code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_u... | 109 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils impo... | 296 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, rand... | 266 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( _lowercase : int) -> list[int]:
"""simple docstring"""
if num <= 0:
a__ : Tuple = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueE... | 266 | 1 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
a_ = False
try:
a_ = _is_package_available('goog... | 175 | import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __lowercase ( lowerCamelCase : str , lowerCamelCase : str , **lowerCamelCase : List[Any] ):
UpperCamelCase_ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase ... | 175 | 1 |
from pathlib import Path
import fire
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
a = Path(__A )
a = Path(__A )
dest_dir.mkdir(exist_ok=__A )
for path in src_dir.iterdir():
a = ... | 357 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 256 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 256 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = len(_A ) // 2
# choose the middle 3 elements
lowerCAmelCase_ = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
... | 363 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 167 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class A_ :
'''simple docstring'''
UpperCAmelCase_ : Optional[Union[str, Path]] = None
UpperCAmelCase_ ... | 151 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class A_ :
'''simple docstring'''
UpperCAmelCase_ : Optional[Union[str, Path]] = None
UpperCAmelCase_ ... | 151 | 1 |
import argparse
import os
import re
import packaging.version
snake_case_ = '''examples/'''
snake_case_ = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s+=\s... | 363 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def snake_case__ ( SCREAMING_SNAKE_CASE_ : BertModel , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
... | 216 | 0 |
"""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,
StableDif... | 144 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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
from ... | 116 | 0 |
"""simple docstring"""
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_... | 371 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase : Node | None = None
UpperCAmelCase ... | 76 | 0 |
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
SCREAMING_SNAKE_CASE__ = [
'EAGER',
'AOT_EAGER',
'INDUCTOR',
'NVFUSER',
'AOT_NVFUSER',
... | 325 |
'''simple docstring'''
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_... | 267 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:... | 159 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
"kssteven/ibert-rober... | 159 | 1 |
'''simple docstring'''
__snake_case ={str(digit): digit**5 for digit in range(10)}
def a_ ( lowerCamelCase : int ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase ) )
def a_ ( ):
return sum(
number
for num... | 4 |
'''simple docstring'''
import os
__snake_case ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def a_ ( lowerCamelCase : str ):
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(lowerCamelCas... | 4 | 1 |
'''simple docstring'''
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class a_ ( TensorF... | 355 |
'''simple docstring'''
import numpy as np
def UpperCAmelCase_ ( __lowerCamelCase : str ,__lowerCamelCase : Optional[Any] ,__lowerCamelCase : int ,__lowerCamelCase : List[str] ,__lowerCamelCase : List[Any] ):
lowercase_ :Dict = int(np.ceil((x_end - xa) ... | 147 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if n == 1 or not isinstance(_A , _A):
return 0
elif n == 2:
return 1
else:
_a = [0, 1]
for i in range(2 , n + 1):
sequence.append(sequence[i - 1] + s... | 211 | '''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__lowercase = logging.get_logger(__name__)
__lowercase = '''T5Config'''
class a__( lowerCAmelCas... | 272 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImagePr... | 37 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A ( ctypes.Structure ):
"""simple docstring"""
lowerCamelCase = [('size', ctypes.c_int... | 7 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class __A (snake_case__):
'''simple docstring'''
def __init__( self : ... | 347 | 0 |
import numpy as np
import datasets
__SCREAMING_SNAKE_CASE = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introdu... | 256 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def UpperCAmelCase ( _lowerCamelCase , ... | 256 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A : int = logging.get_logger(__name__)
class A (__UpperCAmelCase ):
'''simple docstring'''
def __init__( self : str , *__lowerCAmelCase : ... | 274 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( _UpperCAmelCase ... | 167 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice... | 370 | import math
def UpperCamelCase ( __lowercase : int = 1_00 ):
'''simple docstring'''
A_ : List[Any] = sum(i * i for i in range(1 ,n + 1 ) )
A_ : int = int(math.pow(sum(range(1 ,n + 1 ) ) ,2 ) )
return sq... | 192 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""camembert-base""": """https://hug... | 343 | import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UN... | 343 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers... | 365 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only a... | 99 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _snake_case( SCREAMING_SNAKE_CASE__ : Any ) -> str:
'''simple docstring'''
A__ = prime_factors(_a )
if is_square_free(_a ):
... | 7 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 0 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = 'T5Config'
class UpperCamelCase_... | 353 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_... | 193 | 0 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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... | 159 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCAmelCase ( lowerCAmelCase_ :str , lowerCAmelCase_ :str = "cpu" , lowerCAmelCase_ :Union[str, None] = None )->None:
'''simple docstring'''
snake_case_ ... | 159 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/convnextv... | 350 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_... | 1 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# ful... | 324 |
a : Optional[int] = 9.8_0_6_6_5
def lowerCAmelCase_ (lowerCAmelCase__: float , lowerCAmelCase__: float , lowerCAmelCase__: float = g ):
"""simple docstring"""
if fluid_density <= 0:
raise ValueError("""Impossible... | 147 | 0 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowercase ( a__ : np.ndarray , a__ : np.ndarray , a__ : np.ndarray , a__ : int , a__ : int ) -> np.ndarray:
_UpperCamelCase ... | 367 | """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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProce... | 54 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_lo... | 37 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = abs(UpperCamelCase )
lowerCAmelCase__ : List[Any] = 0
while n > 0:
res += n % 10
n //= 10
return res... | 37 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transfor... | 302 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(... | 302 | 1 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
snake_case_ = {"""UserAgent""": UserAgent().random}
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase ... | 78 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE ... | 260 | 0 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
loggi... | 51 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __magic_name__ ( Te... | 51 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Dict = '''MCTCTFeatureExtractor'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = ... | 206 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeniza... | 192 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from fl... | 23 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_a = logging.get_logger(__name__)
class... | 23 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils imp... | 93 |
import math
import random
def A_ ( A__ , A__ = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowercase : Optional[Any] = 0.02
def A_ ( A__ , A__ ) -> float:
a__ ... | 99 | 0 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def UpperCAmelCase ( lowerCamelCase_ :str = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def UpperCA... | 8 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 1 |
import pprint
import requests
UpperCamelCase__ = 'https://zenquotes.io/api'
def lowerCAmelCase_ ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def lowerCAmelCase_ ( ) -> ... | 65 |
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_devic... | 193 | 0 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :int = 9.8_0665
def UpperCAmelCase_ ( __lowercase : float , __lowercase : float , __lowercase : float = g ) -> float:
'''simple docstring'''
if fluid_density <= 0:
raise... | 156 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list ) -> list:
'''simple docstring'''
for i in range(len(__lowercase ) - 1 , 0 , -1 ):
_UpperCAmelCase = False
for j in range(__lowercase , 0 , ... | 156 | 1 |
"""simple docstring"""
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, l... | 179 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(snake_case_ , x % y )
def lowerCAmelCase_ ( snake_case_ : int , ... | 1 | 0 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = [
'''word_embeddings_layernorm.weight''',
... | 364 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 0 |
"""simple docstring"""
import math
def lowercase ( A_ )-> Union[str, Any]:
'''simple docstring'''
a : Any = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowerCAmelCase_ )
... | 40 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return x + 2
class UpperCamelCase_ ( unittest.TestCase):
... | 54 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 356 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAv... | 57 | 0 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def lowerCamelCase__ ( _A ):
a : Dict = np.max(__lowerCAmelCase , axis=-1 , keepdims=__lowerCAmelCase )
a : Optional[Any] = np.exp(outputs - maxes )
return s... | 297 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCamelCase__ = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Transl... | 234 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) -> Optional[Any]:
A_ : Any = data
... | 164 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAnd... | 164 | 1 |
def A (__A : int = 1000 ) -> int:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = 1, 1
UpperCAmelCase_ = []
for i in range(1 , n + 1 ):
UpperCAmelCase_ = ... | 51 |
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,
)
snake_case_ : Dict = {"configuration_mbart"... | 51 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
if nth_term == "":
return [""]
A_ : Optional[Any] = int(_UpperCamelCase )
A_ : Optional[int] = int(_UpperCamelCase )
A_ : Dict = []
f... | 359 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if len(SCREAMING_SNAKE_CASE ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE )]
A_ : Any = []
def generate(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Dict = [0] * n
res.append(tuple(SCREAMING_SNAKE_CASE... | 65 | 0 |
'''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_image_i... | 23 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test,... | 23 | 1 |
from collections.abc import Sequence
def lowerCamelCase__ ( UpperCamelCase__ : Sequence[float] , UpperCamelCase__ : bool = False ) -> float:
'''simple docstring'''
if not arr:
return 0
_snake_case = 0 if allow_empty_subar... | 295 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase_ = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui W... | 295 | 1 |
from collections import Counter
from timeit import timeit
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE_... | 8 |
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(SCREAMING_SNAKE_CASE__ )
else:
if x == 0: ... | 8 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
... | 26 |
import math
def _a ( a :int = 100 ) -> int:
a = sum(i * i for i in range(1 , n + 1 ) )
