code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowercase ( __snake_case : Dict ):
lowercase_ : Optional[int] = [
'''encoder.version''... | 231 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...te... | 231 | 1 |
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/convnextv2-tiny-1k-224""": """ht... | 703 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( __lowerCamelCase = "laptop" ):
SCREAMING_SNAKE_CASE_ = F'''https://www.amazon.in/laptop/s?k={product}'''
SCREAMING_SNAKE_CASE_ = {
'''User-Agent''': '''Mozilla/5.0 (X11;... | 597 | 0 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_att... | 2 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__A : Tuple = TypeVar('T')
class __UpperCamelCase ( Generic[T] ):
def __init__( self :Optional[Any] ,_UpperCamelCase :T ):
... | 334 | 0 |
"""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, ... | 701 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
gr... | 487 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a_ ( lowerCamelCase : Callable , lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float ... | 133 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( lowerCamelCase : Union[str, Any] , lowerCamelC... | 133 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 670 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from... | 424 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@r... | 189 | 0 |
import math
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int ):
'''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 are not primes
return Fals... | 601 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
"""simple docstring"""
lowerCAmelCase__ =... | 601 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__A : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
... | 499 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
fro... | 499 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Any = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE... | 21 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTes... | 21 | 1 |
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
_UpperCAmelCase = len(a__ )
_UpperCAmelCase = [[False for _ in range(m + 1 )] for _ in rang... | 618 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _UpperCamelCase ( __A , __A ) -> List[Any]:
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_lowerCAmelCase ... | 711 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( a__ , unitte... | 223 | 0 |
'''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,
MobileViTImag... | 109 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import... | 341 | 0 |
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 trans... | 715 |
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 .... | 649 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__UpperCamelCase : int = {
"""facebook/maskformer-s... | 328 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase = logg... | 474 | 0 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
fro... | 101 |
"""simple docstring"""
from math import isqrt, loga
def __A ( a_ :int) -> list[int]:
__a : int = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , a_ , ... | 101 | 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... | 403 | class lowercase : # Public class to implement a graph
def __init__( self : Union[str, Any] , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[bool]] ) -> None:
'''simple docstring'''
... | 403 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
__magic_name__ :int
__magic_name__ :TreeNode | None = None
__m... | 560 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__A = {"""UserAgent""": UserAgent().random}
def __A (_SCREAMING_SNAKE_CASE ) ->dict:
"""simple docstring"""
lower... | 560 | 1 |
def lowerCamelCase__ ( _lowerCamelCase ) ->str:
_UpperCAmelCase =0
# if input_string is "aba" than new_input_string become "a|b|a"
_UpperCAmelCase =""
_UpperCAmelCase =""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(_lowerCamelCase )... | 408 |
from __future__ import annotations
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase ) ->bool:
if len(_lowerCamelCase ) == 0:
return False
_UpperCAmelCase =len(_lowerCamelCase ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_lis... | 408 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def lowerCamelCase__ ( a ):
__snake_case , __snake_case = np.shape(a )
if rows != columns:
__snake_case = (
'\'table\' has to be of square shaped arr... | 427 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( a , a ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(a ):
print(f'{i}\t\t{d}' )
def lowerCamelCase__ ( a , a ,... | 427 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils... | 325 |
'''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 import M... | 325 | 1 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
... | 536 | """simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 / sqrt(2 ) ):
"""simple docstring"""
A__... | 536 | 1 |
lowerCamelCase__ : dict[tuple[int, int, int], int] = {}
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,... | 12 |
lowerCamelCase__ : dict[tuple[int, int, int], int] = {}
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,... | 12 | 1 |
from math import pi, sqrt, tan
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : float ) -> float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNA... | 688 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 688 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {'''vocab_file''': '''... | 677 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 677 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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_f... | 692 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
''... | 692 | 1 |
"""simple docstring"""
