code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from math import isqrt, loga
def __snake_case ( UpperCAmelCase_ : int ):
lowerCamelCase_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCAmelCase_ , ... | 675 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : str ):
lowerCamelCase_ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( UpperCAmelCase_ : str ):
lowerCamelCase_ ... | 675 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@d... | 718 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCAmelCase__ ( ) -> Optional[Any]:
import os as original_os
from os import path as original_path
from os import rename as original_rename
... | 109 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase__ =logging.get_logger(__name__)
# TODO: upload to AWS
lowercase__ ={
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolv... | 263 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
lowercase__ =datasets.logging.get_logger(__name__)
lowercase__ ='\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title ... | 263 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __snake_case ):
'''simple docstring'''
_lowercase : int = ['''image_processor''', '''tokenizer''']
_lowercase : ... | 702 |
'''simple docstring'''
def A (__lowerCamelCase :int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divisor for di... | 162 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowercase ( _a ) -> List[Any]:
UpperCAmelCase_: Dict = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.dec... | 137 |
from math import pi
def lowercase ( _a ,_a ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10)) | 137 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
fr... | 206 |
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
return abs(__magic_name__ ) if a == 0 else greatest_common_divisor(b % a , __magic_name__ )
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
while y: # --> when y=0 then loop will terminate and retu... | 206 | 1 |
import argparse
from collections import defaultdict
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any )... | 635 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig... | 635 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowerCamelCase :Optional[Any] = logging.getLogger(__name__)
if is_torch_tpu_av... | 346 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metri... | 346 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_prope... | 197 |
'''simple docstring'''
import socket
def __snake_case ( ):
snake_case_ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case_ = socket.gethostname()
snake_case_ = 12_312
sock.connect((host, port) )
sock.send(b"Hello server!" )
with ope... | 508 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configura... | 500 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list ):
if len(UpperCAmelCase_ ) <= 1:
return lst
A__ = 1
while i < len(UpperCAmelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
A__ ... | 500 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ ... | 175 |
import os
import sys
import unittest
a_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_in... | 175 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slo... | 712 | import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(a ) , """Tatoeba direc... | 647 | 0 |
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 import GenerationTes... | 457 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase ) -> int:
"""simple docstring"""
snake_case__ : Any ... | 261 | 0 |
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
while a != 0:
_snake_case ,_snake_case : Tuple = b % a, a
return b
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
if... | 580 | import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ... | 580 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( snake_case_=None, ... | 416 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_to... | 316 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 525 | import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 525 | 1 |
# 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
#
# Unless required by applicab... | 513 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewToke... | 513 | 1 |
import os
import sys
import unittest
SCREAMING_SNAKE_CASE_:Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 520 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_:Optional[int] = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_tor... | 520 | 1 |
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
SCREAMING_SNAKE_CASE : Optional[int] = gray_code_sequence_string(lowercase )
#
# c... | 62 |
# 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
#
# Unless required ... | 62 | 1 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from... | 252 |
def snake_case__ ( lowerCAmelCase_ = 1000000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =limit + 1
SCREAMING_SNAKE_CASE =[0] * limit
for first_term in range(1, lowerCAmelCase_ ):
for n in range(lowerCAmelCase_, lowerCAmelCase_, lowe... | 252 | 1 |
'''simple docstring'''
__UpperCAmelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__Uppe... | 329 |
'''simple docstring'''
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 ... | 329 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image,... | 147 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 147 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = ("dense.weight", "attention.self.query", "... | 463 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A ={
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
... | 463 | 1 |
from __future__ import annotations
class snake_case__ :
'''simple docstring'''
def __init__( self , a__ , a__ ) -> Union[str, Any]:
'''simple docstring'''
__snake_case , __snake_case :... | 291 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 291 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
A__ = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
print... | 252 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A__ = logging.get_logger(__name__)
A__ = {'''v... | 252 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
__A = namedtuple("covid_data", "cases deaths recovered")
def lowerCamelCase_ ( UpperCamelCase__ : str = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
... | 716 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) ->... | 167 | 0 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=A__ ):
__A : Optional[Any] = ["""flax""", """transformers"""]
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(self ... | 32 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 32 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 409 |
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = [0] * len(snake_case__ )
lowercase_ = []
lowercase_ = []
lowercase_ = 0
for values in graph.values():
for i in values:
... | 409 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCamelCase_ ( A ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : int , _lowerCamelCase ... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.conf... | 719 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.... | 173 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : str = {
"""vocab_file""": """vocab.json""",
... | 568 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = os.path.join(args.tf_model_dir , "parameters.json" ... | 139 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 139 | 1 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_util... | 6 |
def A_ ( a ):
"""simple docstring"""
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
SCREAMING_SNAKE_CASE_ : Tuple = 4
SCREAMING_SNAKE_CASE_ : List[str] = (1 << p) - 1
for _ in ran... | 511 | 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
a_ = logging.get_logger(__name__)
a_ = ... | 523 |
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( __UpperCamelCase ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(__UpperCamelCase ) + 1 ) )
def __UpperCAmelCase ( __UpperCamelCase = 10**6 ):
__... | 523 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase =logging.get_logger(__name__)
UpperCamelCase ={
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-t... | 208 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class A ( SCREAMING_SNAKE_CASE__ ):
"""simple ... | 208 | 1 |
import unittest
from transformers import XLMConfig, 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 ModelTesterMixin, i... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
... | 223 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python util... | 223 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
requi... | 701 | import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 82 | 0 |
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[Any] , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = num_of_nodes
SCREAMIN... | 16 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.u... | 679 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE__ = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
SCREAMING_SNAKE_CASE__ = ... | 704 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 601 | 0 |
import math
def A ( __UpperCamelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number ... | 9 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCa... | 469 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamp... | 705 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathL... | 501 | 0 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class l... | 471 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->int:
'''simple docstring'''
__A : str = 1
for i in range(1 ,num + 1 ):
fact *= i
return fact
def __lowercase ( snake_case_ : int ) ->int:
'''simple do... | 177 | 0 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
SCREAMING_SNAKE_CASE__ = "."
