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 |
|---|---|---|---|---|
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import t... | 55 |
SCREAMING_SNAKE_CASE :List[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :Union[str, Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :int = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',... | 55 | 1 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(a_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod()
| 55 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase ( a_ = "isbn/0140328726" ) -> dict:
"""simple docstring"""
__A = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
i... | 55 | 1 |
# 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 by app... | 55 |
import requests
SCREAMING_SNAKE_CASE :List[str] = 'YOUR API KEY'
def UpperCAmelCase ( a_ , a_ = giphy_api_key ) -> list:
"""simple docstring"""
__A = "+".join(query.split() )
__A = F'''https://api.giphy.com/v1/gifs/search?q={for... | 55 | 1 |
from collections import Counter
from timeit import timeit
def UpperCAmelCase ( a_ = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def UpperCAmelCase ( a_ = "" ) ->... | 55 |
import itertools
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
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... | 55 | 1 |
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,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
'configuration_... | 55 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = ("dense.weight", "attention.self.query", "attention.self.... | 55 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tra... | 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise... | 55 | 1 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
... | 55 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE :int = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def UpperCAmelCase ( a_ = "mumbai" ) -> Generator[tuple[str, str], N... | 55 | 1 |
SCREAMING_SNAKE_CASE :Optional[Any] = range(2, 20 + 1)
SCREAMING_SNAKE_CASE :Optional[int] = [10**k for k in range(ks[-1] + 1)]
SCREAMING_SNAKE_CASE :dict[int, dict[int, list[list[int]]]] = {}
def UpperCAmelCase ( a_ , a_ , a_ , a_ ) ... | 55 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcess... | 55 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE :int = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCAmelC... | 55 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def UpperCAmelCase ( a_ = "laptop" ) -> DataFrame:
"""simple docstring"""
__A = F'''https://www.amazon.in/laptop/s?k={product}'''
__A = {
... | 55 |
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
@require_tokenizers
... | 55 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :List[str] = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFI... | 55 |
SCREAMING_SNAKE_CASE :int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def UpperCAmelCase ( ) -> int:
"... | 55 | 1 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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... | 55 | 1 |
def UpperCAmelCase ( a_ = 1_0_0_0 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , a_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 55 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 55 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark... | 55 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 55 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca im... | 55 |
from numpy import exp, pi, sqrt
def UpperCAmelCase ( a_ , a_ = 0.0 , a_ = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 55 | 1 |
from math import factorial
SCREAMING_SNAKE_CASE :dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if not isinstance(a_ , a_ ):
raise TypeError("Parameter number must... | 55 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 55 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
SCREAMING_SNAKE_CASE :List[Any] = {
# 1536-bit
5: {
'prime': int(
... | 55 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch... | 55 | 1 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = ["speech"]
def __init__( self : List[Any] ,*A : Optional[int] ,**A : List[str] ):
requires_back... | 55 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake... | 55 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 55 |
from math import sqrt
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
__A = True
# 0 and 1 are none primes.
if number <= 1:
__... | 55 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :Optional[Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autoforme... | 55 |
import os
def UpperCAmelCase ( ) -> Any:
"""simple docstring"""
__A = os.path.dirname(os.path.realpath(a_ ) )
__A = os.path.join(a_ , "triangle.txt" )
with open(a_ ) as f:
__A = f.readlines()
__A = []
f... | 55 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConf... | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE :Union[str, Any] = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE :List[str] ... | 55 | 0 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerat... | 1 |
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 ...test_configuration_common imp... | 55 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main... | 2 |
SCREAMING_SNAKE_CASE :List[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :Union[str, Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :int = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',... | 55 | 0 |
'''simple docstring'''
def A_( A : int):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_ind... | 3 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase ( a_ = "isbn/0140328726" ) -> dict:
"""simple docstring"""
__A = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
i... | 55 | 0 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
f... | 4 |
import requests
SCREAMING_SNAKE_CASE :List[str] = 'YOUR API KEY'
def UpperCAmelCase ( a_ , a_ = giphy_api_key ) -> list:
"""simple docstring"""
__A = "+".join(query.split() )
__A = F'''https://api.giphy.com/v1/gifs/search?q={for... | 55 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
... | 5 |
import itertools
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
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... | 55 | 0 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Optional[int] , UpperCamelCase__: int ):
SCREAMING_SNAKE_CASE__ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: List[A... | 6 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = ("dense.weight", "attention.self.query", "attention.self.... | 55 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
... | 7 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise... | 55 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : int , __snake_case : int ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
__A : Optional[Any] = str(bin(__sn... | 8 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE :int = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def UpperCAmelCase ( a_ = "mumbai" ) -> Generator[tuple[str, str], N... | 55 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 9 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcess... | 55 | 0 |
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
_lowerCAmelCase = False
class lowerCAmelCase_ ( unittest.TestCase ):
p... | 10 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __A ( A ):
'''simple docstring'''
@require_torch
def a__ (self ) -> Opti... | 11 |
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
@require_tokenizers
... | 55 | 0 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - ... | 12 |
SCREAMING_SNAKE_CASE :int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def UpperCAmelCase ( ) -> int:
"... | 55 | 0 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transfo... | 13 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ = {
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 14 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 55 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_arra... | 15 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 55 | 0 |
__A : Tuple = {str(digit): digit**5 for digit in range(1_0)}
def __a ( A__ : int ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A__ ) )
def __a ( ):
return sum(
number
for number in range(1000 , 1000... | 16 |
from numpy import exp, pi, sqrt
def UpperCAmelCase ( a_ , a_ = 0.0 , a_ = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 55 | 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
UpperCAmelCase_ : Dict = logging.get_logger(__n... | 17 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 55 | 0 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_SCREAMING_SNAKE_CASE = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Gri... | 18 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch... | 55 | 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_a... | 19 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake... | 55 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transform... | 20 |
