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 |
|---|---|---|---|---|
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
UpperCAmelCase__ : Any ... | 313 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CL... | 313 | 1 |
from __future__ import annotations
import math
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if len(_UpperCAmelCase ) != 2 or len(a[0] ) != 2 or len(_UpperCAmelCase ) != 2 or len(b[0] ) != 2:
... | 715 |
def __snake_case ( _UpperCAmelCase = 10 ):
"""simple docstring"""
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or n < 0:
raise ValueError('Invalid input' )
lowercase = 10**n
lowercase = 2_84_33 * (pow(2 ... | 314 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfAr... | 623 |
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_ava... | 623 | 1 |
import math
class lowerCAmelCase__:
'''simple docstring'''
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> int:
_SCREAMING_SNAKE_CASE : List[Any] = 0.0
_SCREAMING_SNAKE_CASE ... | 713 |
import cmath
import math
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = math.radians(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Tuple = math.radians(__lo... | 381 | 0 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 513 |
from __future__ import annotations
import time
import numpy as np
lowerCamelCase_ = [8, 5, 9, 7]
lowerCamelCase_ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCamelCase_ = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3... | 513 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> List[Any]:
"""simple docstring"""
snake_case_ = analyze_text(__UpperCamelCase )
sn... | 721 |
import requests
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )-> None:
"""simple docstring"""
snake_case_ = {'''Content-Type''': '''application/json'''}
snake_case_ = requests.post(SCREAMING_SNAKE_CASE , json={'''text''': messag... | 531 | 0 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCamelCase_( _A :float , _A :float , _A :bool = False )-> Optional[Any]:
if radian_mode:
return [magnitude * cos(lowerCamelCase_ ), magnitude * ... | 551 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: int , lowerCamelCase_: int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
... | 449 | 0 |
"""simple docstring"""
import math
def snake_case__ ( _snake_case : 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, al... | 304 | """simple docstring"""
from collections.abc import Sequence
def snake_case__ ( _snake_case : Sequence[float] , _snake_case : bool = False ):
"""simple docstring"""
if not arr:
return 0
UpperCamelCase__ = 0 if allow_empty_suba... | 304 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import Tes... | 644 | """simple docstring"""
import math
from datetime import datetime, timedelta
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = year % 19
lowerCAmelCase__ = year % 4
lowerCAmelCase__ = year % 7
lowerCAmelCase__ = math.floor(year / 100 )
... | 644 | 1 |
from PIL import Image
def a ( SCREAMING_SNAKE_CASE_ : Image , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : Tuple = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(SCR... | 713 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def _lowercase ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring... | 643 | 0 |
'''simple docstring'''
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,
... | 350 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTeste... | 350 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> float:
UpperCAmelCase : List[Any] = x
UpperCAmelCase : List[str] = y
for step in range(_lower... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 672 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {"configuration_fnet": ["FNET_PRETR... | 26 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
Mobile... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a: str = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not ... | 715 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a: Dict = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = ['input_ids... | 268 | 0 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 557 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-r... | 557 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
def _snake_case ( lowercase__ : Optional[Any] ) -> bytes:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = """https://downloadgram.net/wp-json/wppress/video-down... | 701 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCAmelCase = False
clas... | 256 | 0 |
import argparse
_a : Any = 'docs/source/_static/js/custom.js'
def a_ ( __magic_name__ ) -> List[Any]:
"""simple docstring"""
with open(__magic_name__ , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case :... | 598 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large"... | 532 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prep... | 711 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( a=None , a=None ) -> Union[str, ... | 245 | 0 |
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__lowerCAmelCase : List[str] = logging.get_logger(__name__) # pylint: disab... | 262 | '''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__lowerCAmelCase : int ... | 262 | 1 |
"""simple docstring"""