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
# Return True if there is node that has not iterated.
UpperCamelCase : List[str] = [False] * len... | 102 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
__magic_name__ : Dict = logging.getLogger(__name__)
@dataclass
c... | 102 | 1 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
__lowercase = BeautifulSoup(requests.get(lowercase , params=lowercase ).content , '''html.parser''' )
__lowe... | 522 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a : List[str] = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTokenize... | 522 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase_ : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 64 |
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 UpperCamelCase ( lowercase__ ):
'''simple do... | 257 | 0 |
from __future__ import annotations
__magic_name__ = tuple[int, int, int]
__magic_name__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__magic_name__ = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# -------------------------- de... | 314 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
... | 314 | 1 |
'''simple docstring'''
import os
from distutils.util import strtobool
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : List[Any] ) -> Optional[int]:
for e in env_keys:
__snake_case = int(os.environ.get(lowerCAmelCase__ , -1 ) )
... | 69 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__SCREAMING_SNAKE_CASE : Dict =HfArgumentParser(InitializationArguments)
__SCREAMING_SNAKE_CASE : Optional[Any] =parser.pa... | 428 | 0 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase_ = "src/transformers"
# This is to make sure the transformers module impo... | 701 |
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE ( a_ : Optional[Any] , a_ : int , a_ : Tuple , a_ : Tuple ):
__a = {
'en': 'Machine learning ... | 490 | 0 |
def _A ( lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
while second != 0:
lowerCAmelCase__ = first & second
first ^= second
lowerCAmelCase__ = c << 1
return first
if __name__ ... | 61 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCAmelCase_ : Optional[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:]... | 692 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class A__ :
lowerCamelCase__ : List[str]
lowerCamelCase__ : Optio... | 707 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets i... | 336 | 0 |
__SCREAMING_SNAKE_CASE : str = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def snake_case (__lowercase , __lowercase , __lowercase , __lowercase ) -> List[str]:
... | 670 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 670 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import Poly... | 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifie... | 441 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils... | 24 | '''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTok... | 494 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.u... | 716 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCamelCase( _a ):
lowercase_ : int = ["""image_processor""", """tokenizer"""]
lowercase_ : List[str] = """CLIPImageProcessor"""
... | 354 | 0 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, sl... | 125 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
# TODO Update this
A = {
'''facebook/esm-1b''': '''https://huggingface... | 125 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...ut... | 399 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = '''▁'''
_lowerCAmelCase ... | 399 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : float | Decimal , lowerCAmelCase__ : float = 10**-10 ... | 695 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase , ... | 91 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class _lowerCamelCase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCAmelCase__ : str ... | 700 |
import heapq
import sys
import numpy as np
a_ = tuple[int, int]
class _lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ):
__UpperCamelCase = []
__UpperCamelCase = set()
def snake_case ( self :... | 375 | 0 |
def A__ ( snake_case_ : Dict , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int ):
if height >= 1:
move_tower(height - 1 , snake_case_ , snake_case_ , snake_case_ )
move_disk(snake_case_ , ... | 64 | from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase_ : Tuple = TypeVar('T')
class _lowerCamelCase ( Generic[T] ):
def __init__( self , lowerCAmelCase , lowerCAmelCase ) -> None:
SCREAMING_SNAKE... | 64 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class lowerCAmelCase ( __a ):
'''simple docstring'''
def __init__( self : int , *__a : Dict , *... | 649 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : list[int] ):
if not nums:
return 0
__lowercase : Tuple = nums[0]
__lowercase : Tuple = 0
for num in nums[1:]:
__lowercase ... | 649 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''sentencepiece.bpe.model'''}
... | 463 |
"""simple docstring"""
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_... | 617 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a__... | 98 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class a__ :
'''simple docstring'''
def __init__( self ) -> None:
lowerCAmelCase__ = [2, 1, 2, -1]
lowerCAmelC... | 98 | 1 |
from ..utils import DummyObject, requires_backends
class A ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : str , *_UpperCAmelCase : Tuple , **_UpperCAmelCa... | 15 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_S... | 225 | 0 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __UpperCamelCase : List[Any] ) -> str:
"""simple docstring"""
snake_case__ :... | 700 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE (lowercase__ ):
A__ = 'ClapFeatureExtractor'
A__ = ('RobertaTokenizer', 'RobertaTokenizerFast')
def _... | 574 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 109 |
"""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"""... | 163 | 0 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSchedule... | 668 | """simple docstring"""