from math import sqrt
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
__A = True
# 0 and 1 are none primes.
if number <= 1:
__... | 55 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCAmelCase_ : Tuple = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argumen... | 21 |
import os
def UpperCAmelCase ( ) -> Any:
"""simple docstring"""
__A = os.path.dirname(os.path.realpath(a_ ) )
__A = os.path.join(a_ , "triangle.txt" )
with open(a_ ) as f:
__A = f.readlines()
__A = []
f... | 55 | 0 |
'''simple docstring'''
import math
import qiskit
def snake_case_ (UpperCamelCase : int = 1 , UpperCamelCase : int = 1 , UpperCamelCase : int = 1 ):
'''simple docstring'''
if (
isinstance(UpperCamelCase , UpperCamelCas... | 22 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE :Union[str, Any] = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE :List[str] ... | 55 | 0 |
# 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 ... | 23 |
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 ...test_configuration_common imp... | 55 | 0 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _UpperCamelCase (_l... | 24 |
SCREAMING_SNAKE_CASE :List[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :Union[str, Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :int = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',... | 55 | 0 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 25 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase ( a_ = "isbn/0140328726" ) -> dict:
"""simple docstring"""
__A = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
i... | 55 | 0 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
__UpperCamelCase = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and R... | 26 |
import requests
SCREAMING_SNAKE_CASE :List[str] = 'YOUR API KEY'
def UpperCAmelCase ( a_ , a_ = giphy_api_key ) -> list:
"""simple docstring"""
__A = "+".join(query.split() )
__A = F'''https://api.giphy.com/v1/gifs/search?q={for... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Dict = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvN... | 27 |
import itertools
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
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... | 55 | 0 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 28 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = ("dense.weight", "attention.self.query", "attention.self.... | 55 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
return base * power(lowerCAmelCase__ ,(exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the power of exponent using recursion...""")
A_ = int(input("""Enter ... | 29 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise... | 55 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __a( _a ):
"""simple docstring"""
lowerCAmelC... | 30 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE :int = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def UpperCAmelCase ( a_ = "mumbai" ) -> Generator[tuple[str, str], N... | 55 | 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,
is_accel... | 31 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcess... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_A... | 32 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import Ima... | 33 |
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
@require_tokenizers
... | 55 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_ = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'con... | 34 |
SCREAMING_SNAKE_CASE :int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def UpperCAmelCase ( ) -> int:
"... | 55 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a ( ) -> Optional[Any]:
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTIO... | 35 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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... | 55 | 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'''
__lowerCamelCase : str =... | 36 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 55 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:... | 37 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 55 | 0 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
A_ : str = None
try:
import msvcrt
except ImportError:
A_ : Optional[int] = None
try:
import fcntl
except ImportError:
A_ : List[Any] = ... | 38 |
from numpy import exp, pi, sqrt
def UpperCAmelCase ( a_ , a_ = 0.0 , a_ = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 55 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipel... | 39 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 55 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils imp... | 40 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch... | 55 | 0 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowerCAmelCase__ = '''\
@misc{chen2021evaluating,
title={Ev... | 41 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake... | 55 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
lowerCamelCase_ = ''
for i in table:
res += inp[i - 1]
return res
def _UpperCamelCase ( __UpperCamelCase ) -> Tuple:
return data[1:] + data[0]
