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 __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mo... | 674 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__lowerCAmelCase = TypeVar("""T""")
__lowerCAmelCase = TypeVar("""U""")
class lowerCamelCase_ ( Generic[T, U] ):
def __init__( self , lowerCamelCase_ ... | 147 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def __a ( A__ : Callable[[int | float], int | float] , A__ : int | float , A__ : int | float , A__ : int = 100 , ):
SCREAMING_SNAKE_CASE = x_start
SCREAMING... | 698 |
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 __a ( A__ : List[str] ):
... | 698 | 1 |
"""simple docstring"""
a :Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def _lowercase ( __lowerCAmelCase ) -> Optional[Any]:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_snake_case ) )
def _lowercase ( ) -> ... | 680 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallba... | 110 | 0 |
from sklearn.metrics import recall_score
import datasets
a_ = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""
a_ = ... | 622 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str = " " ):
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__... | 622 | 1 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> Dict:
"""simple docstring"""
def is_in_circle(_SCREAMING_SNAKE_CASE : ... | 71 |
'''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,
Auto... | 71 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision... | 712 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _UpperCAmelCase ( lowerCAmelCase__):
... | 485 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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 j... | 107 |
'''simple docstring'''
import sys
from collections import defaultdict
class A :
def __init__( self : Any ):
"""simple docstring"""
lowerCAmelCase__ = []
def __SCREAMING_SNAKE_CASE ( self : List[str] , __magic_name__ : ... | 48 | 0 |
from math import isqrt
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
_UpperCAmelCase : int = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 328 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassificat... | 328 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_A: str = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIV... | 126 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
snake_case_ : List[Any] = logging.getLogger()
d... | 714 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import Ba... | 292 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_t... | 539 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( a_ : str = "" ):
__a = url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__a = Beautiful... | 539 | 1 |
import numpy as np
def lowerCAmelCase_ ( snake_case_ : np.ndarray , snake_case_ : np.ndarray , snake_case_ : float = 1E-1_2 , snake_case_ : int = 1_00 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(snake_ca... | 716 | '''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __A :
def __init__(self : Dict , __a : Any ):
UpperCAmelCase_ = data
UpperCAmelCase_ = None
class __A :
... | 415 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_swi... | 73 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class UpperCAmelCase__ ( ... | 438 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_avai... | 438 | 1 |
'''simple docstring'''
_A: Dict = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def _l... | 126 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase =... | 137 | 0 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformer... | 715 |
"""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
if is_torch... | 386 | 0 |
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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
log... | 590 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''... | 36 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def _lowerCAmelCase( ):
UpperCAmelCase , UpperCAmelCase = ... | 1 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ :Optional[int] = get_logger(__name__)
class lowercase ( enum.Enum ):
lowerCamelCase : str = '''all_checks'''
lowerCamelCase : str ... | 35 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowercase : Any = {
"""tiny.en""": """https://openaipublic.azureedge.net/main/whisper/models/d3d... | 336 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor im... | 73 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
__UpperCAmelCase : Dict = '''ClapFeatureExtractor'''
__UpperCAmelCase : ... | 73 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, l... | 38 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ ( snake_case , snake_case , snake_c... | 143 | 0 |
import math
def UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__A = [True] * n
__A = False
__A = False
__A = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
__A = ... | 205 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : str ={
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE... | 205 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTest... | 331 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a : int = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfi... | 700 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'Th... | 200 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
class snake_case ( a_ ):
def __init__( self : List[Any] , *a_ : Tuple , **a_ : ... | 85 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
__A= np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
__A= np.exp(outputs - maxes )
return shifte... | 186 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( snake_case__ ):
def __init__( self : int , snake_case__ : Tuple , snake_case__ : int )... | 708 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
A__ : List[str] = logging.... | 244 | 0 |
'''simple docstring'''
from collections.abc import Generator
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_, lowerCAmelCase_ : Dict = 0, 1
while True:
lowerCAmelCase_, lowerCAmelCase_ : List[Any] = b, a +... | 275 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A : str = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAIN... | 275 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : List[Any] ={
