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
def lowercase ( a__ : str , a__ : Dict ) -> list[list[int]]:
_UpperCamelCase = []
_UpperCamelCase = []
_UpperCamelCase = 0
_UpperCamelCase = sum(lowercase_ )
create_s... | 420 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
cl... | 606 | 0 |
import warnings
from functools import wraps
from typing import Callable
def a (lowerCAmelCase__ ):
@wraps(lowercase_ )
def _inner_fn(*lowerCAmelCase__ , **lowerCAmelCase__ ):
warnings.warn(
(f'''\'{fn.__name__}\' is experimental and might be subject to breakin... | 99 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( ... | 5 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 0 |
'''simple docstring'''
import datasets
_a : Tuple = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwen... | 56 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import Robe... | 606 | 0 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : int = int(lowercase_ )
if n_element < 1:
__lowerCamelCase : Dict = ValueError('a should be a positive number' )
raise my_error
__lowerCamelCase : Union[str, Any] = [1]
__lowerCamelCa... | 669 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __a (snake_case__ , unittest.TestCase):
'''simple docstring'''
_SCREAMI... | 680 |
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():
import torch
i... | 606 | 0 |
'''simple docstring'''
from collections.abc import Callable
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : Optional[Any] = None ) -> None:
__magic_name__ = []
# Sto... | 664 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__a : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
... | 606 | 0 |
def _a ( lowerCamelCase ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
... | 681 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 0 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict = 1_000) -> int:
'''simple docstring'''
__UpperCamelCase : Tuple = -1
__UpperCamelCase : List[str] = 0
for a in range(1 , n // 3):
# Solv... | 557 |
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():
fro... | 606 | 0 |
"""simple docstring"""
from functools import lru_cache
def _a ( UpperCAmelCase__ ) -> set:
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = set()
while i * i <= n:
if n % i:
i += 1
else:
... | 482 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 0 |
'''simple docstring'''
import sys
from collections import defaultdict
class __snake_case :
'''simple docstring'''
def __init__( self ):
snake_case__ : Optional[Any] = []
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE ):
ret... | 38 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""... | 420 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 0 |
def a (lowerCAmelCase__ ):
try:
__a = float(lowercase_ )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__a = decimal - int(lowercase_ )
if fractional_part == 0:
return int(lowercase_ ), 1
else:
__a = len(str(... | 99 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a : Tuple = logging.get_logger(__name__)
... | 606 | 0 |
'''simple docstring'''
from __future__ import annotations
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = 2
_lowerCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
facto... | 5 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
_a : Dict = {
"""huggingface/time-series-transformer-tourism-monthly""": (
"... | 56 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ ... | 669 |
# 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-2.0
#
# Unl... | 606 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * len(lowercase_ )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [1] * len(lowercase_ )
... | 680 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__magic_name__ : List[Any] =logging.get_logger(__name__)
def __snake_case ( lowerCamelCase_ : str ):
'... | 664 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 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 transformers.mod... | 681 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...tes... | 557 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 0 |
"""simple docstring"""
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
__SCREAMING_SNAKE_CASE = (boundary[1] - boundary[0]) / steps
__SCREAMING_SNAKE_CASE = boundary[0]
__SCREAMING_SNAKE_CASE = boundary[1]
__SCREAMING_SNAKE_CASE... | 482 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 | 0 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation... | 38 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ( snake_case__):
... | 420 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
cl... | 606 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--d... | 99 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler,... | 5 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 0 |
'''simple docstring'''
def _a () -> Optional[int]:
"""simple docstring"""
__snake_case = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(lowercase_ )[-1_0:]
if __name__ == "__main__":
print(solution())
| 56 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import Robe... | 606 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase_ = 1_0
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CA... | 669 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceC... | 680 |
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():
import torch
i... | 606 | 0 |
'''simple docstring'''
__magic_name__ : Union[str, Any] =6_55_21
def __snake_case ( lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
__magic_name__ = 1
__magic_name__ = 0
for plain_chr in plain_text:
__magic_name__ = (a + ord... | 664 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__a : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
... | 606 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""", [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""]... | 681 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 0 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str = 1_000) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1))
if __name__ == "__main__":
print(solution()) | 557 |
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():
fro... | 606 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"""kssteven/ibert-ro... | 482 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
A_ : Dict = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv pre... | 38 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full voca... | 420 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 0 |
from manim import *
