





'''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_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				str											=       ["pixel_values"]


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







				'''simple docstring'''





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

				__lowercase										=							do_resize
				__lowercase										=							do_rescale
				__lowercase										=							do_normalize
				__lowercase										=							do_center_crop
				__lowercase										=							crop_size
				__lowercase										=							size
				__lowercase										=							resample
				__lowercase										=							rescale_factor
				__lowercase										=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__lowercase										=							image_std if image_std is not None else IMAGENET_DEFAULT_STD


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = PILImageResampling.BILINEAR     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase										=							get_size_dict(_lowerCamelCase )

				if "shortest_edge" in size:
							__lowercase										=							get_resize_output_image_size(_lowerCamelCase     ,size=size['''shortest_edge''']     ,default_to_square=_lowerCamelCase )
							# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
							__lowercase										=							(size['''height'''], size['''width'''])
				else:
							raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}" )
				return resize(_lowerCamelCase     ,size=_lowerCamelCase     ,resample=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase										=							get_size_dict(_lowerCamelCase )
				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(_lowerCamelCase     ,size=(size['''height'''], size['''width'''])     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							np.ndarray:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = ChannelDimension.FIRST     ,**_lowerCamelCase     ,)							->							BatchFeature:







				'''simple docstring'''





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

				__lowercase										=							size if size is not None else self.size
				__lowercase										=							get_size_dict(_lowerCamelCase )

				if not is_batched(_lowerCamelCase ):
							__lowercase										=							[images]

				if not valid_images(_lowerCamelCase ):
							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.''' )

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

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

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

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

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

				__lowercase										=							[to_channel_dimension_format(_lowerCamelCase     ,_lowerCamelCase ) for image in images]

				__lowercase										=							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase     ,tensor_type=_lowerCamelCase )












'''simple docstring'''



from __future__ import annotations

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

from matplotlib import pyplot as plt




def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			__lowercase										=							(low + high) // 2
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       mid + 1		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_cross_sum(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			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						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1

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

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

			return max_left, max_right, (left_sum + right_sum)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							[randint(1		,       lowerCamelCase_				) for _ in range(lowerCamelCase_				)]
			__lowercase										=							time.time()
			max_subarray(lowerCamelCase_		,       0		,       input_size - 1				)
			__lowercase										=							time.time()
			return end - start




def    _lowerCAmelCase						(				):
			__lowercase										=							[1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
			__lowercase										=							[time_max_subarray(lowerCamelCase_				) for input_size in input_sizes]
			print('''No of Inputs\t\tTime Taken'''				)
			for input_size, runtime in zip(lowerCamelCase_		,       lowerCamelCase_				):
						print(lowerCamelCase_		,       '''\t\t'''		,       lowerCamelCase_				)
			plt.plot(lowerCamelCase_		,       lowerCamelCase_				)
			plt.xlabel('''Number of Inputs'''				)
			plt.ylabel('''Time taken in seconds'''				)
			plt.show()


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

	testmod()