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_... | 222 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
A_ : List[str] ={
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
... | 222 | 1 |
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowercase_ = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class _UpperCamelCase :
'''simpl... | 562 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 331 | 0 |
from typing import Any
class A :
"""simple docstring"""
def __init__( self : Dict,lowercase_ : Any )-> Union[str, Any]:
'''simple docstring'''
A__ = data
A__ = None
def __repr__(... | 586 |
from math import loga
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):... | 586 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/m... | 5 |
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 : Any = False
class lowercase ( ... | 352 | 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_ = False
class ... | 122 |
"""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_ = False
class ... | 122 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowercase = logging.get_logger(__name__)
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : Union[str, A... | 91 |
from __future__ import annotations
import numpy as np
def __a ( __lowerCAmelCase ) -> Optional[Any]:
return np.maximum(0 , __lowerCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 352 | 0 |
'''simple docstring'''
import itertools
import os
import re
_UpperCamelCase : List[Any] = re.compile(R'([A-Z]+)([A-Z][a-z])')
_UpperCamelCase : str = re.compile(R'([a-z\d])([A-Z])')
_UpperCamelCase : List[str] = re.compile(R'(?<!_)_(?!_)')
_UpperCamelCase : Optional[Any] = re... | 703 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( UpperCamelCase ... | 216 | 0 |
from __future__ import annotations
from PIL import Image
# Define glider example
_lowerCAmelCase: Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 20 |
"""simple docstring"""
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 util... | 223 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine impor... | 721 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__UpperCAmelCase : Dict = TypeVar("_T")
class __snake_case ( Generic[_T] ):
'''simple docstring'''
def __init__( self : Union[str, Any] , A : Ite... | 155 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = None , l... | 89 |
'''simple docstring'''
from math import factorial
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 100 ) -> int:
"""simple docstring"""
return sum(int(SCREAMING_SNAKE_CASE_ ) for x in str(factorial(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
print(solution(in... | 51 | 0 |
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 PaddingStrategy, logging
lowerc... | 37 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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():
... | 37 | 1 |
"""simple docstring"""
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 lowerCAmelCase__ ( ... | 512 |
"""simple docstring"""
def snake_case ( _a: int , _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowerCamelCase__ = n - k
# Calc... | 510 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowerCAmelCase ( A_ : Tuple ) -> Op... | 713 | from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=snake_case ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = ['transformers', 'torch', 'note_seq']
def __init__( self: List[str] , *__lowerCAmelCase: Optional[int] , **... | 286 | 0 |
'''simple docstring'''
import math
def __UpperCAmelCase ( _UpperCAmelCase : list , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 ) -> list:
__snake_case = end or len(_UpperCAmelCase )
for i in range(_UpperCAmelCase , _UpperCAmelCase ):... | 69 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisio... | 459 | 0 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 720 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 635 | 0 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ) -> Dict:
raise RuntimeError("""CUDA out of... | 369 |
'''simple docstring'''
from manim import *
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[Any] )-> Union[str, Any]:
snake_case = Rectangle(height=0.5 , width=0.5 ... | 369 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
SCREAMING_SNAKE_CASE = (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase_ :
"""simple... | 8 |
'''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE = 8.988E9 # units = N * m^s * C^-2
def lowercase_ ( __A : float , __A : float , __A : float , __A : float ) -> dict[str, float]:
"""simple do... | 8 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamel... | 311 |
from statistics import mean, stdev
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = 3 ) -> list:
lowerCamelCase : Optional[int] = min(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Union[str, Any] = max(_SCREAMING_SNAKE_CASE )
... | 311 | 1 |
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
_snake_case = logging.get_logger(__name__)
class UpperCamelCase_ ... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_xlm_roberta": [... | 413 | 0 |
"""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 Conversation
UpperCAme... | 420 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _lowercase):
snake_case__ = '''encoder-decoder'''
snake_case__ = True
d... | 420 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 715 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
fro... | 376 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : List[Any] = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''X... | 445 |
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 _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE_... | 445 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def A ( A_ : int ):
if num <= 0:
snake_case : List[Any] = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(A_ )
snake_case ... | 555 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCond... | 555 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _... | 161 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase ={
"""configuration_rembert""": ["""REMBER... | 337 | 0 |