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
def snake_case_ ( self ):
__a = Rectangle(height=0.5 , width=0.5 )
__a = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 ... | 99 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a : Tuple = logging.get_logger(__name__)
... | 606 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def A (__lowerCamelCase :Tuple , __lowerCamelCase :List[Any] ,... | 5 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 0 |
'''simple docstring'''
from __future__ import annotations
class _lowercase :
def __init__( self : int , SCREAMING_SNAKE_CASE_ : Dict = 0 ) -> Optional[int]:
__snake_case = key
def a ( self : Union[str, Any] ,... | 56 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class A_ ( snake_case__ ):
'''simple docstring'''
def __init__( self: List[Any] ):
self.test()
def _snake_case ( self: Optional[int] ):
__lowerCamelCase ... | 669 |
# 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-2.0
#
# Unl... | 606 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _lowercase ( __lowerCAmelCase = "" ) -> dict[str, float]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = url or """https://www.imdb.com/chart/top/?ref_=... | 680 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : str =logging.get_logger(__name__)
__magic_name__ : List[str] ={
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""st... | 664 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
# See all SEW-... | 681 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 0 |
print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))')) | 557 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaV... | 482 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeni... | 38 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace 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-2.0
#
# Unle... | 420 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
cl... | 606 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer... | 99 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRCon... | 5 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 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():
impor... | 56 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import Robe... | 606 | 0 |
import numpy as np
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
return 1 / (1 + np.exp(-vector ))
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
return vector * sigmoid(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 669 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 0 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _lowercase ( *__lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase=True , __lowerCAmelCase=2 ) -> Optional[int]:
from ... | 680 |
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():
import torch
i... | 606 | 0 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : Optional[int] = 1000 ):
'''simple docstring'''
__magic_name__ = 3
__magic_name__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
r... | 664 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__a : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
... | 606 | 0 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ... | 681 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 0 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReade... | 557 |
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():
fro... | 606 | 0 |
"""simple docstring"""
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Tuple:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _a ( UpperCAmelCase__ , UpperCAmelCase__=0 ) -> Optional[Any]:
return sorted(lowercase_ , key=la... | 482 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
A_ : Optional[int] = logging.get_logger(__n... | 38 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 420 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pas... | 99 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a : Tuple = logging.get_logger(__name__)
... | 606 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 5 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def _a (lowercase__ : int = 1_5_0_0_0_0_0 ) -> int:
"""simple docstring"""
__snake_case = defaultdict(lowercase_ )
__snake_case = 2
while 2 * euclid_m ... | 56 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class A_ ( snake_case__ ):
'''simple docstring'''
__snake_case = fiel... | 669 |
# 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-2.0
#
# Unl... | 606 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffusion... | 680 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 0 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 664 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamel... | 681 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ... | 557 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 482 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A_ : Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __snake_case ( snake_c... | 38 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 0 |
"""simple docstring"""
from math import isqrt
def lowercase ( a__ : Optional[int] ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase_ ) + 1 ) )
def lowercase ( a__ : int = 10**6 ) -> int:
_UpperCamelCase = 0
... | 420 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
cl... | 606 | 0 |
def a (lowerCAmelCase__ ):
return 10 - x * x
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
if equation(lowercase_ ) * equation(lowercase_ ) >= 0:
raise ValueError("""Wrong space!""" )
__a = a
while (b - a) >= 0.0_1:
# Find middle point
_... | 99 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transf... | 5 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 0 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCal... | 56 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import Robe... | 606 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowercase_ = 1_0_0
lowercase_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowercase_ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
... | 669 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 0 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_w... | 680 |
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():
import torch
i... | 606 | 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 PreTrainedTokenizer
from ...utils import logging
