code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
lowerCAmelCase: Optional[int] ={
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
... | 607 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTe... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIV... | 393 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def ... | 59 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 94 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "CLIPImageProc... | 59 | 0 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
Mobi... | 609 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
for param in module.parameters():
lowerCamelCase__: Tuple =False
def lowerCAmelCase_ ( ) -> Optional[int]:
... | 59 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="my_dataset" ... | 216 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 59 | 0 |
"""simple docstring"""
def _UpperCamelCase ( ) -> str:
"""simple docstring"""
__UpperCAmelCase : str = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
__UpperCAmelCase : List[str] = 6
__UpperCAmelCase : int ... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A = logging.get_logger(__name__)
__A = {... | 59 | 0 |
"""simple docstring"""
import numpy as np
def _snake_case ( lowercase__ ):
return 1 / (1 + np.exp(-vector ))
def _snake_case ( lowercase__ ):
return vector * sigmoid(1.7_0_2 * vector )
if __name__ == "__main__"... | 630 |
import operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: T... | 59 | 0 |
import requests
lowerCamelCase_ = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def lowerCamelCase ( a_ ) -> None:
lowerCAmelCase_ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
... | 318 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
f... | 59 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/de... | 499 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = CustomTokenizer
pass
| 59 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Dict = 10 ):
if not isinstance(__a , __a ) or n < 0:
raise ValueError('Invalid input' )
lowerCAmelCase = 10**n
lowerCAmelCase = 2_8433 * (pow(2 , 783_0457 , __a )) + 1
return str(number %... | 4 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''s... | 59 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_... | 463 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose... | 59 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
def ... | 607 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS... | 59 | 0 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.tes... | 393 |
from collections import UserDict
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... | 59 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 94 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_byte... | 59 | 0 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCAmelCase__ (snake_case__ : Dict ):
"""simple docstring"""
for param in module.parameters():
_snake_case : Tuple = False
def UpperCAme... | 609 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "ChineseCLIPIm... | 59 | 0 |
from maths.prime_check import is_prime
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(__a , __a ):
SCREAMING_SNAKE_CASE_ : Any = f'Input value of [number={number}] must be an integer'
raise TypeError(__a )
i... | 216 |
from math import ceil, sqrt
def lowerCAmelCase_ ( __a = 1000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCamelCase__: Optional[int] =... | 59 | 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__ ( ... | 77 |
def lowerCAmelCase_ ( __a = 50000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =set()
lowerCamelCase__: int =int((limit - 24) ** (1 / 2) )
lowerCamelCase__: Tuple =set(range(3 , prime_square_limit + 1 , 2 ) ... | 59 | 0 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _snake_case ( lowercase__ ):
return np.dot(__a , __a )
class lowerCAmelCase__ :
... | 630 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( __a , __a , __a = 10**-10 ) -> float:
"""simple docstring"""
lowerCamelCase__: List[str] =a
while Tru... | 59 | 0 |
from math import pow, sqrt
def lowerCamelCase ( *a_ ) -> bool:
lowerCAmelCase_ = len(__a ) > 0 and all(value > 0.0 for value in values )
return result
def lowerCamelCase ( a_ , a_ ) -> float | ValueError:
return (
... | 318 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''... | 59 | 0 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__A : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership fu... | 499 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import ... | 59 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-be... | 4 |
from __future__ import annotations
from math import pi
def lowerCAmelCase_ ( __a , __a , __a ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be... | 59 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 463 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 59 | 0 |
"""simple docstring"""
lowerCAmelCase: Dict ={
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3"... | 607 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTe... | 59 | 0 |
A_ = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
A_ ... | 393 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def ... | 59 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCAmelCase_ ... | 94 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "CLIPImageProc... | 59 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ..... | 609 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
for param in module.parameters():
lowerCamelCase__: Tuple =False
def lowerCAmelCase_ ( ) -> Optional[int]:
... | 59 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UN... | 216 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 59 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = ... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A = logging.get_logger(__name__)
__A = {... | 59 | 0 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__... | 630 |
import operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: T... | 59 | 0 |
import argparse
from collections import defaultdict
def lowerCamelCase ( a_ , a_ , a_ , a_ , a_ ) -> Optional[int]:
lowerCAmelCase_ = F'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
with open(__a , 'r' ) as f:
... | 318 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
f... | 59 | 0 |
"""simple docstring"""
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
UpperCamelCase : List[str] = [0] * (upper_limit + 1)
# Ba... | 499 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = CustomTokenizer
pass
| 59 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
'''ut/deta''': '''h... | 4 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''s... | 59 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class ... | 463 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose... | 59 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_imag... | 607 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS... | 59 | 0 |
from __future__ import annotations
from math import pi
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )-> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only on... | 393 |
