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 |
|---|---|---|---|---|
# 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... | 166 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
A__ = logging.get_logger(__name__)
A__ = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at h... | 166 | 1 |
from math import sqrt
def lowerCamelCase__ ( lowercase = 1000000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : int
while num_cuboids <= limit:
max_cuboid_size += ... | 714 |
import warnings
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
snake_case = logging.get_logger(__name__)
snake_case = {
"""nvidia/... | 488 | 0 |
'''simple docstring'''
A_ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
A_ = [{"type": "code", "content": INSTALL_CONTENT}]
A_ = ... | 143 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
fr... | 143 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_att... | 246 | from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase( __lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(__lowerCamelCase , __lowerCamelCase ) -> bool:
... | 246 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 472 |
'''simple docstring'''
import argparse
import copy
def _A ( UpperCAmelCase ):
'''simple docstring'''
A__ = {}
with open(UpperCAmelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
A__ = ... | 531 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_jso... | 586 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention... | 586 | 1 |
"""simple docstring"""
import numpy as np
import qiskit
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ = 8, SCREAMING_SNAKE_CASE__ = None ) -> str:
a_ : Any = np.random.default_rng(seed=SCREAMING_SNAKE_CASE__ )
# Roughly 25% of the qubits will contribute to the key.
... | 237 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case_ ( a_ ):
__lowerCA... | 237 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value... | 398 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int ):
'''simple docstring'''
if not isinstance(A__ , A__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
lowerCAmelCase_ : List[str] = ... | 398 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly... | 464 |
'''simple docstring'''
from typing import Any
class A :
def __init__( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self ) -> ... | 325 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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_... | 706 |
"""simple docstring"""
def snake_case__ ( _SCREAMING_SNAKE_CASE ) ->Tuple: # noqa: E741
UpperCAmelCase__ = len(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ = 0
UpperCAmelCase__ = [0] * n
UpperCAmelCase__ = [False] * n
UpperCAmelCase__ = [False] * n
... | 422 | 0 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
_UpperCAmelCase = [True] * (num + 1)
_UpperCAmelCase = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , nu... | 657 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = {
"en": "Machine learning is grea... | 657 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ... | 718 |
import argparse
import json
import subprocess
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : List[str] , UpperCamelCase : Tuple ) -> Dict:
"""simple docstring"""
a_ = []
a_ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {toke... | 403 | 0 |
from __future__ import annotations
import math
def __UpperCAmelCase ( __A ) -> list[int]:
'''simple docstring'''
if num <= 0:
UpperCAmelCase__ = F"""{num}: Invalid input, please enter a positive integer."""
r... | 475 |
import math
import unittest
def __UpperCAmelCase ( __A ) -> bool:
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 475 | 1 |
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 1_000 ) -> int:
_snake_case : List[Any] = 2**power
_snake_case : Optional[Any] = 0
while n:
_snake_case : Optional[int] = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(in... | 708 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ... | 198 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ (UpperCamelCase : Callable[[int | float], int | float] , UpperCamelCase : int | float , UpperCamelCase : int | float , UpperCamelCase : int = 1... | 22 |
'''simple docstring'''
def lowerCamelCase_ ( A_ = 60_08_51_47_51_43 ):
try:
__lowerCamelCase = int(A_ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or... | 316 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from tra... | 42 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identifie... | 42 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A : List[... | 343 |
from typing import Dict, Optional
import numpy as np
import datasets
__A : Any = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentati... | 343 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__ : Optional[Any] = list[list[int]]
# assigning initial values to the grid
UpperCamelCase__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
... | 178 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase_ ( _l... | 178 | 1 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : int | float | str ) -> tuple[int, int]:
try:
_lowercase = float(SCREAMING_SNAKE_CASE_ )
except ValueError:
raise ValueError("""Please enter a valid number""" )
_lowercase = ... | 287 |
from __future__ import annotations
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more ... | 287 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a : Dict = logging.get_logger(__name__)
__a : Any = {""... | 559 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizati... | 559 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
a_ :Optional[int] = True
except ImportEr... | 478 |
import requests
SCREAMING_SNAKE_CASE = "" # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE = "https://api.openweathermap.org/data/2.5/"
def snake_case__ ( __SCREAMING_SNAKE_CASE = "Chicago" , __SCREAMING_SNAKE_CASE = APPID ) -> dict:
return requests.get(UR... | 579 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def a__ ( snake_case ) -> List[Any]:
"""simple docstring"""
if not isinstance(snake_case , snake_case ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
rais... | 712 |
from math import pi, sqrt
def a__ ( snake_case ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(snake_case ) not in (0, 0.5):
