code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tr... | 309 |
'''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_convbert import ConvBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
Uppe... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCAmelCase ( _lowerCamelCase : float , _lowerCamelCase : int ) -> float:
_lowerCAmelCase : Optional[Any] = u
for i in range(1 , _lowerCamelCase ):
_lowerCAmelCase : ... | 309 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self ):
_lowerCAmelCase : Any = """"""
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase ... | 309 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridge... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""... | 309 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.tr... | 309 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase_ = 0
UpperCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0,... | 309 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ) -> list[int]:
_lowerCAmelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
_lowerCAmelCase : Any = []
# Traverse through all denomi... | 309 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import Learn... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_... | 309 | 1 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
UpperCamelCase_ = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S 9S... | 309 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_t... | 309 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_lo... | 309 | 1 |
'''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 PreTrainedTokenizerBase
from ...... | 309 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 309 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ) -> Union[str, Any]:
# =====... | 309 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (_a ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.war... | 309 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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 Config... | 309 | 1 |
'''simple docstring'''
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,
DPMSolverMultistepInv... | 309 |
'''simple docstring'''
# 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
#
... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def _UpperCAm... | 309 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 309 | 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 _UpperCAmelCase ( ) ... | 309 |
'''simple docstring'''
import argparse
import os
import re
UpperCamelCase_ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
UpperCamelCase_ = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase_ = re.compile(r"""^\s*\"([^\"... | 309 | 1 |
'''simple docstring'''
from timeit import timeit
UpperCamelCase_ = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panama"
}
# Ensure ... | 309 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCamelCase_ = logging.get_logger(__name__)
class a_ :
def __init__( self ... | 309 | 1 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ (_a ):
__lowerCAmelCase : Any = (KDPMaDiscreteScheduler,)
__lowerCAmelCase : Tuple = 1_0
... | 309 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] ) -> List[Any]:
_lowerCAmelCase : List[Any] = min(_lowerCamelCase ) # min() finds the minimum value
_lowerCAmelCase : Tuple = max(_lower... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int = 1_00 ) -> int:
_lowerCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
_lowerCAmelCase : str = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
... | 309 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def _UpperCAmelCase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == ... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 309 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = """▁"""
Up... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfi... | 309 | 1 |
'''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.configuration_uti... | 309 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : list[float] ) -> Dict:
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 | 1 |
'''simple docstring'''
import os
import sys
UpperCamelCase_ = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceCl... | 309 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (_a ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.w... | 309 | 1 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
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 ... | 309 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_torch_a... | 309 |
'''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_convbert import ConvBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
Uppe... | 309 | 1 |
'''simple docstring'''
from PIL import Image
def _UpperCAmelCase ( _lowerCamelCase : Image , _lowerCamelCase : float ) -> Image:
def brightness(_lowerCamelCase : int ) -> float:
return 1_28 + level + (c - 1_28)
if not -255.0 <= level <= 255.0:
raise ValueError(""... | 309 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self ):
_lowerCAmelCase : Any = """"""
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase ... | 309 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""... | 309 | 1 |
'''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
class a_ (unittest.TestCase ):... | 309 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase_ = 0
UpperCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0,... | 309 | 1 |
'''simple docstring'''
from manim import *
class a_ (_a ):
def __UpperCamelCase ( self ):
_lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase : List[str] = Rectangle(height=0.46 , ... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ) -> list[int]:
_lowerCAmelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
_lowerCAmelCase : Any = []
# Traverse through all denomi... | 309 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class a_ (unittest.TestCase ):
def ... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_... | 309 | 1 |
'''simple docstring'''
import pprint
import requests
UpperCamelCase_ = """https://zenquotes.io/api"""
def _UpperCAmelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _UpperCAmelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/random"... | 309 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_availabl... | 309 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_lo... | 309 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : list , _lowerCamelCase : int ) -> list:
_lowerCAmelCase : int = len(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = [[0] * n for i in range(_... | 309 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_... | 309 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ) -> Union[str, Any]:
# =====... | 309 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = """▁"""
Up... | 309 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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 Config... | 309 | 1 |
'''simple docstring'''
from math import loga
def _UpperCAmelCase ( _lowerCamelCase : int ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""Input value must be a '... | 309 |
'''simple docstring'''
# 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
#
... | 309 | 1 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ (_a ):
def __init__( self , snake_... | 309 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 309 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (_a ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.w... | 309 |
'''simple docstring'''
import argparse
import os
import re
UpperCamelCase_ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
