code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-han... | 237 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass... | 26 | 0 |
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_flax_common import... | 608 |
from __future__ import annotations
__magic_name__ : List[Any] = 8.9_8_8e9 # units = N * m^s * C^-2
def lowerCAmelCase ( snake_case__ : float , snake_case__ : float , snake_case__ : float , snake_case__ : float )-> ... | 608 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , *A__ ,... | 342 |
'''simple docstring'''
import math
def __UpperCamelCase ( a : int ) ->list[int]:
snake_case = []
snake_case = 2
snake_case = int(math.sqrt(a ) ) # Size of every segment
snake_case = [True] * (end + 1)
snake_case = ... | 342 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCame... | 547 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int = 3 , _SCREAMING_SNAKE_CASE : int = 7 , _SCREAMING_SNAKE_CASE : int = 100_0000 ):
"""simple docstring"""
__a = 0
__a = 1
for current_denominator in range(1 , limit + ... | 547 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.util... | 252 |
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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 252 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int] , __magic_name__ :list[int] , __magic_name__ :list[int] , __magic_name__ :list[list[str]] , __magic_name__ :int , ):
UpperCAmelCase_ = len(__magi... | 407 |
from math import isqrt, loga
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __magic_name__ , ... | 407 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A ( lowerCamelCase__ ):
'''simple docstring'''
lowerCamelCase : str = (EulerDiscreteScheduler,)
low... | 226 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_comm... | 526 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddi... | 136 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
A : Union[str, Any] = TypeVar("""T""")
class lowerCAmelCase_ ( Generi... | 136 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _SCREAMING_SNAKE_CASE ( *lowerCAmelCase__ ):
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ : List[Any] = list... | 364 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowercase : int =logging.get_logger(__name__)
class UpperCamelCase_ ( snake_case__ ):
def __init__( self : Tuple , *lowerCamelC... | 364 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def __UpperCAmelCase ( UpperCamelCase__ :dict , UpperCamelCase__ :str , UpperCamelCase__ :set , UpperCamelCase__ :set , UpperC... | 574 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any ={
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Condition... | 574 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class A:
'''simple docstring'''
UpperCamelCase = 42 # [batch_size x 3]
UpperCamelCase = 42 # [batch_size x 3]
UpperCamelCase = 42 # [batch_size x 3]
... | 70 |
def _A ( lowerCamelCase = 200 ):
a__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
a__ : Dict = [0] * (pence + 1)
a__ : int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCamelCase , pence + 1 , 1 ):
num... | 112 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( ... | 717 |
from ...configuration_utils import PretrainedConfig
UpperCamelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-b... | 383 | 0 |
"""simple docstring"""
import argparse
import struct
import unittest
class lowercase :
def __init__( self , lowercase ) -> None:
lowerCAmelCase = data
# Initialize hash values
lowerCAmelCase = [
0X6A_09E_667,
... | 532 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
lowerCAmelCase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return ke... | 532 | 1 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path #... | 85 |
"""simple docstring"""
def lowercase__(A ) ->list[int]:
"""simple docstring"""
lowercase__ : List[str]= len(A )
for i in range(A ):
for j in range(i + 1 , A ):
if numbers[j] < numbers[i]:
... | 85 | 1 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
__lowerCamelCase = "us-east-1" # defaults region
@dataclass
class _snake_case :
'''simple docstring'''
UpperCamelCase__ =42
UpperCamelCase__ ="""arn:aws:iam::558105141721:role/sagemaker_execu... | 608 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"Gro... | 608 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : Union[str, Any] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
... | 146 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 146 | 1 |
__snake_case : str ='\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__snake_case : ... | 647 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 647 | 1 |
import torch
from torch import nn
class a__ ( nn.Module ):
def __init__( self :Union[str, Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Tuple , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[int]=... | 708 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
f... | 395 | 0 |
import math
def snake_case (UpperCamelCase : 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 False
#... | 165 |
import qiskit
def snake_case (UpperCamelCase : int = 2 ):
'''simple docstring'''
lowerCamelCase__ = qubits
# Using Aer's simulator
lowerCamelCase__ = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q regis... | 165 | 1 |
'''simple docstring'''
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... | 338 |
'''simple docstring'''
lowercase__ : List[Any] = '''Input must be a string of 8 numbers plus letter'''
lowercase__ : Optional[Any] = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def _lowerCAmelCase ( __snake_case : str ) -> bool:
if n... | 338 | 1 |
def a__ ( A_, A_ = " " ):
'''simple docstring'''
__magic_name__ = []
__magic_name__ = 0
for index, char in enumerate(UpperCamelCase__ ):
if char == separator:
split_words.append(string[last_index:index] )
__magic_name__ = ... | 529 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 506 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAm... | 706 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowerCAmelCase : Any = logging.get_logger(... | 432 | 0 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 533 |
'''simple docstring'''
import argparse
import datetime
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"... | 533 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : int =logging.get_logger(__name__)
A_ : List[str] ... | 606 | '''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def snake_case_ ( __snake_case : Tuple) -> str:
lowerCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 606 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class A__ :
def __init__( self , UpperCamelCase__=2 , UpperCamelCase__=3 , UpperCamelCase__=64 , UpperCamelCase__=... | 288 |
'''simple docstring'''
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featu... | 288 | 1 |
'''simple docstring'''
import qiskit
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
lowerCamelCase_ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
lowerCamelCase_ = qiskit.QuantumCi... | 718 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Con... | 445 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Dict = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/r... | 13 |
"""simple docstring"""
import os
