) -> str:
_a = len(A_ )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A_ ):
_a ... | 168 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.m... | 310 | 0 |
'''simple docstring'''
from functools import reduce
__snake_case = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227... | 97 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 310 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__SCREAMING_SNAKE_CASE :Any = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be... | 22 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''xlm-roberta-base''': '''https://huggin... | 310 | 0 |
from __future__ import annotations
import numpy as np
def lowerCAmelCase_ ( __a ) -> Optional[int]:
"""simple docstring"""
return np.maximum(0 , _lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 10 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__snake_case = logging.getLogg... | 310 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from d... | 187 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_en... | 310 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resi... | 121 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTok... | 310 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | 217 |
import os
def _A ( ) -> Tuple:
"""simple docstring"""
with open(os.path.dirname(_lowercase ) + '/p022_names.txt' ) as file:
__UpperCamelCase = str(file.readlines()[0] )
__UpperCamelCase = names.replace... | 310 | 0 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
a : Dict = get_logger(__name__)
a : Optional[Any] = R"\n Args:\n input_ids (`jnp.ndarray` of shape `(b... | 311 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoToken... | 311 | 1 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a : int = datasets.load_iris()
a : Union[str, Any] = np.array(data["data"])
a : Optional[Any] = np.array(data["target"])
a... | 311 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : str = len(__magic_name__ )
# We need to create solution object to save path.
UpperCAmelCase : Tuple ... | 311 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase )... | 311 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = "M-CLIP"
def __init__( self , snake_case=1_0_2_4 , ... | 311 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_com... | 311 | 1 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = (CMStochasticIterativeScheduler,)
SCREA... | 311 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase : List[Any] = len(bin(__magic_name__ )[3:] )
UpperCAmelCase... | 311 | 1 |
'''simple docstring'''
import math
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Dict = 0
UpperCAmelCase : Any = 0
while num > 0:
UpperCAmelCase : Optional[Any] = num % 8
... | 311 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a : int = datasets.load_iris()
a : Union[str, Any] = np.array(data["data"])
a : Optional[Any] = np.array(data["target"])
a... | 311 | 1 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules im... | 311 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals... | 311 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constan... | 311 | 1 |
'''simple docstring'''
import datasets
a : Union[str, Any] = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and S... | 311 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : Optional[Any] = logging.get_logger(__name__)
a : Tuple = "T5Config"
... | 311 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : int = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_avai... | 311 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
a : List[Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improve... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
a : str = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def lowercase ( __magic_name__ = "mumbai" ):
'''simple do... | 311 |
'''simple docstring'''
from functools import lru_cache
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = 2
UpperCAmelCase : str = set()
while i * i <= n:
if n % i:
... | 311 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from ... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"fea... | 311 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = None , ):
'''simple docstring'''
UpperCAmelCase : Dict = np.shape(__magic_name__ )
UpperCAmelCas... | 311 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a : Tuple = re.compile(R"([A-Z]+)([A-Z][a-z])")
a : Union[str, Any] = re.compile(R"([a-z\d])([A-Z])")
a : str = re.compile(R"(?<!_)_(?!_)")
a : List[Any] = re.compile(R"(_{2,})")
a : ... | 311 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
res... | 311 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a : Optional... | 311 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
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
fro... | 311 |
'''simple docstring'''
import argparse
import copy
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : List[str] = {}
with open(__magic_name__ ) as f:
for line in f:
if line.split()[0] not in dict_... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
... | 311 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def lowercase ( __magic_name__ ):
'''simple docstring'''
if len(__magic_name__ ) != 32:
raise ValueError("Input must be of length 32" )
UpperCAmelCase : Un... | 311 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase ( __magic_name__ ):
'''simple docstring'''
return x + 2
class UpperCamelCase__ ( unittest.Te... | 311 |
'''simple docstring'''
a : List[str] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip... | 311 | 1 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ ):
'''simple docstring'... | 311 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosit... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
a : Tuple = 0
a : List[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],
[... | 311 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=loggi... | 311 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_mo... | 311 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_... | 311 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import c... | 311 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : str = "docs/source/en/_toctree.yml"
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Dict = defaultdict(__magic_name__ ... | 311 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Any = int(np.ceil((x_e... | 311 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowercase ( __magic_name__ ):
'''simple docstring'''
for param in module.parameters():
UpperCAmelCase : Any = False
def lowercase ... | 311 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["sentencepiece"]
def __init__( self , *snake_case , **snake_case ):
... | 311 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoToken... | 311 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : List[Any] = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANINE models at ht... | 311 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 311 | 1 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils... | 311 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase )... | 311 | 1 |
'''simple docstring'''
a : List[str] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip... | 311 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_com... | 311 | 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 : Union[str, Any] = logging.get_logger(__nam... | 311 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase : List[Any] = len(bin(__magic_name__ )[3:] )
UpperCAmelCase... | 311 | 1 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , ... | 311 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a : int = datasets.load_iris()
a : Union[str, Any] = np.array(data["data"])
a : Optional[Any] = np.array(data["target"])
a... | 311 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...mode... | 311 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311 | 1 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : Optional[Any] = logging.get_logger(__name__)
a : Tuple = "T5Config"
... | 311 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constan... | 311 | 1 |
'''simple docstring'''