# Internal TensorFlow ops that can be ... | 712 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 140 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
tra... | 34 |
def __snake_case ( ) -> int:
return 1
def __snake_case ( lowerCAmelCase_ ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __snake_case ( lowerCAmelCase_ ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) +... | 100 | 0 |
from __future__ import annotations
def A__ ( __lowerCamelCase ):
if not nums:
raise ValueError('''List is empty''' )
return sum(__lowerCamelCase ) / len(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 597 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 597 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase( a__ ,a__ ,a__ ... | 691 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCAmelCase__ ( unittest.TestCase ):
def lowercase ( self : List[str] ... | 718 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class lowerCAmelCase__ :
__a = 42
__a... | 430 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ : List[Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_ex... | 410 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {
'xlm-mlm-en-2048': 'https://hu... | 511 | 0 |
from __future__ import annotations
import math
def _snake_case ( SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
if num <= 0:
_lowerCAmelCase : Union[str, Any] = f'''{num}: Invalid input, please enter a positive integer.'''
r... | 503 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__UpperCAmelCase = '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.... | 503 | 1 |
import argparse
import os
import re
import packaging.version
__lowercase : Tuple ="""examples/"""
__lowercase : Optional[Any] ={
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.c... | 54 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'facebook/encodec_24khz': 'https://huggingface.co/facebook/enc... | 217 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from trans... | 700 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 0 |
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 lowerCamelCase( ... | 27 |
"""simple docstring"""
import sys
_snake_case = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 580 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
... | 713 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sen... | 631 | 0 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__lowerCamelCase : int = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 297 |
from itertools import count
def UpperCamelCase__ ( _A: int = 50 ):
'''simple docstring'''
__lowerCamelCase = [1] * min_block_length
for n in count(_A ):
fill_count_functions.append(1 )
for block_length in range(_A , ... | 479 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
... | 443 |
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int , __A : int ) -> float:
"""simple docstring"""
a_ : Optional[int] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
... | 443 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
a_ = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling... | 221 | import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ..... | 221 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCAmelCase = """src/transformers"""
# This is to make sure the trans... | 582 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.u... | 582 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_s... | 538 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a ( UpperCamelCase_ : Any ) -> List[str]:
snake_case__ =os.path.join(args.tf_model_dir , 'parameters.json' )
... | 538 | 1 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import Byt... | 713 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def _SCREAMING_SNAKE_CA... | 590 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config... | 590 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> tuple[int, int]:
try:
lowerCAmelCase__ : Union[str, Any] = float(lowerCAmelCase__ )
except ValueError:
raise ValueError('Please enter a valid number' )
lowerCAmelCase__ : int = decimal - int(lowerCAm... | 721 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( __magic_name__ ):
lowercase = (DDPMParallelScheduler,)
def _lowerCamelCase ( self : str , **a : Optional[i... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin': ['MaskForme... | 496 | """simple docstring"""
from heapq import heappop, heappush
import numpy as np
def snake_case__ ( _snake_case : np.ndarray , _snake_case : tuple[int, int] , _snake_case : tuple[int, int] , _snake_case : bool , ):
... | 516 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE( UpperCamelCase ,UpperCamelCase ) -> str:
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError('Invalid Input' )
UpperCAmelCase_ : int = sum(array[:k] )
for i in... | 704 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
impo... | 471 | 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-2.... | 473 | """simple docstring"""
UpperCamelCase = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_l... | 473 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 710 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (... | 322 | 0 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 443 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
... | 443 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 351 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A_ ( __a : str = "laptop" ):
"""simple docstring"""
a__ = F'''https://www.amazon.in/laptop/s?k={product}'''
a__ = {
"""User-Agent... | 351 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowercase : int ={"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__... | 54 |
import requests
def _A( UpperCamelCase__ : str , UpperCamelCase__ : str ) -> None:
'''simple docstring'''
__lowercase = {'''Content-Type''': '''application/json'''}
__lowercase = requests.post(UpperCamelCase__ , jso... | 332 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_ava... | 679 | import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mo... | 679 | 1 |
"""simple docstring"""
A_ = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_libro... | 391 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter pos... | 659 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
),
# See all Vivit models ... | 587 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, rando... | 587 | 1 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __A( a ):
def __init__( self , _snake_case="" , _snake_case="train" ) -> Union[str, Any]:
'''simple docstring'''
assert os.path.isdir(_snake_case ... | 219 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
A : Any = logging.get_logger(__name__)
class __A:
def __init__( self , _snake_case , _sn... | 219 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : int = 16 , a_ ... | 241 |