'''simple docstring'''
import requests
def __a ( A__ , A__ ) -> None:
lowerCAmelCase = {"Content-Type": "application/json"}
lowerCAmelCase = requests.post(A__ , json={"text": message_body} , headers=A__ )
if response.status_code != 200:
... | 159 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase : Union[str, Any] = TypeVar('T')
class _lowerCAmelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self ... | 159 | 1 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__A : Tuple = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_i... | 656 | """simple docstring"""
import os
def lowercase ( ):
"""simple docstring"""
A__ : List[Any] =os.path.dirname(os.path.realpath(UpperCamelCase ) )
A__ : str =os.path.join(UpperCamelCase , "triangle.txt" )
with open(UpperCamelCase ) as f:
... | 656 | 1 |
from math import pow, sqrt
def A_ ( *snake_case : float ) -> bool:
'''simple docstring'''
__UpperCamelCase = len(snake_case ) > 0 and all(value > 0.0 for value in values )
return result
def A_ ( snake_case : float , snake_case : f... | 451 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSe... | 451 | 1 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: int = 10 , UpperCamelCase__: int = 1_000 , UpperCamelCase__: bool = True ):
assert (
isinstance(UpperCamelCase__ , UpperCamelCase__ )
and isinstance(UpperCamelCase__ , UpperCamelCase__ )
a... | 6 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_torch_available():
raise ... | 74 | 0 |
from typing import Any
import numpy as np
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
return np.array_equal(__a, matrix.conjugate().T )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring... | 720 |
'''simple docstring'''
import random
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> tuple:
'''simple docstring'''
snake_case_ ,snake_case_ ,snake_case_ = [], [], []
for element in data:
if element < pivot:
less.append(__Upp... | 593 | 0 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase ( UpperCAmelCase__ ):
... | 207 |
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,
EfficientFormerForImageClassificationWithTeacher,
... | 376 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __A ( _A ):
"""simple docstring"""
if not is_accelerate_available():
return method
__a = version.parse(accelerate.__version__ ).bas... | 700 | from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE : Optional[Any] = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __A ( ):
... | 525 | 0 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to... | 41 |
from math import ceil
def __a ( SCREAMING_SNAKE_CASE = 1_0_0_1 ) -> int:
'''simple docstring'''
__UpperCAmelCase = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__UpperCAmelCase = 2 * i + 1
__UpperCAmelCase ... | 303 | 0 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowercase :
def __init__( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCa... | 490 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"distil... | 490 | 1 |
"""simple docstring"""
import os
def SCREAMING_SNAKE_CASE__ ( ):
"""simple docstring"""
with open(os.path.dirname(SCREAMING_SNAKE_CASE__ ) + """/p022_names.txt""" ) as file:
snake_case_ : Any = str(file.readlines()[0] )
snake_... | 480 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( _UpperCAmelCase , unittest.TestCase):
"""simp... | 480 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 661 | import math
def A__ ( lowercase: int ) -> list:
A : Optional[Any] =[True] * n
A : Tuple =False
A : List[Any] =False
A : Dict =True
for i in range(3, int(n**0.5 + 1 ), 2 ):
... | 661 | 1 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_de... | 574 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _SCREAMING_SNAKE_CASE ( UpperCamelCase : str = "laptop" ):
A__ = F"""https://www.amazon.in/laptop/s?k={produ... | 574 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 182 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def snake_case__ ( __lowercase ) -> bool:
"""simple docstring"""
A__ : int = int(number**0.5 )
return number == sq * sq
def snake_case__ ( __lowe... | 182 | 1 |
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():
im... | 520 | # We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _a :
'''simple docstring'''
... | 520 | 1 |
from collections.abc import Callable
def A__ ( _a : Callable[[float], float] , _a : float , _a : float ):
'''simple docstring'''
snake_case__ : float =a
snake_case__ : float =b
if function(_a ) == 0: # one of the a or b is... | 448 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder imp... | 448 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils ... | 433 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _lowerCAmelCase ( lowerCamelCase_ : Optional[Any] ):
__lowercase = [
'''decoder.ver... | 502 | 0 |
def lowerCamelCase__ ( __lowerCAmelCase : str ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
lowerCAmelCase_ = sorted(string.lower() )
return l... | 279 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_A = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, ... | 279 | 1 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.da... | 501 |
'''simple docstring'''
import argparse
import gc
import json
import os
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
... | 501 | 1 |
import datasets
from .evaluate import evaluate
_lowercase = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
_lo... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_lowercase = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert-large-v1": "https://huggingface.co/albert-la... | 526 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xop... | 79 | """simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
... | 516 | 0 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def A_ ( _lowercase = "", ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(""" """, """""" ).lower() ).values() ) < 2
def A_ ( _lowercase = "" ... | 720 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A_ ( ):
'''simple docstring'''
snake_case_ :Tuple = {
"""repo_name""": ["""test_repo1""", """test_r... | 310 | 0 |
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 .tokenization_re... | 340 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