from collections import UserDict
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... | 59 | 0 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
SCREAMING_SNAKE_CASE = 'naver-clova-ix/donut-base'
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self : List[Any] ) -> str:
''... | 94 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_byte... | 59 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase__ = ["note_seq"]
def __init__( self: List[str], *a_: Optional[Any]... | 609 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "ChineseCLIPIm... | 59 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@requ... | 216 |
from math import ceil, sqrt
def lowerCAmelCase_ ( __a = 1000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCamelCase__: Optional[int] =... | 59 | 0 |
"""simple docstring"""
import random
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : List[Any] = a[left_index]
__UpperCAmelCase : Optional[int]... | 77 |
def lowerCAmelCase_ ( __a = 50000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =set()
lowerCamelCase__: int =int((limit - 24) ** (1 / 2) )
lowerCamelCase__: Tuple =set(range(3 , prime_square_limit + 1 , 2 ) ... | 59 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowercase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''... | 630 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( __a , __a , __a = 10**-10 ) -> float:
"""simple docstring"""
lowerCamelCase__: List[str] =a
while Tru... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
"""configuration_mobilebert""": [
"""MOBILEBERT_PRETRAINED_CONFIG_AR... | 318 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''... | 59 | 0 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def A_ ( snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = np.max(__a ,axis=-1 ,keepdims=__a )
UpperCamelCase : int =... | 499 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import ... | 59 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 3 , _UpperCAmelCase : List[Any] = 7 , _UpperCAmelCase : Dict = 100_0000 ):
lowerCAmelCase = 0
lowerCAmelCase = 1
for current_denominator in range(1 , limit + 1 ):
lowerCAmelCas... | 4 |
from __future__ import annotations
from math import pi
def lowerCAmelCase_ ( __a , __a , __a ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be... | 59 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 463 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 59 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils impo... | 607 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTe... | 59 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
g... | 393 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def ... | 59 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor... | 94 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "CLIPImageProc... | 59 | 0 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''t5-small''': '''https://huggingface.co... | 609 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
for param in module.parameters():
lowerCamelCase__: Tuple =False
def lowerCAmelCase_ ( ) -> Optional[int]:
... | 59 | 0 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomS... | 216 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 59 | 0 |
"""simple docstring"""
A = tuple[float, float, float]
A = tuple[float, float, float]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Vectorad:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = end_poin... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A = logging.get_logger(__name__)
__A = {... | 59 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...ut... | 630 |
import operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: T... | 59 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"""microsoft/focalnet-tiny""": ""... | 318 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
f... | 59 | 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/LICENSE... | 499 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = CustomTokenizer
pass
| 59 | 0 |
"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] ):
lowerCAmelCase = {
"en": "Machine learning... | 4 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''s... | 59 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
__A =[
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
('''time_embed... | 463 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose... | 59 | 0 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowerCAmelCase: Any =namedtuple("covid_data", "cases deaths recovered")
def __snake_case ( __A = "https://www.worldometers.info/coronavirus/" ) -> c... | 607 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS... | 59 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils impor... | 393 |
from collections import UserDict
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... | 59 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ = ['''image_processor''', '''t... | 94 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_byte... | 59 | 0 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def UpperCAmelCase__ (snake_case__ : Optional[int] ):
"""simple docstring... | 609 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "ChineseCLIPIm... | 59 | 0 |
def _snake_case ( lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE_ : set[int] = set()
return ... | 216 |
from math import ceil, sqrt
def lowerCAmelCase_ ( __a = 1000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCamelCase__: Optional[int] =... | 59 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A = True
except (ImportError, ModuleNotFoundError):
A = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def _UpperCam... | 77 |
def lowerCAmelCase_ ( __a = 50000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =set()
lowerCamelCase__: int =int((limit - 24) ** (1 / 2) )
lowerCamelCase__: Tuple =set(range(3 , prime_square_limit + 1 , 2 ) ... | 59 | 0 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _snake_case ( lowercase__ ):
if num <= 0:
raise ValueError('math domain error' )
return quad(__a , 0 , __a , ... | 630 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( __a , __a , __a = 10**-10 ) -> float:
"""simple docstring"""
lowerCamelCase__: List[str] =a
while Tru... | 59 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase ( a_ ) -> typing.Counter[int]:
lowerCAmelCase_ = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(__a ... | 318 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''... | 59 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalD... | 499 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import ... | 59 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : int = {'''configuration_fnet''': ['''FNET_PRETRAINE... | 4 |
from __future__ import annotations
from math import pi
def lowerCAmelCase_ ( __a , __a , __a ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be... | 59 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if len(__a ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(__a )
or lef... | 463 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 59 | 0 |