a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f... | 26 | 1 |
from collections import deque
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : int , _snake_case : str , _snake_case : int , _snake_case : int ):
__lowercase : Optional[int] = process_name... | 156 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
Image... | 156 | 1 |
from __future__ import annotations
from math import pi
def _snake_case ( A , A , A ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 353 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _snake_case ( A , A , A , A=5 ) -> List[str]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
... | 228 | 0 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowercase_ ( a__ ):
def __init__( self , a , a ):
super().__init__()
se... | 80 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCh... | 34 | 0 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 85 | """simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase (__UpperCamelCase : dict , __UpperCamelCase : str , __UpperCamelCase : set , __UpperCamelCase : set , __UpperCamelCase : dict , __UpperCamelCase ... | 85 | 1 |
'''simple docstring'''
import argparse
import os
import re
_lowercase : int = "src/transformers"
# Pattern that looks at the indentation in a line.
_lowercase : int = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_lower... | 93 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 57 | 0 |
def lowerCamelCase__ ( A__ : str , A__ : str = " " ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(A__ ):
if char == separator:
split_words.append(stri... | 29 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase_ = datasets.utils.logging.get_logger(__name__)
class lowerCamelCase__( folder_based_builder.FolderBasedBuilderConfig):
UpperCAmelCa... | 29 | 1 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class A :
def __init__( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
... | 164 |
'''simple docstring'''
def _A ( lowercase__ ):
lowercase__ = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowercase__ = True
for i in range(0 , len(lowercase__ ) - 1 , 2... | 164 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.... | 360 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 299 | 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_opt': ['OPT_PRETRAINED_CONFIG_ARCHIV... | 29 | from __future__ import annotations
class A :
def __init__(self : Union[str, Any] , __UpperCAmelCase : list[list[int]] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = TypeError(
"Matrices must be for... | 65 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(UpperCAmelCa... | 337 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensor... | 337 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowerCAmelCase = namedtuple('''covid_data''', '''cases deaths recovered''')
def _lowerCamelCase( lowercase__ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
'''simple docstring'... | 295 |
from math import isqrt
def _lowerCamelCase( lowercase__ ) -> bool:
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase__ ) + 1 ) )
def _lowerCamelCase( lowercase__ = 1_0**6 ) -> int:
'''simple docstring'''
__... | 295 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..m... | 351 |
import unittest
import numpy as np
import requests
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():
... | 178 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
_snake_case = TypeVar("T")
class lowercase ( Generic[T] ):
def __init__( self , _a ) -> None:
_A : List[str] = data
_A : int = ... | 26 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase ( UpperCamelCase__ ):
_a = (DPMSol... | 26 | 1 |
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.testing_utils import DUMMY_UNKNOWN_IDENTIFI... | 284 | import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 284 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import ... | 137 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 0 |
"""simple docstring"""
import json
import sys
def A_ ( snake_case_ : List[Any] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : Optional[int] = json.lo... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict ... | 27 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_SCREAMI... | 85 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
parser.add_a... | 85 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Flax... | 368 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unittest.TestCase ):
@pro... | 279 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 29 |
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : list[list[int]] = [[0 for _ in range(__snake_case )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase_ : Optional[A... | 29 | 1 |
"""simple docstring"""
from math import factorial, radians
def lowerCAmelCase_( lowercase_ : float , lowercase_ : int = 18 , lowercase_ : int = 10 ) -> float:
_lowerCamelCase = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)
... | 73 |
"""simple docstring"""
import qiskit
def lowerCAmelCase_( lowercase_ : int , lowercase_ : int ) -> qiskit.result.counts.Counts:
_lowerCamelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
... | 73 | 1 |
from __future__ import annotations
class lowercase__:
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : int ) -> None:
lowercase_ = data
lowercase_ = None
lowercase_ = None
def ... | 30 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : Optional[Any] = logging.get_logger(__name__)
a : Tuple = "T5Config"
... | 311 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/g... | 161 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase__ =... | 161 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b, 2 ) for a, b in zip(_UpperCamelCase, _UpperCamelCase ) ) ... | 337 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Tuple = HfArgumentParser(_UpperCamelCase )
lowercase : List[str] = parser.parse_args_in... | 337 | 1 |
import string
from math import logaa
def a_ ( __lowercase : str , __lowercase : str ) -> Optional[int]:
_snake_case = document.translate(
str.maketrans('' , '' , string.punctuation ) ).replace('\n' , '' )