raise NotImplemen... | 131 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowerCamelCase : Optional[int] = False
class A__ ... | 405 |
from maths.prime_check import is_prime
def _SCREAMING_SNAKE_CASE ( lowercase : int ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
lowerCamelCase_ = f"""Input value of [number={number}] must be an integer"""
... | 70 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Optional[Any] = ['''speech''']
def __init__( self, *A, ... | 708 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx... | 508 | 0 |
'''simple docstring'''
class a_ :
def __init__( self ):
_lowerCAmelCase : Optional[int] = 0
_lowerCAmelCase : Any = 0
_lowerCAmelCase : List[Any] = {}
def __UpperCamelCase ( self , snake_case_ ):
... | 384 |
'''simple docstring'''
UpperCamelCase_ = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def _UpperCAmelCase ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError(""... | 384 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.js... | 365 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowercase ( ):
__A : List[Any] = ArgumentParser(
description=(
'PyTorch TPU distributed tra... | 365 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
UpperCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is ... | 702 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
exc... | 217 | 0 |
"""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 impor... | 218 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase__(A ) ->list[list[float]]:
"""simple docstring"""
lowercase__ : str= Decimal
# Check if the prov... | 218 | 1 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 705 |
def A ( UpperCAmelCase ):
if n == 1 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
return 0
elif n == 2:
return 1
else:
_snake_case : List[Any] = [0, 1]
for i in range(2 , n + 1 ):
... | 278 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
print('''Making key files...''' )
make_key_files('''r... | 14 | def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
'''simple docstring'''
_enforce_args(UpperCamelCase_ , UpperCamelCase_ )
if n == 0:
return 0
UpperCamelCase = float("""-inf""" )
for i in range(1 , n + 1 ):
UpperCamelCase ... | 537 | 0 |
from PIL import Image
def snake_case__ ( _SCREAMING_SNAKE_CASE ) ->Image:
UpperCAmelCase__ = image.size
UpperCAmelCase__ = 0
UpperCAmelCase__ = image.load()
for i in range(lowercase_ ):
for j in range(lowercase_ ):
UpperCAmelCase__ = pixels[j, i]
... | 720 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_... | 422 | 0 |
"""simple docstring"""
def UpperCAmelCase ( A : Optional[Any] , A : Any ):
'''simple docstring'''
_UpperCAmelCase = [0 for i in range(r + 1 )]
# nc0 = 1
_UpperCAmelCase = 1
for i in range(1 , n + 1 ):
# to com... | 573 |
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():
... | 687 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Union[str, Any] = 'ClapFeatureExtractor'
lowercase : List[Any] =... | 58 | '''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tran... | 58 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffuser... | 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
Uppe... | 312 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 312 | 1 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import... | 650 |
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 import ModelTesterMixin, ids_ten... | 80 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase__ ( __lowerCamelCase ):
""... | 73 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAme... | 73 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
A_: Dict = logging.getLogger(__name__)
class _lowercase ( A_ ):
"""simple docstring"""
def __init__( se... | 398 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConfig''',
'''SwiftFormerOnnx... | 157 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .atten... | 342 | """simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import T... | 342 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class _a ( SCREAMING_SNAKE_CASE ):
... | 28 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ : Tuple = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_... | 682 | 0 |
'''simple docstring'''
import enum
import shutil
import sys
_lowerCAmelCase , _lowerCAmelCase : Tuple = shutil.get_terminal_size()
_lowerCAmelCase : List[Any] = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class lowerCAmelCase ( enum.Enum ):
_lowerCamelCase :... | 646 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Union[str, Any] = get_tests_dir('fixture... | 646 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
loggi... | 79 |
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
from torchvision.t... | 79 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 100 * 2**20, 900 * 2**20] )
def lowerCamelCase... | 488 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.... | 488 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_util... | 551 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
... | 551 | 1 |
_UpperCAmelCase = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
_UpperCAmelCase = ['''a''', '''b''', '''c''', '''d''', '''e''']
def UpperCamelCase ( __lowercase : Dict ,__lowercase : Tuple ,__lowercase : Union[str, Any] ... | 710 | def UpperCamelCase ( __lowercase : str ,__lowercase : int ):
'''simple docstring'''
A_ : int = word.split()
def justify(__lowercase : list ,__lowercase : int ,__lowercase : int ) -> str:
A_ : Optional[Any] = max_width - width... | 70 | 0 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBer... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
def a_ (_lowerCAmelCase : int )-> Tup... | 164 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase ( __snake_case ):
__lowerCamelCase = ['image_processor', 'tokenizer']
__lowerCamelCase = 'AutoImageProcessor'
__lowerCamelCase = 'AutoT... | 164 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transform... | 261 |
'''simple docstring'''
def _A ( snake_case__ : list[int] , snake_case__ : list[int] ):
snake_case__ : Tuple = len(snake_case__ )
print('''The following activities are selected:''' )
# The first activity is always selected
snake_case__ : Optional[Any] ... | 261 | 1 |
def lowerCAmelCase_ ( snake_case_ ):
_A : Union[str, Any] = len(snake_case_ )
for _ in range(snake_case_ ):
for i in range(_ % 2,arr_size - 1,2 ):