import requests
__a = "" # <-- Put your OpenWeatherMap appid here!
__a = "https://api.openweathermap.org/data/2.5/"
def A_ ( _lowercase = "Chicago", _lowercase = APPID ):
'''simple docstring'''
return requests.get(URL_... | 717 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusion... | 310 | 0 |
# 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 ... | 568 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import Au... | 568 | 1 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (... | 186 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaIm... | 186 | 1 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowercase__ : str = str(bin(UpperCAmelCase ) )[2:] # remove the leading "0b"
lowercase__ : int ... | 152 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_... | 152 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenc... | 514 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, to... | 514 | 1 |
'''simple docstring'''
from math import isqrt, loga
def _lowerCAmelCase ( lowercase ) -> list[int]:
__lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowercas... | 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase : List[Any] = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
tr... | 57 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertToken... | 57 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTok... | 54 |
def __lowerCAmelCase ( A , A , A , A ):
# Return True if there is node that has not iterated.
UpperCAmelCase_ = [False] * len(A )
UpperCAmelCase_ = []
queue.append(A )
UpperCAmelCase_ = True
while queue:
UpperCAmelCase_ ... | 162 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggi... | 719 | from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Co... | 246 | 0 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class a ( __lowerCAmelCase ):
"""simple docstring"""
... | 401 | import math
def snake_case ( snake_case__ :int) -> list:
_A = [True] * n
_A = False
_A = False
_A = True
for i in range(3 , int(n**0.5 + 1) , 2):
_A = i * 2
while index < n:
... | 401 | 1 |
def a__ ( _UpperCamelCase : list ):
if len(_UpperCamelCase ) < 2:
return collection
def circle_sort_util(_UpperCamelCase : list ,_UpperCamelCase : int ,_UpperCamelCase : int ) -> bool:
__lowerCamelCase = False
if low == high:
return s... | 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 622 | 1 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def __a(SCREAMING_SNAKE_CASE_ : jnp.ndarray , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : float = 1 , SCREAMING_SNAKE_CASE_ : float = 1 , SCREAMING_SNAKE_CASE... | 18 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase__ ( a , a ):
__snake_case = u
for i in range(1 , a ):
__snake_case = temp * (u - i)
return temp
def lowerCamelCase__ ( ):
__snake_c... | 356 | 0 |
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
... | 710 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
'... | 206 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseM... | 136 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenize... | 556 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETR... | 48 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokeni... | 48 | 1 |
"""simple docstring"""
import sys
__UpperCamelCase = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648... | 247 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common imp... | 334 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/... | 720 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCamelCase__ : List... | 18 | 0 |
def _lowerCAmelCase ( _lowerCAmelCase = 1_0_0_0 ):
'''simple docstring'''
A_ : int = 3
A_ : int = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return result
if __name_... | 569 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 569 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __A ( lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : int = u
for i in range(1 , lowerCAmelCase_ ):
_UpperCAmelCase : Optional[int] = temp... | 704 |
'''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
... | 156 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : str = ['image_processor', 'tokenizer']
A_ : Optional[int] = 'ViTIma... | 592 | from math import isqrt, loga
def lowerCAmelCase__ ( a__ ) ->list[int]:
'''simple docstring'''
_UpperCamelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , a__ , a__ ):
_Upper... | 547 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
... | 239 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 239 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : List[Any] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRA... | 102 |
'''simple docstring'''
def __A ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __A ( ):
"""simple docstring"""
... | 211 | 0 |
from itertools import count
def __lowerCAmelCase ( _UpperCamelCase = 50 ) -> int:
'''simple docstring'''
lowerCamelCase__: List[Any] = [1] * min_block_length
for n in count(_UpperCamelCase ):
fill_count_functions.append(1... | 242 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowercase = re.compile(r'\b(a|an|the)\b', re.UNICODE)
_lowercase = None
def __lowerCAmelCase ( ) -> Optional[Any]:
... | 242 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : List[Any] = []
lowe... | 501 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configu... | 501 | 1 |
'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
... | 331 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extracti... | 331 | 1 |
"""simple docstring"""
import qiskit
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
... | 102 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
f... | 467 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
snake_case : Any = logging.getLogger(__name__)
class lowerCAmelCase__ :
def __init__( self : ... | 182 |
from __future__ import annotations
snake_case : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase__ :
def __init__( self : ... | 182 | 1 |
'''simple docstring'''
import math
import sys
def _lowercase ( UpperCamelCase__ : str ):
__A : Optional[Any] = ''
try:
with open(UpperCamelCase__, 'rb' ) as binary_file:
__A : str = binary_file.read()
for dat in data:
__A : Tuple = f"""{... | 365 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.js... | 365 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def lowerCamelCase_ ( lowercase__ , lowercase__ , lowercase__):
if (inductance, frequency, reactance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if inductance < 0:
... | 187 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : in... | 187 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.c... | 492 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_UpperCamelCase = logging.get_logger(__name__)
class __lowercase (_UpperCAmelCase ):
def __init__( self , *A_ , **A_ ) ->None:
'''simple docst... | 492 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 718 | from __future__ import annotations
from collections import Counter
from random import random
class _a :
"""simple docstring"""
def __init__( self ):
_lowercase ={}
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
_lowercase ={}
def __lowerCAmelCase ( ... | 594 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase : int ={"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfi... | 54 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE... | 118 | 0 |
# 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... | 720 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase ={
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_... | 405 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_availa... | 48 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : int , UpperCamelCase_ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelC... | 48 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : List[str] = {'configuration_timm_backbone': ['TimmBackboneConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
ex... | 702 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_a : str = logging.get_logger(__name__)
... | 84 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCAmelCase = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config'''... | 90 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = OmegaConf.load(A )
... | 90 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_... | 702 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwr... | 590 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : bool = True , lowerCamelCase_ : float = math.inf , lowerCamelCase_ : float = -math.in... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : List[Any] = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_... | 709 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
... | 21 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image... | 444 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def lowercase (_A , _A = 2 , _A = 1 , _A = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError('The input value cann... | 444 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
_UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class a ( a_ ):
def __init__( self , *_lowerCamelCase , **_lowerCam... | 134 |
"""simple docstring"""
import math
import unittest
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 134 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : int = {
'configuration_distilbert': [
'DISTILBERT_PRETRA... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
from typing import List
import numpy as np
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : List[Any] = {key: len(SCREAMING_SNAKE_CASE__ ) for key, value in gen_kwargs.items() if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )}
if len(set(lists_lengths.v... | 230 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Union[str, Any] ... | 230 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 413 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class A ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self : List[str] , _UpperCamelCase : ... | 226 | 0 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
... | 713 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normaliz... | 299 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sched... | 16 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A = 'src/transformers'
# This is to make sure the tra... | 449 | 0 |
from math import factorial
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ = 1_0_0 ):
return sum(int(UpperCamelCase__ ) for x in str(factorial(UpperCamelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 712 |
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ = 5_0_0_0_0_0_0_0 ):
UpperCamelCase__ : Any = set()
UpperCamelCase__ : Any = int((limit - 2_4) ** (1 / 2) )
UpperCamelCase__ : Dict = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 ... | 462 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_tor... | 75 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMA... | 612 | 0 |
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... | 701 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
# TODO: upload to AWS
_lowerCAmelCase = {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main... | 481 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.uti... | 26 | from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class SCREAMING_SNAKE_CASE_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : ... | 305 | 0 |
from __future__ import annotations
class a :
def __init__( self , _lowerCAmelCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Optional[Any] = order
# a_{0} ... a_{k}
__SCREAMING_SNAKE_CASE: Any ... | 713 |
from math import isclose, sqrt
def lowerCAmelCase ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> tuple[float, float, float]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: int = ... | 146 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
snake_case_ = str(bin(SCREAMING_SNAKE_CASE__ ) )
binary_number += "0" * shi... | 39 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Dic... | 39 | 1 |
import unittest
from knapsack import knapsack as k
class UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def __a ( self ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Optional[Any] ... | 705 |
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
from transformers im... | 298 | 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... | 20 |
from manim import *
class lowercase_ (lowercase__ ):
def __UpperCamelCase ( self) -> List[Any]:
a__ =Rectangle(height=0.5 , width=0.5)
a__ =Rectangle(height=0.46 , width=0.46).set_stroke(width=0)
a__ =[mem.copy() for... | 20 | 1 |
'''simple docstring'''
__magic_name__ ={
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'... | 715 | import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__magic_name__ =logging.get_logger(__name__)
__magic_name__ =... | 469 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
fr... | 647 |