import argparse
a : List[str] = "docs/source/_static/js/custom.js"
def lowercase ( __magic_name__ ):
'''simple docstring'''
with open(__magic_name__ , encoding="utf-8" , newline="\n" ) as f:
UpperCAmelCase : ... | 311 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : Optional[Any] = logging.get_logger(__name__)
a : Tuple = "T5Config"
... | 311 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_F... | 311 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
a : List[Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improve... | 311 | 1 |
'''simple docstring'''
import cva
import numpy as np
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case , snake_case ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase : int = k
UpperCAmel... | 311 |
'''simple docstring'''
from functools import lru_cache
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = 2
UpperCAmelCase : str = set()
while i * i <= n:
if n % i:
... | 311 | 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 transfo... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"fea... | 311 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 311 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a : Tuple = re.compile(R"([A-Z]+)([A-Z][a-z])")
a : Union[str, Any] = re.compile(R"([a-z\d])([A-Z])")
a : str = re.compile(R"(?<!_)_(?!_)")
a : List[Any] = re.compile(R"(_{2,})")
a : ... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
import bisect
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ = 0 , __magic_name__ = -1 ):
'''simple docstring'''
if hi < 0:
UpperCAmelCase : int = len(__magic_n... | 311 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a : Optional... | 311 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase )... | 311 |
'''simple docstring'''
import argparse
import copy
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : List[str] = {}
with open(__magic_name__ ) as f:
for line in f:
if line.split()[0] not in dict_... | 311 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : ... | 311 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def lowercase ( __magic_name__ ):
'''simple docstring'''
if len(__magic_name__ ) != 32:
raise ValueError("Input must be of length 32" )
UpperCAmelCase : Un... | 311 | 1 |
'''simple docstring'''
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : Any = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
UpperCAmelCase : List[Any] = 6
UpperCAmelCase : Any = 1
UpperCAmelCase : Li... | 311 |
'''simple docstring'''
a : List[str] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip... | 311 | 1 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Tuple = [1]
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] = 0, 0, 0
UpperCAmelCase : Dict = ... | 311 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosit... | 311 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( lowercase__ ):
"""... | 311 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=loggi... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
a : Tuple = list[tuple[int, int]]
a : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0... | 311 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = 2
UpperCAmelCase : Optional[Any] = []
while i * i <= n:
if n % i:
... | 311 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : str = "docs/source/en/_toctree.yml"
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Dict = defaultdict(__magic_name__ ... | 311 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a : str = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
def __init__( self , *snake_case , **snake_... | 311 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowercase ( __magic_name__ ):
'''simple docstring'''
for param in module.parameters():
UpperCAmelCase : Any = False
def lowercase ... | 311 | 1 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
... | 311 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoToken... | 311 | 1 |
'''simple docstring'''
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(__magic_name__ , x % y )
def lowercase ( __magic_name__ , __magic_name__ ):
'''simpl... | 311 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 311 | 1 |
'''simple docstring'''
def lowercase ( __magic_name__ = 1000 ):
'''simple docstring'''
UpperCAmelCase : str = 2**power
UpperCAmelCase : List[Any] = 0
while n:
UpperCAmelCase , UpperCAmelCase : Dict = r + n... | 311 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase )... | 311 | 1 |
'''simple docstring'''
import string
import numpy
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , __magic_name__ )
class UpperCamelCase__ :
"""simple docstring""... | 311 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_com... | 311 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
def... | 311 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase : List[Any] = len(bin(__magic_name__ )[3:] )
UpperCAmelCase... | 311 | 1 |
'''simple docstring'''
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.uti... | 311 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a : int = datasets.load_iris()
a : Union[str, Any] = np.array(data["data"])
a : Optional[Any] = np.array(data["target"])
a... | 311 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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_c... | 311 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311 | 1 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a : Tuple = re.compile(R"([A-Z]+)([A-Z][a-z])")
a : Union[str, Any] = re.compile(R"([a-z\d])([A-Z])")
a : str = re.compile(R"(?<!_)_(?!_)")
a : List[Any] = re.compile(R"(_{2,})")
a : ... | 311 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constan... | 311 | 1 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
a : Any = namedtuple(
"_TestCommandArgs",
[
... | 311 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : Optional[Any] = logging.get_logger(__name__)
a : Tuple = "T5Config"
... | 311 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ["speech"]
def __init__( self , *snake_case , **snake_case ):
'''simple doc... | 311 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
a : List[Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improve... | 311 | 1 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if len(__magic_name__ ) <= 1:
return [tuple(__magic_name__ )]
UpperCAmelCase : int = []
def generate(__magic_name__ , __magic_name__ ):
... | 311 |
'''simple docstring'''
from functools import lru_cache
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = 2
UpperCAmelCase : str = set()
while i * i <= n:
if n % i:
... | 311 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : List[Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/c... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"fea... | 311 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : List[Any] = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/con... | 311 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a : Tuple = re.compile(R"([A-Z]+)([A-Z][a-z])")
a : Union[str, Any] = re.compile(R"([a-z\d])([A-Z])")
a : str = re.compile(R"(?<!_)_(?!_)")
a : List[Any] = re.compile(R"(_{2,})")
a : ... | 311 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Optional[int] = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all V... | 311 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a : Optional... | 311 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
f... | 311 |
'''simple docstring'''
import argparse
import copy
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : List[str] = {}
with open(__magic_name__ ) as f:
for line in f:
if line.split()[0] not in dict_... | 311 | 1 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert im... | 311 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def lowercase ( __magic_name__ ):
'''simple docstring'''
if len(__magic_name__ ) != 32:
raise ValueError("Input must be of length 32" )
UpperCAmelCase : Un... | 311 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_... | 311 |
'''simple docstring'''
a : List[str] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip... | 311 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.