if arr[i + 1] < arr[i]:
_A : Tuple = arr[i + 1], arr[i]
ret... | 710 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_be... | 54 | 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 : Union[str, Any] = logging.get_logger(__name__) # pylint:... | 444 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : int ... | 444 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def snake_case_ ( lowercase__ : str ):
'''simple docstring'''
_lowerCAmelCase =analyze_text(snake_case__ )
_lowerCAmelCas... | 706 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 149 | 0 |
"""simple docstring"""
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... | 434 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : str ={'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
ex... | 434 | 1 |
def _lowerCamelCase ( snake_case , snake_case ):
def get_matched_characters(snake_case , snake_case ) -> str:
_lowerCAmelCase = []
_lowerCAmelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumer... | 225 | from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils imp... | 225 | 1 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = [[] for _ in range(SCREAMING_SNAKE_CASE__ )]
snake_case_ = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if k... | 39 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import ... | 535 | 0 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple ):
"""simple docstring"""
__snake_case = psutil.Process()
__snake_case =... | 680 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 | 1 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
lowerCamelCase = """1"""
lowerCamelCase = """0"""
lowerCamelCase = """1"""
lowerCamelCase = ort.SessionOptions()
lowerCamelCase = ort.GraphOptimizati... | 474 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''... | 373 | 0 |
'''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_warmup... | 713 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def __snake_case ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int ):
lowerCamelCase_ = [0] * no_of_processes
lowerCamelCase_ = ... | 445 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _SCREAMING_SNAKE_CA... | 107 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_SCREAMING_SNAKE_CASE =str(bin(_UpperCamel... | 405 | 0 |
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
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"vocab_file": "sen... | 715 | # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _a ( low... | 594 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils impo... | 88 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase_ :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : list[tuple[float, float]] ) -> Optional[int]:
lowerC... | 133 | 0 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def UpperCAmelCase ( snake_case : int , snake_case : str , snake_case : Union[str, Any] , snake_case : List[Any] ):
_lowerCAmelCase:... | 439 |
"""simple docstring"""
import baseaa
def UpperCAmelCase ( snake_case : str ):
return baseaa.aaaencode(string.encode('''utf-8''' ) )
def UpperCAmelCase ( snake_case : bytes ):
return baseaa.aaadecode(snake_case ).decode('''utf-8''' )
if __n... | 439 | 1 |
from __future__ import annotations
_snake_case : Union[str, Any] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class a :
"""simple docstring"""
def __in... | 81 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
def __init__( self :Union[str, Any] ... | 468 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : List[str] = TypeVar('''T''')
class UpperCAmelCase__ ( Generic[T] ):
"""simple docstring"""
def __init__( self : Optional[Any] , __lowerCamelCase : List[Any] ) -> ... | 708 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...token... | 472 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence,... | 26 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
d... | 26 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a__ ( UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] ="""Speech2TextFeatureExtractor"""
UpperCAmelCas... | 446 |
from __future__ import annotations
import numpy as np
def __lowercase ( _A ) -> tuple[np.ndarray, np.ndarray]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = np.shape(_A )
if rows != columns:
SCREAMING_SNAKE_CASE : int... | 446 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> bool:
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
__lowerCamelCase : Union[str, Any] = F'Input value of [number={number}] must be an integer'
... | 13 |
__lowerCamelCase = """Tobias Carryer"""
from time import time
class UpperCAmelCase :
def __init__(self : Dict , snake_case__ : Optional[Any] , snake_case__ : Optional[Any] , snake_case__ : str , snake_case__ : Optio... | 204 | 0 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = len(_snake_case ) + 1
UpperCamelCase = len(_snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of... | 711 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSche... | 544 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
UpperCAmelCase__ : Optional[int]... | 410 |
'''simple docstring'''
def _lowercase ( UpperCamelCase__ : dict ):
__A : Dict = set()
# edges = list of graph's edges
__A : Any = get_edges(UpperCamelCase__ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and ... | 365 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTester... | 718 | '''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 605 | 0 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1337 , num_exampl... | 156 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 156 | 1 |
a__ : str = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ... | 714 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if b == 0:
return (1, 0)
((lowercase__), (lowercase__)) = extended_euclid(A__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _lowerCAmelCase ( A__ , A__... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenization_m2m_100": ["M2... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__UpperCAmelCase = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIV... | 308 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _A , _A = 0 ):
lowerCAmelCase_ = length or len(_A )
lowerCAmelCase_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