def a ( _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 10_00 ):
'''simple docstring'''
__UpperCAmelCase : List[str] = 1
__UpperCAmelCase : Dict = 0
for divide_by_number in range(_UpperCAmelCase , di... | 241 | 1 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __lowercase ( snake_case_ : Optional[int] ) ->str:
'''simple docstring''... | 177 |
"""simple docstring"""
import requests
a_ = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __lowercase ( snake_case_ : str ) ->None:
'''simple docstring'''
__A : str = requests.get(_NEWS_API + bbc_news_api_key ).json()... | 177 | 1 |
from __future__ import annotations
import queue
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , _A : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] =... | 702 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requir... | 131 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ = 50 ):
UpperCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 82 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""camembert-base""": """https://hu... | 451 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extractio... | 420 |
'''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... | 420 | 1 |
import random
class lowercase :
@staticmethod
def __snake_case( _UpperCamelCase : str ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [ord(_lowerCamelCase ) for i in text]
S... | 403 |
"""simple docstring"""
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__SCREAMING_SNAKE_CASE ... | 357 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Dict = [
'good first issue',
'feature request',
'wip',
]
def _UpperCAmelCase ( ) -> Union[str, Any]:
A_ = Github(os.environ['''G... | 719 | '''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusion... | 174 | 0 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class A__ ... | 494 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : Any = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Optional[Any] ... | 494 | 1 |
import copy
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
from ..auto import CONFIG_MAPPING
_UpperCAmelCase : Optional[int] = logging.get_l... | 704 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_... | 108 | 0 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproces... | 425 | """simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__SCREAMING_SNAKE_CASE =False
class UpperCamel... | 425 | 1 |
def __snake_case ( _UpperCamelCase ) -> float:
return 10 - x * x
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_UpperCamelCase ) * equation(_UpperCamelCase ) >= 0:
r... | 346 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 346 | 1 |
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 : List[Any] = logging.get_logger(__name__)
__UpperCamelCas... | 468 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
"SenseTime/deformable-detr": "https://huggingface.co/sen... | 468 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils ... | 707 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __lowercase ( a : str ) -> None:
__snake_case , __snake_case : List[Any] =analyze_text(a )
__snake_case : ... | 497 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Tuple = {"configuration_xglm": ["XGLM_PRETRAINED... | 193 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__magic_name__ = logging.get_logger(__name__)
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , *_snake_case... | 254 | 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__)
class snake_case__ ( __snake_case , __snake_case ):
_... | 716 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCamelCase : str = input('Enter image url: ').strip()
print(F"""Downloading image from {url} ...""")
lowerCamelCase : Optional[Any] = BeautifulSoup(requests.get(url).content, 'html.... | 303 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Mobi... | 2 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : int = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Llam... | 540 | 0 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_n... | 579 |
"""simple docstring"""
from timeit import timeit
def A__ ( A__ ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
_UpperCAmelCase = 0
while number:
number &= number - 1
result += 1
ret... | 579 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'huggingface/time-series-transformer-tourism-monthly': (
'htt... | 621 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 621 | 1 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import A... | 22 |
"""simple docstring"""
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_lowercase =... | 22 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 58 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""s... | 319 | 0 |
'''simple docstring'''
_lowercase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def lowerCamelCase__ ( a ):
__snake_case = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squar... | 427 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( a , a ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(a ):
print(f'{i}\t\t{d}' )
def lowerCamelCase__ ( a , a ,... | 427 | 1 |
def a ( ):
'''simple docstring'''
return 1
def a ( snake_case__: Optional[int] ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def a ( snake_case__: Optional[Any] ):
'''simple docstring''... | 97 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
SCREAMING_SNAKE_CASE_ : Union[str, Any] = '''http://www... | 375 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__A : str = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :str ,*_UpperCamelCase :List[str] ,**_UpperC... | 267 |
'''simple docstring'''
import random
from typing import Any
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
for _ in range(len(lowerCamelCase_ ) ):
snake_case_ : Union[str, Any] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
... | 267 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama... | 21 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> None:
'''simple docstring'''
_UpperCAmelCase = generate_pascal_triangle(__lowercase )
for row_idx in range(__lowercase ):
# Print left spaces
for _ in range(num_rows - ro... | 236 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase_ : Union[str, Any] = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
i... | 718 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
UpperCAmelCase_ : int = get_logger(__name__)
class lowercase__ ( enum.Enum ):
__UpperCamelCase = """all_checks"""
__UpperCamelCase ... | 440 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __lowerCamelCase ( __snake_case : Any, __snake_case : List[str]=(), __snake_case : ... | 215 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_... | 179 | 0 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from... | 715 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxrunt... | 248 | 0 |
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