
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A     :	Tuple    = logging.get_logger(__name__)


if is_vision_available():
					import PIL







class 			__A( a			):
				snake_case_						   =    ['''pixel_values''']




				def __init__(     self		,     _snake_case = True		,     _snake_case = None		,     _snake_case = PILImageResampling.BICUBIC		,     _snake_case = True		,     _snake_case = None		,     _snake_case = True		,     _snake_case = 1 / 255		,     _snake_case = True		,     _snake_case = None		,     _snake_case = None		,     _snake_case = True		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    size if size is not None else {'''shortest_edge''': 224}
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								__a										=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case		,     param_name='''crop_size'''				)

								__a										=    do_resize
								__a										=    size
								__a										=    resample
								__a										=    do_center_crop
								__a										=    crop_size
								__a										=    do_rescale
								__a										=    rescale_factor
								__a										=    do_normalize
								__a										=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__a										=    image_std if image_std is not None else OPENAI_CLIP_STD
								__a										=    do_convert_rgb




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = PILImageResampling.BICUBIC		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								if "shortest_edge" not in size:
												raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
								__a										=    get_resize_output_image_size(_snake_case		,     size=size['''shortest_edge''']		,     default_to_square=_snake_case				)
								return resize(_snake_case		,     size=_snake_case		,     resample=_snake_case		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case				)
								if "height" not in size or "width" not in size:
												raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
								return center_crop(_snake_case		,     size=(size['''height'''], size['''width'''])		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      Tuple:


								'''simple docstring'''




								return rescale(_snake_case		,     scale=_snake_case		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								return normalize(_snake_case		,     mean=_snake_case		,     std=_snake_case		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = ChannelDimension.FIRST		,     **_snake_case		,     )    ->      PIL.Image.Image:


								'''simple docstring'''




								__a										=    do_resize if do_resize is not None else self.do_resize
								__a										=    size if size is not None else self.size
								__a										=    get_size_dict(_snake_case		,     param_name='''size'''		,     default_to_square=_snake_case				)
								__a										=    resample if resample is not None else self.resample
								__a										=    do_center_crop if do_center_crop is not None else self.do_center_crop
								__a										=    crop_size if crop_size is not None else self.crop_size
								__a										=    get_size_dict(_snake_case		,     param_name='''crop_size'''		,     default_to_square=_snake_case				)
								__a										=    do_rescale if do_rescale is not None else self.do_rescale
								__a										=    rescale_factor if rescale_factor is not None else self.rescale_factor
								__a										=    do_normalize if do_normalize is not None else self.do_normalize
								__a										=    image_mean if image_mean is not None else self.image_mean
								__a										=    image_std if image_std is not None else self.image_std
								__a										=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__a										=    make_list_of_images(_snake_case				)

								if not valid_images(_snake_case				):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

								if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.'''				)

								if do_center_crop and crop_size is None:
												raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

								if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												__a										=    [convert_to_rgb(_snake_case				) for image in images]

								# All transformations expect numpy arrays.
								__a										=    [to_numpy_array(_snake_case				) for image in images]

								if do_resize:
												__a										=    [self.resize(image=_snake_case		,     size=_snake_case		,     resample=_snake_case				) for image in images]

								if do_center_crop:
												__a										=    [self.center_crop(image=_snake_case		,     size=_snake_case				) for image in images]

								if do_rescale:
												__a										=    [self.rescale(image=_snake_case		,     scale=_snake_case				) for image in images]

								if do_normalize:
												__a										=    [self.normalize(image=_snake_case		,     mean=_snake_case		,     std=_snake_case				) for image in images]

								__a										=    [to_channel_dimension_format(_snake_case		,     _snake_case				) for image in images]

								__a										=    {'''pixel_values''': images}
								return BatchFeature(data=_snake_case		,     tensor_type=_snake_case				)

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  tuple[int | None, int | None, float]:
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				__a										=    (low + high) // 2
				__a							,     __a							,     __a										=    max_subarray(a__						,    a__						,    a__  )
				__a							,     __a							,     __a										=    max_subarray(a__						,    mid + 1						,    a__  )
				__a							,     __a							,     __a										=    max_cross_sum(a__						,    a__						,    a__						,    a__  )
				if left_sum >= right_sum and left_sum >= cross_sum:
								return left_low, left_high, left_sum
				elif right_sum >= left_sum and right_sum >= cross_sum:
								return right_low, right_high, right_sum
				return cross_left, cross_right, cross_sum


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  tuple[int, int, float]:
				__a							,     __a										=    float('''-inf'''  ), -1
				__a							,     __a										=    float('''-inf'''  ), -1

				__a										=    0
				for i in range(a__						,    low - 1						,    -1  ):
								summ += arr[i]
								if summ > left_sum:
												__a										=    summ
												__a										=    i

				__a										=    0
				for i in range(mid + 1						,    high + 1  ):
								summ += arr[i]
								if summ > right_sum:
												__a										=    summ
												__a										=    i

				return max_left, max_right, (left_sum + right_sum)


def     __lowerCAmelCase			(			a__  )       ->  float:
				__a										=    [randint(1						,    a__  ) for _ in range(a__  )]
				__a										=    time.time()
				max_subarray(a__						,    0						,    input_size - 1  )
				__a										=    time.time()
				return end - start


def     __lowerCAmelCase			(			)       ->  None:
				__a										=    [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
				__a										=    [time_max_subarray(a__  ) for input_size in input_sizes]
				print('''No of Inputs\t\tTime Taken'''  )
				for input_size, runtime in zip(a__						,    a__  ):
								print(a__						,    '''\t\t'''						,    a__  )
				plt.plot(a__						,    a__  )
				plt.xlabel('''Number of Inputs'''  )
				plt.ylabel('''Time taken in seconds'''  )
				plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()