def SCREAMING_SNAKE_CASE__ ( )-> Optional[Any]:
'''simple docstring'''
with open(os.path.dirname(snake_case ) + "/p022_names.txt" ) as file:
UpperCAmelCase__ : Tuple = str(file.readlines()[0] )
Uppe... | 438 | 0 |
'''simple docstring'''
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_cas... | 719 |
'''simple docstring'''
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
__magic_name__ : Dict = logging.get_logger(__name__)
__magic_name_... | 368 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeq... | 7 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
... | 412 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
... | 8 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE = parse(importlib.metadata.version('torch'))
def lowercase_ ( __A : Union[str, Version] , ... | 8 | 1 |
from __future__ import annotations
def lowercase__ ( A_: list[int] ) -> bool:
"""simple docstring"""
return len(set(A_ ) ) == len(A_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 68 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Any = [
'encoder.version',
'decoder.version',
'model.encoder.version',
... | 101 | 0 |
"""simple docstring"""
import os
import sys
import unittest
__A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_... | 366 | """simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__A ... | 366 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from... | 485 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class A_ ( ... | 485 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[Any] = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AutoformerConfig",
],
}
... | 707 | import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
A : List[str] = False
class lowerCamelCase (unittest.TestCase ):
... | 356 | 0 |
'''simple docstring'''
from string import ascii_uppercase
_UpperCAmelCase : List[str] = {str(ord(c) - 55): c for c in ascii_uppercase}
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
if isinstance(lowercase_ , lowe... | 72 |
"""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 = {
'''facebook/xmod-base''': '''https://... | 7 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Enco... | 703 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compressio... | 392 | 0 |
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 = "▁"... | 684 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvaila... | 684 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''onnx''']
def __init__( self : Dict , *__UpperCamelCase : Any , **__UpperCamelCase : Union[str, Any] ) -> Optional[int]:... | 706 | """simple docstring"""
def lowercase ( a__ : str , a__ : str ) -> float:
def get_matched_characters(a__ : str , a__ : str ) -> str:
_UpperCamelCase = []
_UpperCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumer... | 342 | 0 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
A_ : Dict = datasets.logging.get_logger(__name__)
A_ : Dict = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, A... | 196 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 196 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfi... | 695 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( u... | 695 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCAmelCase__ :
"""simple docstring"""
a = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
a = field(
default="./" , met... | 493 |
# 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 torch
from diffusers.configuration_utils import Config... | 493 | 1 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __lowercase ( snake_case, snake_case, snake_case = 1_0**-1_0 ):
"""simple docstring"""
__magic_name__ :Tuple = a
while True:
__mag... | 180 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''', ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''', ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize(''... | 180 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
__UpperCamelCase : Tuple = '''
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... | 4 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__UpperCAmelCase = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def lowercase__ ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str... | 642 | 0 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentTex... | 513 |
'''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_util... | 513 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _A ( UpperCAmelCase_ ):
@staticmethod
@abstractmethod
def a ( lowerCamelCase__ : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError()
@abstractmethod
def a ( ... | 269 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_... | 269 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
f... | 113 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A ={
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_... | 113 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResamp... | 657 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Optional[Any]... | 657 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ = {
'configuration_perceiver': ['PERCEIV... | 122 |
"""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... | 122 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsC... | 694 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 4000000 ) -> int:
_a : Optional[Any] =[]
_a , _a : Union[str, Any] =0, 1
while b <= n:
if b % 2 == 0:
... | 694 | 1 |
'''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_common i... | 709 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 502 | 0 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
... | 48 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
f... | 383 | 0 |
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
def _UpperCAmelCase (UpperCamelCase_ : Union[str, Any] ... | 713 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_log... | 196 | 0 |
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Ne... | 83 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
lowerCAmelCase__ = '''1'''
lowerCAmelCase__ = '''0'''
lowerCAmelCase__ = '''1'''
lowerCAmelCase__ = ort.SessionOptions()
lowerCAmelCase__ ... | 83 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
a_ : Optional[int] =(CMStochasticIterativeScheduler,)
a_ : Any ... | 708 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _lowerCAmelCase :
'''simple docstring'''
a_ : Optional[Union[str, Path]] =None
a_ : bool =False
a_ : bool ... | 669 | 0 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs t... | 296 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : list, UpperCamelCase__ : int ):
'''simple docstring'''
if len(UpperCamelCase__ ) <= 1 or n <= 1:
return
insert_next(UpperCame... | 296 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : Union[str, Any] = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolv... | 595 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_rembert": ["REMBER... | 595 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
fro... | 575 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : Optional[str] = field(
default='codeparrot/codeparrot' , metadata={'help': 'Model name or path of model to be trained.'} )... | 687 | 0 |
'''simple docstring'''
import argparse
import os
import re
lowercase__ : Optional[Any] = "src/diffusers"