... | 42 |
from math import sqrt
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
__A = True
# 0 and 1 are none primes.
if number <= 1:
__... | 55 | 0 |
from math import pi, sqrt, tan
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CA... | 43 |
import os
def UpperCAmelCase ( ) -> Any:
"""simple docstring"""
__A = os.path.dirname(os.path.realpath(a_ ) )
__A = os.path.join(a_ , "triangle.txt" )
with open(a_ ) as f:
__A = f.readlines()
__A = []
f... | 55 | 0 |
'''simple docstring'''
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_mvp impor... | 44 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE :Union[str, Any] = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE :List[str] ... | 55 | 0 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations... | 45 |
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 ...test_configuration_common imp... | 55 | 0 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCAmelCase : List[str] = 300 # TEMPERATURE (unit = K)
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ) -> float:
'''simple ... | 46 |
SCREAMING_SNAKE_CASE :List[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :Union[str, Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :int = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',... | 55 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPrior... | 47 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase ( a_ = "isbn/0140328726" ) -> dict:
"""simple docstring"""
__A = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
i... | 55 | 0 |
'''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 A ... | 48 |
import requests
SCREAMING_SNAKE_CASE :List[str] = 'YOUR API KEY'
def UpperCAmelCase ( a_ , a_ = giphy_api_key ) -> list:
"""simple docstring"""
__A = "+".join(query.split() )
__A = F'''https://api.giphy.com/v1/gifs/search?q={for... | 55 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase__ ( snake_case_ :Dict ):
__UpperCAmelCase = {}
__UpperCAmelCase = job['''started_at''']
__UpperCAmelCase ... | 49 |
import itertools
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
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... | 55 | 0 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..... | 50 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = ("dense.weight", "attention.self.query", "attention.self.... | 55 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["torch", "transformers", "onnx"]
def __init__( self : Tuple , *a__ : Dict... | 51 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise... | 55 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list) -> float:
if not nums:
raise ValueError('''List is empty''')
return sum(a_) / len(a_)
if __name__ == "__main__":
import doctest
doctest.testmod() | 52 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE :int = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def UpperCAmelCase ( a_ = "mumbai" ) -> Generator[tuple[str, str], N... | 55 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
_snake_case : Dict = '1'
_snake_case : str = '0'
_snake_case : Optional[Any] = '1'
_snake_case : int = ort.SessionOptions()
_snake_case : Optional[Any] ... | 53 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcess... | 55 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
def __init__( s... | 54 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 56 |
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
@require_tokenizers
... | 55 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from... | 57 |
SCREAMING_SNAKE_CASE :int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def UpperCAmelCase ( ) -> int:
"... | 55 | 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 : Optional[Any] = logging.get_logger(__name__)
... | 58 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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... | 55 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 59 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 55 | 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_mvp import MvpTokenizer
lowerCA... | 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 55 | 0 |
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,
WavaVecaFeatu... | 61 |
from numpy import exp, pi, sqrt
def UpperCAmelCase ( a_ , a_ = 0.0 , a_ = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 55 | 0 |
snake_case = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
snake_case = ["""a""", """b""", """c""", """d""", """e"""]
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMIN... | 62 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 55 | 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,
)
a : List[str] = {"configuration_xg... | 63 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch... | 55 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowercase_ : int = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase_ ):
def __init__( self , *lowerCAmelCase , **lowerCAmelCase ) -> None:... | 64 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake... | 55 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 65 |
from math import sqrt
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
__A = True
# 0 and 1 are none primes.
if number <= 1:
__... | 55 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ..... | 66 |
import os
def UpperCAmelCase ( ) -> Any:
"""simple docstring"""
__A = os.path.dirname(os.path.realpath(a_ ) )
__A = os.path.join(a_ , "triangle.txt" )
with open(a_ ) as f:
__A = f.readlines()
__A = []
f... | 55 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case__ :list[int | float] , snake_case__ :int , snake_case__ :int ) -> int | float:
if len(snake_case__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= l... | 67 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE :Union[str, Any] = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE :List[str] ... | 55 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
"google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config... | 68 |
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 ...test_configuration_common imp... | 55 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Dict = logging.get_logger(__name__)
... | 69 |
SCREAMING_SNAKE_CASE :List[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :Union[str, Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :int = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',... | 55 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_... | 70 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase ( a_ = "isbn/0140328726" ) -> dict:
"""simple docstring"""
__A = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
i... | 55 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
Segf... | 71 |
import requests
SCREAMING_SNAKE_CASE :List[str] = 'YOUR API KEY'
def UpperCAmelCase ( a_ , a_ = giphy_api_key ) -> list:
"""simple docstring"""
__A = "+".join(query.split() )
__A = F'''https://api.giphy.com/v1/gifs/search?q={for... | 55 | 0 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageProcessingSa... | 72 |
import itertools
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
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... | 55 | 0 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_UpperCAmelCase , n - 1 , _UpperCAmelCase) * a) % mod
else:
SCREAMING_SNAKE_CASE = binar... | 73 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = ("dense.weight", "attention.self.query", "attention.self.... | 55 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def a__ ( snake_case="ro" , snake_case="en" , snake_case="wmt16" , snake_case=None ):
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError(... | 74 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise... | 55 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
UpperCamelCase__ = list[tuple[int, int]]
UpperCamelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0... | 75 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE :int = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def UpperCAmelCase ( a_ = "mumbai" ) -> Generator[tuple[str, str], N... | 55 | 0 |
"""simple docstring"""
a_ = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'd... | 76 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcess... | 55 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( __magic_name__ ):
@staticmethod
@abstractmethod
def a_ ( UpperCamelCase_ : ArgumentParser):
"""simple docstring"""
raise NotImplementedError()
... | 77 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __A ( UpperCamelCase__ ):
a__ : int = """MCTCTFeatureExtractor"""
a__ : int = """AutoTokenizer"""
def __init__(self : Di... | 78 |
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
@require_tokenizers
... | 55 | 0 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require... | 79 |
SCREAMING_SNAKE_CASE :int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def UpperCAmelCase ( ) -> int:
"... | 55 | 0 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : int = get_tests_dir("""fixtures/test_sent... | 80 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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... | 55 | 0 |
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