lowerCAmelCase_: Union[str, Any] = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
1_3_3,
1_1_1,
... | 668 | 1 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __snake_case ( _lowerCAmelCase ... | 454 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_I... | 454 | 1 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a_ = '''src/diffusers'''
# Matches is_xxx_available()
a_ = re.compile(r'''is\_([a-z_]*)_available... | 48 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thread... | 48 | 1 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase__ :
def __init__( self : List[str] , lowerCamelCase : int = 0 ):
'''simple docstring'''
a__ = key
def __a ( self : List[str... | 489 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 342 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
SCREAMING_SNAKE_CASE : Union[str, Any] = """
import os
"""
SCREAMING_SNAKE_CASE : Optional[int] = """
def foo():
import os
return False
"""
SCREAMING_SNAKE_CASE : Union[str, Any] ... | 525 | import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 525 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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 import BackboneTest... | 81 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'''Intel/dpt-large''': '''https://hu... | 414 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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_imag... | 705 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__UpperCAmelCase = logging.get_logger(__name__)
class a_( lowe... | 259 | 0 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Any = (boundary[1] - boundary[0]) / steps
snake_case_ : List[Any] = boundary[0]
snake_case_ : Tuple = ... | 60 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 applicabl... | 124 | 0 |
import math
def lowercase__ ( _UpperCamelCase) -> list[int]:
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = 2
UpperCamelCase = int(math.sqrt(_UpperCamelCase)) # Size of every segment
UpperCamelCase ... | 410 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 410 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE_ = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relie... | 465 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = {
"""en""": """Machine learning is great, isn't it?""",
... | 465 | 1 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def _A ( _a : Optional[Any] ):
"""simple docstring"""
A , A = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i... | 255 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
UpperCAmelCase =get_logger(__name__)
UpperCAmelCase =R"\n Args:\n input_ids (`jnp.ndarray` of... | 255 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = ["""torch""", """torchsde"""]
def __init__( ... | 247 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__UpperCamelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase (SCREAMING_SNAKE_CASE_ : list[float] ) ... | 247 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 60_08_51_47_51_43 ) -> int:
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE = int(_SCREAMING_SNAKE_CASE )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or ... | 702 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", ... | 116 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transfo... | 256 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( snake_case ):
lowerCamelCase_ = (CMStochasticIterativeScheduler,)
lowerCamelCase_ = 1_0
def _UpperCAmelCase ( ... | 256 | 1 |
from __future__ import annotations
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ): # This function is recursive
'''simple docstring'''
lowerCAmelCase : List[Any] = len(SCREAMING_SNAKE_CASE__ )
# If the array contains only one ... | 693 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Union[str, Any] ={
'configuration_roformer': ['ROFO... | 693 | 1 |
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
while a != 0:
lowerCamelCase , lowerCamelCase = b % a, a
return b
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
... | 623 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeni... | 706 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class ... | 589 | 0 |
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = 0
__A = len(a_ )
for i in range(n - 1 ):
for j in range(i + 1 , a_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def... | 55 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( UpperCamelCase="ro" , UpperCamelCase="en" , UpperCamelCase="wmt16" , UpperCamelCase=None ):
"""simple docstring"""
try:
import datasets
... | 565 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
a =logging.get_logger(__name__)
class A_ ( SCREAMING_SNAKE_CASE ):
def __init__( self : Tuple ,*SCREAMING_SNAKE_CASE__ : Any ... | 708 |
import os
import pytest
from attr import dataclass
a ="""us-east-1""" # defaults region
@dataclass
class A_ :
_UpperCAmelCase : str
_UpperCAmelCase : Tuple = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_UpperCAmelCase : ... | 337 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ ... | 34 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock imp... | 34 | 1 |
"""simple docstring"""
def _lowerCamelCase ( lowerCamelCase__ : list ):
lowercase__ : Dict = 0
while len(lowerCamelCase__ ) > 1:
lowercase__ : int = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
lowercase__ : Dict ... | 128 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
__snake_case = '\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.\n... | 128 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a ( __UpperCAmelCase ):
def __init__( self : Dict , *snake_case__ : Optional[Any] , **snake_case__ : int ):... | 611 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}... | 611 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase : str = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIV... | 293 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase : Tuple = "."