lowerCAmelCase_ , l... | 717 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 325 | 0 |
from math import factorial, radians
def A__( __lowerCAmelCase , __lowerCAmelCase = 18 , __lowerCAmelCase = 10 ):
_snake_case : str = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
_snake_case : Dic... | 304 |
from collections import defaultdict
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : str = first_str.lower().strip()
_snake_case : Dict = second_str.lower().strip()
# Remove whitespace
_snake_case : Dict = first_str.repla... | 304 | 1 |
'''simple docstring'''
def _A ( A__ = 1000000 ):
"""simple docstring"""
__lowercase = set(range(3 , A__ , 2 ) )
primes.add(2 )
for p in range(3 , A__ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , ... | 624 |
'''simple docstring'''
def _A ( A__ = 50 ):
"""simple docstring"""
__lowercase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
way... | 624 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : int = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig... | 8 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCAmelCase = False
... | 90 | 0 |
"""simple docstring"""
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_ = {
"kssteven/ibert-roberta-b... | 713 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case_ ( ... | 370 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils i... | 149 |
from __future__ import annotations
from collections import namedtuple
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float ):
__lowercase : str = namedtuple("""result""" , """na... | 149 | 1 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
... | 374 |
"""simple docstring"""
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ) ->Tuple:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCa... | 374 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : List[str] = iter(UpperCAmelCase_)
while True:
snake_case__ : Tuple ... | 648 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_: Optional[Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': [... | 648 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
clas... | 501 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowerCamelCase ( _lowerCamelCase ):
'''simple... | 501 | 1 |
'''simple docstring'''
import json
import sys
def a_ ( __snake_case : Optional[int] , __snake_case : Any ) -> Any:
"""simple docstring"""
with open(_UpperCamelCase , encoding='''utf-8''' ) as f:
lowerCamelCase_ =json.load(_UpperCamelCase )
... | 676 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : int = 'opena... | 319 | 0 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCAmelCase : int = "."
# Internal TensorFlo... | 284 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCAmelCase : Tuple = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification}... | 284 | 1 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def lowerCamelCase ( _snake_case : List[Any] ):
'''simple docstring'''
lowercase__ = tf.convert_to_tensor(_snake_case )
lowercase__ ... | 267 |
'''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... | 267 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRC... | 307 |
from maths.prime_factors import prime_factors
def UpperCamelCase__ ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
_a : Optional[Any] = F'Input value of [number={number}... | 307 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = len(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = []
for i in range(len(_UpperCAmelCase) - pat_len + 1):
SCREAMING_SNAKE_CASE = True
for j in range(_UpperCAmelCase):
... | 73 | '''simple docstring'''
import operator as op
snake_case = '''scaler.pt'''
snake_case = '''pytorch_model'''
snake_case = '''random_states'''
snake_case = '''optimizer'''
snake_case = '''scheduler'''
snake_case = '''pytorch_model.bin'''
snake_case = '''pytorch_model.bin.index.json... | 309 | 0 |
__UpperCAmelCase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def A__ ( __lo... | 710 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 597 | 0 |
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 import ModelTesterMixin... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor i... | 2 | 1 |
"""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_comm... | 707 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : Optional[Any]=False ):
__a : Dict = Omeg... | 63 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
... | 362 | 1 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklear... | 292 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class snake_case__ :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase : List[Any] = {}
def __lowerCA... | 292 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( snake_case ):
def __init__( self : Optional[Any] , lowerCamelCase__ : List[Any]=None , **low... | 348 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __magic_name__ ( nn.Module ):
... | 348 | 1 |
'''simple docstring'''
from PIL import Image
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Image:
"""simple docstring"""
def brightness(__lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= ... | 721 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import l... | 219 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get... | 80 |
UpperCAmelCase__ = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def a_ () -> None:
"""simple docstring"""
__a : Dict = input("Enter message: " )
__a : Dict = input("Enter key [alphanumeric]: " )
__a : str = ... | 351 | 0 |
def _lowercase ( lowerCamelCase__ : Any, lowerCamelCase__ : Any ):
_a = [1]
for i in range(2, lowerCamelCase__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
_a = []
_a = li... | 708 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
f... | 691 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class _... | 11 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = []
self.adlist.append(
{'value': '', 'ne... | 542 |
'''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 ...test_configuration_comm... | 542 | 1 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : int , *_Uppe... | 39 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(rev... | 644 | 0 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 712 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWith... | 422 | 0 |
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