# Pattern that looks at the indentation in a line.
lowercase__ : Any = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__ : Tuple ... | 719 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __lowerCamelCase ( _UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
for param in module.parameters():
UpperCAmelCase_ = False
def __lowerCame... | 43 | 0 |
import argparse
from collections import defaultdict
import yaml
__lowercase = '''docs/source/en/_toctree.yml'''
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[int] = defaultdict(__UpperCAmelCase )
for doc in mo... | 167 | """simple docstring"""
import math
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase = 1_0_0 ) -> int:
lowercase__: Dict = sum(i * i for i in range(1 , n + 1 ) )
lowercase__: int = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return squa... | 586 | 0 |
SCREAMING_SNAKE_CASE = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',... | 209 |
from math import pow
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_co... | 209 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avail... | 550 |
import os
from distutils.util import strtobool
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
for e in env_keys:
snake_case = int(os.environ.get(UpperCamelCase_ ,-1 ) )
if val >= 0... | 550 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIP... | 718 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
UpperCamelCase_ = logging.g... | 88 | 0 |
'''simple docstring'''
import numpy as np
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 120 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case : Optional[Any] = logging.get_logger('transformers.models.speecht5')
def snake_case_ (... | 377 |
'''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_comm... | 377 | 1 |
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, req... | 93 |
"""simple docstring"""
import re
def __A (_SCREAMING_SNAKE_CASE ) ->list:
"""simple docstring"""
return [char.split() for char in re.split(r'[^ a-z A-Z 0-9 \s]' , str_ )]
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Op... | 93 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> int:
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError("String lengths must match!" )
a_ : Optional[Any] = 0
for chara, chara in zip(... | 705 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
... | 370 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(lowerCamelCase__ ) * abs(lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doc... | 463 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 42 # [batch_size x 3]
lowerCAmelCase__ = 42 # [batch_size x 3]
lowerCAmelCase__ = 42 # [batch_size x 3]
lowerCAmelCase__ = 42... | 463 | 1 |
"""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... | 302 |
"""simple docstring"""
import sys
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Dict = len(_UpperCAmelCase )
A_ : int = [[0 for x in range(_UpperCAmelCase )] for x in range(_UpperCAmelCase )]
A_ : T... | 302 | 1 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : list[int] ) -> int:
"""simple docstring"""
if not nums:
return 0
_UpperCAmelCase = nums[0]
_UpperCAmelCase = 0
for num in nums[1:]:
_UpperCAmelCase ... | 32 |
"""simple docstring"""
__A : Optional[int] = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
fro... | 602 | 0 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class snake_case__ ( Up... | 216 |
'''simple docstring'''
_UpperCamelCase : Optional[int] = [
(1_000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def __UpperCAmelCase ( A ... | 216 | 1 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = """EncodecFeatureExtractor"""
snake_case ... | 330 | """simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCamelCase( a , a = "cpu" , a = None ):
__a = torch.load(a , map_location=a )
for k, v in tqdm(state_dict.items() ):
if not isinstance(a , tor... | 528 | 0 |
'''simple docstring'''
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
if len(lowerCAmelCase_ ) < 2:
return collection
def circle_sort_util(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> bool:
_snake_case : List[str] ... | 47 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : List[Any] = 0
if start < end:
... | 47 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from .... | 579 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterM... | 579 | 1 |
from __future__ import annotations
from typing import Any
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 0 ) -> None:
lowerCAmelCase__ = ... | 704 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaF... | 98 | 0 |
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_ ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCAmelCase__ ) )
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_ , l... | 114 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A__ ( _snake_case ):
lo... | 288 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_availabl... | 579 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''stu... | 579 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A__ : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperC... | 13 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
# TODO Update this
A__ : Tuple = {
"""facebook/esm-1b""": "... | 13 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class a__ ( _snake_case ):
"""simple docstring"""
def __init__( self :Optional[Any] , *lowercase... | 314 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
lowercase = int(number**0.5 )
return number == sq * sq
def __snake_case... | 314 | 1 |
import numpy
# List of input, output pairs
lowerCamelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCamelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase__ = [2, 4, 1, 5]