UpperCamelCase_ = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase_ = re.compile(r"""^\s*\"([^\"... | 309 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backb... | 309 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCamelCase_ = logging.get_logger(__name__)
class a_ :
def __init__( self ... | 309 | 1 |
'''simple docstring'''
# 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
#
... | 309 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] ) -> List[Any]:
_lowerCAmelCase : List[Any] = min(_lowerCamelCase ) # min() finds the minimum value
_lowerCAmelCase : Tuple = max(_lower... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig"""... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int = 1_00 ) -> int:
_lowerCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
_lowerCAmelCase : str = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",
... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 309 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : Optional[int] , _lowerCamelCase : List[str] ) -> Optional[Any]:
_lowerCAmelCase : Dict = [0 for i in range(r + 1 )]
# nc0 = 1
_lowerCAmelCase : str = 1
for i in range(1 , n... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfi... | 309 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 309 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : list[float] ) -> Dict:
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 | 1 |
'''simple docstring'''
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 Accelera... | 309 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (_a ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.w... | 309 | 1 |
'''simple docstring'''
UpperCamelCase_ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Union[str, Any] , _l... | 309 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a_ :
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | None = None
UpperCam... | 309 |
'''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_convbert import ConvBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
Uppe... | 309 | 1 |
'''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, PreTrainedTokeniz... | 309 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self ):
_lowerCAmelCase : Any = """"""
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase ... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import _LazyModule
UpperCamelCase_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, gl... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""... | 309 | 1 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, ... | 309 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase_ = 0
UpperCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0,... | 309 | 1 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ) -> list[int]:
_lowerCAmelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
_lowerCAmelCase : Any = []
# Traverse through all denomi... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : list ) -> list:
if len(_lowerCamelCase ) != 2 or len(a[0] ) != 2 or len(_lowerCamelCase ) != 2 or len(b[0] ) != 2:
raise Exc... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_... | 309 | 1 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCamelCase_ = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
UpperCamelCase_ = None
def _UpperCAmelCase ( ) -> Tuple:
_lowerCAmelCase : List[str]... | 309 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_... | 309 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ["""small""", """medium""", """large"""]
UpperCamelCase_ = """lm_head.decoder.weight"""
UpperCamelCase_ = """lm_head.weight"""
def _UpperCAmelCase ( _lowerCamelCas... | 309 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_lo... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase_ = """#"""
class a_ :
def __init__( self ):
_lowerCAmelCase : dict = {}
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : ... | 309 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 1 |
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib im... | 309 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ) -> Union[str, Any]:
# =====... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
UpperCamelCase_ = numpy.array([0, 0])
UpperCamelCase_ = numpy.array([0.5, 0.8_6_6_0_2_5_4])
UpperCamelCase_ = numpy.array([1, 0])
UpperCamelCa... | 309 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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 Config... | 309 | 1 |
'''simple docstring'''
import argparse
import os
import re
UpperCamelCase_ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
UpperCamelCase_ = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase_ = re.compile(r"""^\s*\"([^\"... | 309 |
'''simple docstring'''
# 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
#
... | 309 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import C... | 309 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 309 | 1 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when swi... | 309 |
'''simple docstring'''
import argparse
import os
import re
UpperCamelCase_ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
UpperCamelCase_ = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase_ = re.compile(r"""^\s*\"([^\"... | 309 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase_ = logging.getLogger()
def... | 309 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCamelCase_ = logging.get_logger(__name__)
class a_ :
def __init__( self ... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : list[float] ) -> Dict:
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] ) -> List[Any]:
_lowerCAmelCase : List[Any] = min(_lowerCamelCase ) # min() finds the minimum value
_lowerCAmelCase : Tuple = max(_lower... | 309 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_util... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int = 1_00 ) -> int:
_lowerCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
_lowerCAmelCase : str = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
... | 309 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class a_ (_a ):
__lowerCAmelCase : Tuple = """MCTCTFeatureExtractor"""
__lowerCAmelCase : str = """AutoTokenizer"""
def __init__( self ... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 309 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _UpperCAmelCase ( _lowe... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfi... | 309 | 1 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] ) -> List[Any]:
_lowerCAmelCase : List[Any] = min(_lowerCamelCase ) # min() finds the minimum value
_lowerCAmelCase : Tuple = max(_lower... | 309 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : list[float] ) -> Dict:
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokeni... | 309 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (_a ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.w... | 309 | 1 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 309 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase_ = 10
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : i... | 309 |
'''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_convbert import ConvBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
Uppe... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCamelCase_ = [
[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],
[0... | 309 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self ):
_lowerCAmelCase : Any = """"""
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase ... | 309 | 1 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _UpperCAmelCase ( _lowerCamelCase : Optional[int] ) -> Dict:
_lowerCAmelCase : Optional[int] = {}