_snake_case = document... | 363 |
from __future__ import annotations
_lowerCamelCase : Optional[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase : Optional[int] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def a_ ( __lowercase : list[float] ) -> ... | 130 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
A : int = tuple[int, int]
class _lowercase :
"""simple docstring"""
def __init__( self : List[Any] , __lowerCamelCase : Any , __low... | 184 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.mo... | 178 | 0 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__UpperCamelCase : Union[str, Any] = logging.get_logger(__na... | 351 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if resistance == 0:
return {"r... | 74 | 0 |
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError('String lengths must match!' )
__lowerCAmelCase = 0
for chara, chara in zip(lowerCAmelCase_, lowerCAmelCase_ ):
if chara != chara:
... | 284 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected', [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
({'num_shards': ... | 284 | 1 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithN... | 358 | """simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __UpperCAmelCase... | 30 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class __UpperCamelCase :
def __init__( self ):
'''simple docstring'''
__a : list[Any] = []
__a : int = 0
__a : ... | 27 |
'''simple docstring'''
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 (_SCREAMING_SNAKE_CASE : Dict ):
... | 27 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
_lowerCAmelCase ={}
d... | 359 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__A = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Met... | 341 | 0 |
"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
... | 96 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = ''''''
lowerCamelCase_ : str = (
... | 279 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : int = {
"""configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG... | 352 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a (_lowerCAmelCase ):
"""simple docstring"""
... | 134 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a ={
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25,
"""L""": 4.03,
... | 73 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
__lowerCamelCase : int = sum(
cash_... | 73 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logg... | 306 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as P... | 306 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..u... | 161 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if... | 161 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
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_backbone_common i... | 370 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DO... | 55 | 0 |
UpperCAmelCase_ : Optional[Any] = 6_5521
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> int:
"""simple docstring"""
a_ : Dict = 1
a_ : Dict = 0
for plain_chr in plain_text:
a_ : List[Any... | 32 |
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 __lowerCamelCase ( lowerCamelCase__ , lowerC... | 130 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageV... | 367 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfi... | 126 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, ... | 74 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__:List[Any] = logging.get_logge... | 363 | """simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEm... | 268 | 0 |
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 snake_case_ ( self: List[str] ):
... | 310 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase ... | 192 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BridgeTowerConfig""",
... | 192 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowercase__ : Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
"""simple docstring"""
def __init__( self... | 328 |
'''simple docstring'''
__lowerCAmelCase = [
(1_000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def __SCREAMING_SNAKE_CASE ( _SCREA... | 341 | 0 |
"""simple docstring"""
def __lowercase ( _a , _a ):
return base * power(_a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
lowercase__ : Optional[Any] = int(input('''Enter... | 155 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 155 | 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,
)
__lowercase = {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFI... | 43 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCamelCase :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : MutableSequence[float] ) ->... | 134 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [8, 5, 9, 7]
SCREAMING_SNAKE_CASE_ : int = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0]... | 69 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configur... | 69 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __lowerCamelCase ( ) -> tuple[list[int], int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [randint(-10_00 ... | 306 |
from __future__ import annotations
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = len(snake_case__ ) - 1
while i < j:
... | 306 | 1 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase_ : List[str] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segment... | 365 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 10 ) -> str:
if not isinstance(lowercase , lowercase ) or n < 0:
raise ValueError("Invalid input" )
_a = 10**n
_a = 2_8433 * (pow(2 , 783_0457 ... | 346 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
SCREAMING_SNAKE_CASE_ = 1_0_0
SCREAMING_SNAKE_CASE_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
SCREAMING_SNAKE_CASE_ = 42
for prime in range(3, ceil(NUM_PRIMES*... | 296 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNe... | 55 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as n... | 358 | 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
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/efficient... | 78 | 0 |
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