# In... | 293 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Any = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'X... | 57 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import ... | 260 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowerCAmelCase_: Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 668 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_: Union[str, Any] = {
"configuration_distilbert": [
... | 668 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__lowerCamelCase ... | 96 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
f... | 50 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transfor... | 706 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__a = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight... | 310 | 0 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tran... | 467 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : float )-> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
... | 650 | 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... | 702 |
import fire
from utils import calculate_rouge, save_json
def _UpperCAmelCase ( UpperCamelCase: Any , UpperCamelCase: Union[str, Any] , UpperCamelCase: List[Any]=None , **UpperCamelCase: Optional[int] ):
"""simple docstring"""
__lowerCAmelCase = ... | 376 | 0 |
'''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(imports)
_a :... | 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, l... | 450 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__A : Dict = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__A : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
def lowerCamelC... | 450 | 1 |
'''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_log... | 688 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_util... | 688 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = "▁"
__... | 129 |
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowercase):
__SCREAMING_SNAKE_CASE : Optional[int] = ["""keras_nlp"""]
def __init__( self : Optional[int] , *__UpperCamelCase : List[Any] , **__UpperCamelCa... | 129 | 1 |
A__: Optional[int] = 6_5521
def lowerCAmelCase_ ( A_):
UpperCamelCase__: Tuple = 1
UpperCamelCase__: Any = 0
for plain_chr in plain_text:
UpperCamelCase__: Optional[int] = (a + ord(A_)) % MOD_ADLER
UpperCamelCase__: Any = ... | 380 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A__: Any = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''... | 380 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}
try:
if not is_torch_available():
raise Opti... | 245 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import flo... | 245 | 1 |
from timeit import timeit
lowerCAmelCase_ = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure ou... | 39 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-43... | 39 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default... | 79 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from ... | 79 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE :str = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPe... | 236 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Paddin... | 236 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import A... | 554 |
"""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_i... | 554 | 1 |
import qiskit
def _lowerCAmelCase ( UpperCamelCase__: int = 2 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
A = qubits
# Using Aer's simulator
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q... | 641 |
_lowercase : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "... | 641 | 1 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_... | 327 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap... | 327 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 255 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[str]:
'''simple docstring'''
A__ = len(SCREAMING_SNAKE_CASE_ )
for i in range(length - 1 ):
A__ = i
for k in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
... | 514 | 0 |
from __future__ import annotations
_lowercase = "Muhammad Umer Farooq"
_lowercase = "MIT"
_lowercase = "1.0.0"
_lowercase = "Muhammad Umer Farooq"
_lowercase = "contact@muhammadumerfarooq.me"
_lowercase = "Alpha"
import re
from html.parser import HTMLParser
from url... | 526 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_lowercase = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert-large-v1": "https://huggingface.co/albert-la... | 526 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Tuple = ["torch", "transformers", "onnx"]
def __init__( self : Optional[int] , *_lowercase : int , **_lo... | 49 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeli... | 502 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__A : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowerCAmelCa... | 705 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 126 | 0 |
import operator
def A__ ( __A : str , __A : int = False , __A : Any = None ) ->List[Any]:
__A =operator.lt if reverse else operator.gt
__A =solution or []
if not arr:
return solution
__A =[arr.pop(0 )]
for i, ... | 184 |
'''simple docstring'''
def lowerCamelCase__ ( a ):
assert (
isinstance(a , a ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
return 1
__snake_case , __... | 356 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 309 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
a__ : Optional[Any] = """
import os
"""
a__ : Optional[Any] = """
def foo():
import os
return False
"""
a__ : Tuple = """
def foo():
def... | 309 | 1 |
__a :List[str] = 6_5521
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = 1
A_ = 0
for plain_chr in plain_text:
A_ = (a + ord(__UpperCamelCase )) % MOD_ADLER
A_ = ... | 86 |
import math
def a__ ( A_ ):
'''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 are not primes
return False
# All p... | 529 | 0 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : Optional[int] ):
'''simple docstring'''
return " ".join(
''''''.join(word[::-1] ) if len(_UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
... | 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_snake_case : List[Any] = {'configuration_beit': ['BEIT_PRETRAINED_CON... | 377 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available... | 252 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase = " " ) -> list:
"""simple docstring"""
snake_case__ : str = []
snake_case__ : int = 0
for index, char in enumerate(__lowerCAmelCase ):
if char == separator:
... | 252 | 1 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accele... | 640 |
'''simple docstring'''
import heapq
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
lowercase_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be fil... | 640 | 1 |
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