__magic_name__ : List[Any] =logging.get_logger(__name__)
__magic_name__ : Optional[A... | 664 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__a : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
... | 606 | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_g... | 681 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 0 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : str) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive")
__UpperCamelCase : str ... | 557 |
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():
fro... | 606 | 0 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.''' )
_... | 482 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generat... | 38 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 420 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 0 |
from math import factorial, pi
def a (lowerCAmelCase__ , lowerCAmelCase__ = 30 ):
if not isinstance(lowercase_ , (int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or float for theta""" )
if not isinstance(lowercase_ , lowercase_ ) or ac... | 99 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a : Tuple = logging.get_logger(__name__)
... | 606 | 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
_lowercase = get_logger(__name__)
_lowercase = R"""
Args:
input_ids (`jnp.ndarray` of shape `(b... | 5 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 0 |
"""simple docstring"""
def __snake_case ( __A ) -> float:
if edge <= 0 or not isinstance(__A ,__A ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def __snake_case ( __A ... | 607 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_convbert": ["CONVBERT_PRETRAINE... | 607 | 1 |
"""simple docstring"""
class lowerCamelCase__ :
def __init__( self , snake_case ) -> str:
"""simple docstring"""
lowercase : Optional[Any] = n
lowercase : Tuple = [None] * self.n
lowercase : List[A... | 607 |
"""simple docstring"""
import re
def __snake_case ( __A ) -> str:
if len(re.findall("""[ATCG]""" ,__A ) ) != len(__A ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" ,"""TAGC""" ) )
if _... | 607 | 1 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" ,["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" ,["""filename.csv""", """filename w... | 607 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __snake_case ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" ... | 607 | 1 |
"""simple docstring"""
import numpy as np
def __snake_case ( __A ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 607 |
"""simple docstring"""
from ... import PretrainedConfig
lowerCAmelCase: str ={
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = NEZHA_PRETRAIN... | 607 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( __A ) -> Any:
def wrapper(*__A ,**__A ):
lowercase : Tuple ... | 607 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class lowerCamelCase__ ( tf.keras.layers.Layer ):
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False , **snake_case ) -> ... | 607 | 1 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase__ ( __UpperCamelCase , __... | 607 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __snake_case ( __A ,__A = "cpu" ,__A = None ) -> None:
lowercase : Optional[int] = torch.load(__A ,map_location=__A )
for k, v in tqdm(state_... | 607 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=__UpperCamelCase ):
__UpperCAmelCase = ["""onnx"""]
def __init__( self , *snake_case , **snake_case ) -> Union[str, Any]:
... | 607 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __snake_case ( __A ) -> Any:
lowercase : List[str] = os.path.join(args.tf_model_dir ,"""para... | 607 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( __UpperCamelCase ):
@staticmethod
@abstractmethod
def _UpperCAmelCase ( snake_case ) -> List[Any]:
"""simple docstr... | 607 |
"""simple docstring"""
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_... | 607 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from dif... | 607 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( __A ) -> Any:
def wrapper(*__A ,**__A ):
lowercase : Tuple ... | 607 | 1 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
impor... | 607 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
ge... | 607 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase: List[Any] ={
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git"... | 607 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 607 | 1 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set... | 607 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: int ... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ..... | 607 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase: List[str] =parse(importlib.metadata.version("torch"))
def __snake_case ( __A ,__A ... | 607 | 1 |
"""simple docstring"""
def __snake_case ( __A ,__A ,__A ,__A ,__A ,) -> float:
lowercase : str = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("""All input parameters must be positi... | 607 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 | 1 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowerCAmelCase: List[Any] =datasets.logging.get_logger(__name__)
lowerCAmelCase: Any ... | 607 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from tra... | 607 | 1 |
"""simple docstring"""
from itertools import count
def __snake_case ( __A = 50 ) -> int:
lowercase : int = [1] * min_block_length
for n in count(__A ):
fill_count_functions.append(1 )
for block_length in range(__A ,n + 1 ):... | 607 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config i... | 607 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffuser... | 607 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def __snake_case ( __A ,__A ) -> Any:
lowercase : Any = int(__A )
assert noofclusters < len(__A )
# Find out the dimensionality
lower... | 607 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils impo... | 607 |
"""simple docstring"""
import qiskit
def __snake_case ( __A ,__A ) -> qiskit.result.counts.Counts:
lowercase : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
lowercase : int = qiskit.QuantumCircuit(4 ,2 )
# encod... | 607 | 1 |
"""simple docstring"""
def __snake_case ( ) -> list[list[int]]:
return [list(range(1000 - i ,-1000 - i ,-1 ) ) for i in range(1000 )]
lowerCAmelCase: Optional[Any] =generate_large_matrix()
lowerCAmelCase: Any =(
[[4, 3, 2, -1], [3, 2, 1, ... | 607 |
"""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 transforme... | 607 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_fe... | 607 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __snake_case ( __A ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the C... | 607 | 1 |
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