lowerCamelCase__ =... | 612 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
sk... | 612 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoi... | 711 |
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int )-> None:
"""simple docstring"""
if (direction == 1 and array[indexa] > arra... | 321 | 0 |
# Copyright 2022 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... | 219 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 219 | 1 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 398 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase):
"""simple docstring... | 398 | 1 |
'''simple docstring'''
def __snake_case ( lowerCAmelCase : str ):
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
__UpperCAmelCase = sorted(string.lower() )
return len(lowerCAmelCase ) == le... | 396 | '''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_UpperCamelCase : int = 'docs/source/en/_toctree.yml'
def __snake_case ( lowerCAmelCase : Union[str, Any] ):
__UpperCAmelCase = defaultdict(lowerCAmelCase )
__Up... | 396 | 1 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = prime_factors(__A )
if is_square_free(_... | 714 |
'''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 = {
"camembert-base": "https://huggingface.co/ca... | 13 | 0 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 626 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowercase ):
UpperCAmelCase__ = (UnCLIPScheduler,)
def _lowercase (self , **SCREAMING_SNAKE_CASE_ ):
""... | 626 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ... | 160 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _SCREAMING_SNAKE_CASE ( *UpperCamelCase ):
"""simple docstring"""
if not isinstance(UpperCamelCase , UpperCamelCase ):
lo... | 160 | 1 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ):
'''simple docstring'''
_a : int = AutoConfig.from_pretrained(... | 389 |
"""simple docstring"""
from collections.abc import Sequence
def lowerCAmelCase__ ( UpperCamelCase__ = None ):
'''simple docstring'''
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
_a : List[Any] = num... | 389 | 1 |
from __future__ import annotations
from typing import Any
class lowerCamelCase :
def __init__(self : Optional[int] , _A : int ) -> None:
snake_case = num_of_nodes
snake_case = []
snake_case = {}... | 294 |
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
from datasets.feature... | 294 | 1 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __magic... | 458 | """simple docstring"""
import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__:List[str] = """us-east-1""" # defaults region
@dataclass
class snake_case__ :
_snake_case : str
_snake_case : Optional[Any] = """arn:aws:iam::558105141721:role... | 528 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.... | 709 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transform... | 172 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsM... | 464 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenize... | 529 | 0 |
'''simple docstring'''
from math import factorial
def _SCREAMING_SNAKE_CASE( snake_case_ : int = 20 ) ->int:
'''simple docstring'''
_lowercase : Optional[int] = 2 * n # middle entry of odd rows starting at row 3 is the sol... | 411 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE( snake_case_ : float ) ->float:
'''simple docstring'''
if edge <= 0 or not isinstance(snake_case_ , snake_case_ ):
raise ValueError('''Length must be a positive.''' )
... | 411 | 1 |
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:
fro... | 33 |
'''simple docstring'''
lowerCAmelCase__ = 'Alexander Joslin'
import operator as op
from .stack import Stack
def __UpperCAmelCase ( lowerCamelCase_) -> int:
UpperCamelCase__ : List[str] = {'*': op.mul, '/': op.truediv, '+': op.add, ... | 596 | 0 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.def... | 232 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
ren... | 232 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( __lowercase : Optional[Any] , __lowercase... | 637 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"processing_mctct": ["MCTCTP... | 632 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 721 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 451 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__ : List[Any] = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/confi... | 497 |
'''simple docstring'''
import sys
from collections import defaultdict
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] ):
"""simple docstring"""
_lowercase = []
def snake_case ( self : Optional[Any] ... | 497 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __a ):
"""simple docstring"""
lowerCamelCase__: List[Any] =str(__a )
return len(__a ) == 9 and set(__a ) == set("123456789" )
def lowerCAmelCase_ ( ):
"""simple docstring"""
for base_num in ra... | 701 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__A = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class _SCREAMING_SNAKE_CASE... | 437 | 0 |
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
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 1 |
"""simple docstring"""
from collections import defaultdict
def UpperCAmelCase ( A__: int ) -> int:
__lowerCamelCase : int = 1
__lowerCamelCase : Optional[Any] = True
for v in tree[start]:
if v not in visited:
ret += dfs(A__ )
if... | 263 |
"""simple docstring"""
import random
from typing import Any
def UpperCAmelCase ( A__: list ) -> list[Any]:
for _ in range(len(A__ ) ):
__lowerCamelCase : List[Any] = random.randint(0 , len(A__ ) - 1 )
__lowerCamelCase : Optional[... | 263 | 1 |
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