_lowerCAmelCase : str = job["""start... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""... | 309 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 309 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase_ = 0
UpperCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0,... | 309 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ) -> list[int]:
_lowerCAmelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
_lowerCAmelCase : Any = []
# Traverse through all denomi... | 309 | 1 |
'''simple docstring'''
import functools
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : list[int] ) -> int:
# Validation
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not all(isinstance(_lowerCamelCase , _lowerCamelCase ) ... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_... | 309 | 1 |
'''simple docstring'''
import re
def _UpperCAmelCase ( _lowerCamelCase : str ) -> str:
if len(re.findall("""[ATCG]""" , _lowerCamelCase ) ) != len(_lowerCamelCase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" , ... | 309 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_... | 309 | 1 |
'''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 a_ (_a ):
# to overwrite at feature extractactor specific tests
... | 309 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_lo... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeature... | 309 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_dete... | 309 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ) -> Union[str, Any]:
# =====... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( _lowerCamelCase : str , _lowerCamelCase : list[str] | None = None ) -> list[list[str]]:
_lowerCAmelCase : List[Any] = word_bank or []
# create a table
_lowerCAmelCase : int ... | 309 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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 Config... | 309 | 1 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
UpperCamelCase_ = None
try:
import msvcrt
except ImportError:
UpperCamelCase_ = None
try:
import fcntl
except ImportError:
UpperCamelCase_ = None
... | 309 |
'''simple docstring'''
# 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
#
... | 309 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pi... | 309 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 309 | 1 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ) -> Union[str, Any]:
# =====... | 309 |
'''simple docstring'''
import argparse
import os
import re
UpperCamelCase_ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
UpperCamelCase_ = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase_ = re.compile(r"""^\s*\"([^\"... | 309 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCamelCase_ = TypeVar("""T""")
UpperCamelCase_ = Union[List[T], Tuple[T, ...]]
UpperCamelCase_ = Union[T, List[T], Dict[str, T]]
UpperCamelCase_ = Union[str, bytes, os.PathLike]
| 309 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCamelCase_ = logging.get_logger(__name__)
class a_ :
def __init__( self ... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"... | 309 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] ) -> List[Any]:
_lowerCAmelCase : List[Any] = min(_lowerCamelCase ) # min() finds the minimum value
_lowerCAmelCase : Tuple = max(_lower... | 309 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a_ (_a ):
__lowerCAmelCase : Dict = (IPNDMScheduler,)
__lowerCAmelCase : Tuple = (("""num_inference_steps""", 5_0),)
... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int = 1_00 ) -> int:
_lowerCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
_lowerCAmelCase : str = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
... | 309 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_uti... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 309 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
UpperCamelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCamelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCamelCase_ ... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfi... | 309 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_lowerCAmelCase : Optional[Any] = str(bin(_lowerCamelCase ) )[... | 309 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : list[float] ) -> Dict:
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 | 1 |
'''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_availa... | 309 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (_a ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.w... | 309 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a_ (_a ):
__lowerCAmelCase : int = ["""image_processor""", """tokenizer"""]
__lowerCAmelCase : str = """CLIPImageProcessor"""
... | 309 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _UpperCAmelCase ( _lowerCamelCase : list[list[float]] ) -> list[list[float]]:
_lowerCAmelCase : Optional[Any] = Decimal
# Check if the provided matrix has 2 rows... | 309 |
'''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_convbert import ConvBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
Uppe... | 309 | 1 |
'''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_convbert import ConvBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
Uppe... | 309 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self ):
_lowerCAmelCase : Any = """"""
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase ... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BridgeTowerConfig"""... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""... | 309 | 1 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a_ :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None , snake_case_=None ):
_lowerCAmelCase : int ... | 309 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase_ = 0
UpperCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0,... | 309 | 1 |
'''simple docstring'''
import math
def _UpperCAmelCase ( _lowerCamelCase : int ) -> list[int]:
_lowerCAmelCase : Dict = []
_lowerCAmelCase : int = 2
_lowerCAmelCase : Dict = int(math.sqrt(_lowerCamelCase ) ) # Size of every segment
... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ) -> list[int]:
_lowerCAmelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
_lowerCAmelCase : Any = []
# Traverse through all denomi... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfi... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_... | 309 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int = 10_00 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 309 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_... | 309 | 1 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _UpperCAmelCase ( _lowerCamelCase : str , _lowerCamelCase : complex , _lowerCamelCase : str = "x" , _lowerCamelCase : float = 10**-10 , _lowerCamelCa... | 309 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_lo... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""... | 309 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 1 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
UpperCamelCase_ = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""... | 309 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ) -> Union[str, Any]:
# =====... | 309 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...model... | 309 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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 Config... | 309 | 1 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a_ :
def __init__( self , snake_case_ ):
if isinstance(snake_case_ , ... | 309 |
'''simple docstring'''
# 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
#
... | 309 | 1 |
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