"""simple docstring"""
import sys
import turtle
def __snake_case ( __A ,__A ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __snake_case ( __A ,__A ,__A ,__A ,) -> None:
my_pen.up()
my_pen.goto(vertex... | 607 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTe... | 59 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://... | 393 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def ... | 59 | 0 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowercase_ ( ) -> List[str]:
"""simple docstring"""
lowercase : Optional[Any] =os.path.dirn... | 94 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "CLIPImageProc... | 59 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
A_ = set(
'''approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked co... | 609 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
for param in module.parameters():
lowerCamelCase__: Tuple =False
def lowerCAmelCase_ ( ) -> Optional[int]:
... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pas... | 216 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 59 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
A = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A = logging.get_logger(__name__)
__A = {... | 59 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_comm... | 630 |
import operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: T... | 59 | 0 |
import qiskit
def lowerCamelCase ( a_ , a_ ) -> qiskit.result.counts.Counts:
lowerCAmelCase_ = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
lowerCAmelCase_ = qiskit.QuantumCircuit(__a ... | 318 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
f... | 59 | 0 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_u... | 499 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = CustomTokenizer
pass
| 59 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any ):
lowerCAmelCase = analyze_text(__a )
lowerCAmelCase = list(' ' + ascii_lowercase )
# what ... | 4 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''s... | 59 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ = 'Speech2TextFeatureExtractor'
lowerCAmelCase__ = 'Speech2TextTokenizer'
def __init__( self , lowercase... | 463 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose... | 59 | 0 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transfor... | 607 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS... | 59 | 0 |
from __future__ import annotations
class snake_case :
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = order
# a_{0} ... a_{k}
SC... | 393 |
from collections import UserDict
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... | 59 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Trans... | 94 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_byte... | 59 | 0 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_... | 609 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "ChineseCLIPIm... | 59 | 0 |
import math
def _snake_case ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : str ):
"""simple docstring"""
if (
not isinstance(__a , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a valid fl... | 216 |
from math import ceil, sqrt
def lowerCAmelCase_ ( __a = 1000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCamelCase__: Optional[int] =... | 59 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCamelCase ( UpperCamelCase ) -> Any:
"""simple docstring"""
__UpperCAmelCase : List[Any] = [
... | 77 |
def lowerCAmelCase_ ( __a = 50000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =set()
lowerCamelCase__: int =int((limit - 24) ** (1 / 2) )
lowerCamelCase__: Tuple =set(range(3 , prime_square_limit + 1 , 2 ) ... | 59 | 0 |
"""simple docstring"""
import random
from typing import Any
def _snake_case ( lowercase__ ):
for _ in range(len(__a ) ):
_lowerCamelCase : Tuple = random.randint(0 , len(__a ) - 1 )
_lowe... | 630 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( __a , __a , __a = 10**-10 ) -> float:
"""simple docstring"""
lowerCamelCase__: List[str] =a
while Tru... | 59 | 0 |
def lowerCamelCase ( a_ ) -> list:
for i in range(len(__a ) - 1 , 0 , -1 ):
lowerCAmelCase_ = False
for j in range(__a , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 318 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''... | 59 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
lowercase : List[str] = ['image_processor', 'tokenizer']
lowercase : Any = 'Chinese... | 499 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import ... | 59 | 0 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a ( __SCREAMING_SNAKE_CASE ):
def __init__( ... | 4 |
from __future__ import annotations
from math import pi
def lowerCAmelCase_ ( __a , __a , __a ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be... | 59 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..mod... | 463 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 59 | 0 |
"""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 lowerCamelCase__ ( unittest.Te... | 607 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTe... | 59 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NA... | 393 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def ... | 59 | 0 |
'''simple docstring'''
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,
St... | 94 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "CLIPImageProc... | 59 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_f... | 609 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
for param in module.parameters():
lowerCamelCase__: Tuple =False
def lowerCAmelCase_ ( ) -> Optional[int]:
... | 59 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : Dict ... | 216 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 59 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Conv... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A = logging.get_logger(__name__)
__A = {... | 59 | 0 |
"""simple docstring"""
from typing import 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 imp... | 630 |
import operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: T... | 59 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --hos... | 318 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
f... | 59 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__A : Any = logging.get_logger(__name__)
class ... | 499 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = CustomTokenizer
pass
| 59 | 0 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] = None ):
if... | 4 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''s... | 59 | 0 |
import warnings
from functools import wraps
from typing import Callable
def lowerCamelCase_ ( lowerCamelCase__ ):
@wraps(__a )
def _inner_fn(*lowerCamelCase__ , **lowerCamelCase__ ):
warnings.warn(
(F'\'{fn.__name__}\' is experimental and ... | 463 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose... | 59 | 0 |
"""simple docstring"""
lowerCAmelCase: str =[
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from... | 607 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS... | 59 | 0 |
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