import math
import unittest
def lowerCAmelCase__ ( lowerCamelCase_ : int):
'''simple docstring'''
assert isinstance(lowerCamelCase_ ,lowerCamelCase_) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 a... | 647 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a: List[Any] = logging.get_logger(__name__)
_a: Union[str, Any] = {
"""andreasmadsen/efficient_mlm_m0.40""": (... | 707 |
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 (
center_crop,
get_res... | 268 | 0 |
import torch
def snake_case ( ):
'''simple docstring'''
if torch.cuda.is_available():
__lowercase = torch.cuda.device_count()
else:
__lowercase = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __name__ == "__main__":
main()
| 80 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 98 | 0 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( lowerCamelCase ):
__mag... | 367 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase_ : Dict = 637_8137.0
UpperCAmelCase_ : List[Any] = 635_6752.31_4245
UpperCAmelCase_ : List[str] = 6378137
def lowerCAmelCase_ ( l... | 367 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
while a != 0:
snake_case__ : Tuple = b % a, a
return b
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> str:
... | 374 |
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_: Tuple = {
'tiny.en': 'https://openaipublic.azureedge.net/main/wh... | 648 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 708 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : Tuple = {}
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = 'llama'
SCREAMING_SNAKE_CASE... | 604 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_snake_case : str ... | 81 |
def __lowerCamelCase ( __a :int ) -> Dict:
"""simple docstring"""
A__ = len(__a )
A__ = sum(__a )
A__ = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
A__ ... | 176 | 0 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
a__ : Optional[Any] = """
import os
"""
a__ : Optional[Any] = """
def foo():
import os
return False
"""
a__ : Tuple = """
def foo():
def... | 720 |
"""simple docstring"""
import qiskit
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_lowerCAmelCase = qiskit.QuantumCirc... | 309 | 0 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = CustomTokenizer
pass
| 38 |
'''simple docstring'''
from collections.abc import Callable
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase : Callable | None = None ) -> None:
# Stores actual heap items.
... | 275 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ : List[str] =logging.get_logger(__name__)
snake_case_ : str ={
"facebook/xlm-roberta-xl"... | 712 |
snake_case_ : str =[0, 2, 4, 6, 8]
snake_case_ : List[str] =[1, 3, 5, 7, 9]
def UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if remaining_length == 0:
if ... | 205 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffus... | 92 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedToke... | 616 | 0 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 688 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __SCREAMING_SNAKE_CASE ( ) -> List[str]:
__lowercase = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
__lowercase = parser.add_subparsers(help='diffusers-cl... | 688 | 1 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
... | 259 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple docs... | 259 | 1 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960... | 710 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepIn... | 388 | 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 ( _A ):
"""simple do... | 324 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__magic_name__: Optional[int] = logging.get_logger(__name__... | 324 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase : Tuple ={'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
... | 237 | """simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase : Tuple =logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] ={
... | 237 | 1 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a__ ( ... | 98 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowerCamelCase ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
@re... | 590 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
A__ : Tuple= get_t... | 20 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mo... | 20 | 1 |
import os
def UpperCAmelCase__ ( __snake_case ) -> Dict:
_A = len(grid[0] )
_A = len(snake_case_ )
_A = 0
_A = 0
_A = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for ... | 317 |
"""simple docstring"""
import numpy as np
def A_ ( snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : str ,snake_case_ : Optional[int] ,snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : int = int(np... | 499 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def... | 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True... | 62 | 1 |
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
return np.array_equal(lowerCAmelCase__ , matrix.conjugate().T )
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
SCREAMING_... | 345 | def UpperCamelCase_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_lowerCAmelCase : Optional[int] = str(bin(lowerCAmelCase__ ) )[2:] # remove the leading... | 424 | 0 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = (C... | 713 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Optional[Any] ... | 624 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str]):
lowerCamelCase : int = list(U... | 320 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( UpperCamelCase="ro" , UpperCamelCase="en" , UpperCamelCase="wmt16" , UpperCamelCase=None ):
"""simple docstring"""
try:
import datasets
... | 565 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( _UpperCamelCase : int | str ) -> bool:
"""simple docstring"""
snake_case = str(_UpperCamelCase )
return n == n[::-1]
def ... | 704 | """simple docstring"""
import math
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
return math.sqrt(_UpperCamelCase ) * math.sqrt(_UpperCamelCase ) == num
def lowerCAmelCase__ ( _UpperCa... | 104 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 99 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .s... | 99 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCamelCase : int = False
class _lowerCAmelCase( unittest.TestCase):
"""simple docstrin... | 341 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_... | 341 | 1 |
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