_snake_... | 340 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(snake_case ) , '''Tatoeba dire... | 315 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 315 | 1 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Union[str, Any] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(SCREAMING_S... | 167 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 3 | 0 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 701 | import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mo... | 679 | 0 |
"""simple docstring"""
def _A( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
if index == number_of_items:
return 0
A__ : Dict = 0
A__ : Tuple = 0
A__ : List[Any] ... | 363 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/res... | 277 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 721 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
A : Tuple = logging.get_logger(__name__)
class lowerCAmelCase_ ( a_ ):
def __init__( self : List[Any], *_snake_case ... | 136 | 0 |
'''simple docstring'''
# 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_uti... | 267 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE__ = 100_0003
def lowerCamelCase ( _snake_case : str ,_snake_case : str ):
'''simple docstring'''
lowercase__ = ... | 267 | 1 |
"""simple docstring"""
import math
import qiskit
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE ... | 393 |
"""simple docstring"""
import os
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ) ) as input_file:
lowerCAme... | 393 | 1 |
'''simple docstring'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
__a : list[list[float]] = []
for data in source_data:
for i, el in enumerate(_lowercase ):
if len(_lowercase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(_l... | 597 | '''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE_ = 'scheduler_config.json'
class a ... | 523 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 229 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""asapp/sew-d-tiny-100... | 229 | 1 |
"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
_lowercase ... | 118 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.p... | 118 | 1 |
class A :
def __init__( self : Optional[Any] ) -> str:
"""simple docstring"""
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = {}
def ... | 708 |
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 | 0 |
"""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,
PILImageR... | 129 | def lowerCamelCase_ ( _lowercase , _lowercase ) -> int:
if len(_lowercase ) != len(_lowercase ):
raise ValueError("String lengths must match!" )
__A : Union[str, Any] = 0
for chara, chara in zip(_lowercase , _lowercase ):
... | 520 | 0 |
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
_A = logging.getLogger(__name__)
if is_torch_tpu_availabl... | 720 |
"""simple docstring"""
from math import ceil
def UpperCAmelCase ( a_ = 1001 ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = 1
for i in range(1, int(ceil(n / 2.0 ) ) ):
lowerCamelCase : int = 2 * i + 1
lowerCamelCase : i... | 133 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resi... | 23 |
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 i... | 23 | 1 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 719 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorF... | 672 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Tuple = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
... | 72 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase... | 72 | 1 |
'''simple docstring'''
lowercase_ = 8.314_4598
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''')
if molar_mass <= 0:
raise Exception('''Molar mass cannot be less th... | 701 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
lowercase_ = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling... | 352 | 0 |
'''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_identifi... | 107 | """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
__SCREAMING_SNAKE_CASE =False
class UpperCamel... | 425 | 0 |
"""simple docstring"""
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_confi... | 500 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Co... | 500 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : List[str] = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
raise... | 48 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_A... | 660 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_UpperCAmelCase : int = input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
_UpperCAmelCase : Any = BeautifulSoup(request... | 145 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Optional[Any] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig'... | 145 | 1 |
'''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 to V and v to... | 274 | '''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArgume... | 274 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
snake_case__ : List[str] = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem imp... | 592 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase__ ( _lowerCamelCase ) ->str:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural n... | 592 | 1 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should... | 526 |
'''simple docstring'''
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 a__( lowerCamelCase__ ):
... | 526 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"camembert-base": "https://hugg... | 712 |
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowercase):
__SCREAMING_SNAKE_CASE : Optional[int] = ["""keras_nlp"""]
def __init__( self : Optional[int] , *__UpperCamelCase : List[Any] , **__UpperCamelCa... | 129 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_s... | 475 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowercase__ ( tf.keras.optimizers.schedules.LearningRateSchedule ... | 475 | 1 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def _lowerCamelCase ( __a ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.te... | 628 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, ... | 628 | 1 |
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