




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


lowerCamelCase__ :   Union[str, Any]					=							logging.get_logger(__name__)


if is_vision_available():
   import PIL





class        lowerCamelCase_							(      __A					):


 '''simple docstring'''







 lowercase_				    =    ["pixel_values"]



 def __init__(							self  :		Union[str, Any]				,				_lowerCAmelCase  :		bool = True				,				_lowerCAmelCase  :		Dict[str, int] = None				,				_lowerCAmelCase  :		PILImageResampling = PILImageResampling.BICUBIC				,				_lowerCAmelCase  :		bool = True				,				_lowerCAmelCase  :		Dict[str, int] = None				,				_lowerCAmelCase  :		bool = True				,				_lowerCAmelCase  :		Union[int, float] = 1 / 255				,				_lowerCAmelCase  :		bool = True				,				_lowerCAmelCase  :		Optional[Union[float, List[float]]] = None				,				_lowerCAmelCase  :		Optional[Union[float, List[float]]] = None				,				_lowerCAmelCase  :		bool = True				,				**_lowerCAmelCase  :		Tuple				,				):
       super().__init__(**lowerCAmelCase_						)
       SCREAMING_SNAKE_CASE_							=			size if size is not None else {"shortest_edge": 224}
       SCREAMING_SNAKE_CASE_							=			get_size_dict(lowerCAmelCase_				,				default_to_square=lowerCAmelCase_						)
       SCREAMING_SNAKE_CASE_							=			crop_size if crop_size is not None else {"height": 224, "width": 224}
       SCREAMING_SNAKE_CASE_							=			get_size_dict(lowerCAmelCase_				,				default_to_square=lowerCAmelCase_				,				param_name='crop_size'						)

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



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		np.ndarray				,				_lowerCAmelCase  :		Dict[str, int]				,				_lowerCAmelCase  :		PILImageResampling = PILImageResampling.BICUBIC				,				_lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None				,				**_lowerCAmelCase  :		Tuple				,				):
       SCREAMING_SNAKE_CASE_							=			get_size_dict(lowerCAmelCase_				,				default_to_square=lowerCAmelCase_						)
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"						)
       SCREAMING_SNAKE_CASE_							=			get_resize_output_image_size(lowerCAmelCase_				,				size=size['shortest_edge']				,				default_to_square=lowerCAmelCase_						)
       return resize(lowerCAmelCase_				,				size=lowerCAmelCase_				,				resample=lowerCAmelCase_				,				data_format=lowerCAmelCase_				,				**lowerCAmelCase_						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		np.ndarray				,				_lowerCAmelCase  :		Dict[str, int]				,				_lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None				,				**_lowerCAmelCase  :		Optional[int]				,				):
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		np.ndarray				,				_lowerCAmelCase  :		Union[int, float]				,				_lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None				,				**_lowerCAmelCase  :		str				,				):
       return rescale(lowerCAmelCase_				,				scale=lowerCAmelCase_				,				data_format=lowerCAmelCase_				,				**lowerCAmelCase_						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		np.ndarray				,				_lowerCAmelCase  :		Union[float, List[float]]				,				_lowerCAmelCase  :		Union[float, List[float]]				,				_lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None				,				**_lowerCAmelCase  :		Optional[int]				,				):
       return normalize(lowerCAmelCase_				,				mean=lowerCAmelCase_				,				std=lowerCAmelCase_				,				data_format=lowerCAmelCase_				,				**lowerCAmelCase_						)




 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		ImageInput				,				_lowerCAmelCase  :		bool = None				,				_lowerCAmelCase  :		Dict[str, int] = None				,				_lowerCAmelCase  :		PILImageResampling = None				,				_lowerCAmelCase  :		bool = None				,				_lowerCAmelCase  :		int = None				,				_lowerCAmelCase  :		bool = None				,				_lowerCAmelCase  :		float = None				,				_lowerCAmelCase  :		bool = None				,				_lowerCAmelCase  :		Optional[Union[float, List[float]]] = None				,				_lowerCAmelCase  :		Optional[Union[float, List[float]]] = None				,				_lowerCAmelCase  :		bool = None				,				_lowerCAmelCase  :		Optional[Union[str, TensorType]] = None				,				_lowerCAmelCase  :		Optional[ChannelDimension] = ChannelDimension.FIRST				,				**_lowerCAmelCase  :		List[str]				,				):
       SCREAMING_SNAKE_CASE_							=			do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE_							=			size if size is not None else self.size
       SCREAMING_SNAKE_CASE_							=			get_size_dict(lowerCAmelCase_				,				param_name='size'				,				default_to_square=lowerCAmelCase_						)
       SCREAMING_SNAKE_CASE_							=			resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE_							=			do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE_							=			crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE_							=			get_size_dict(lowerCAmelCase_				,				param_name='crop_size'				,				default_to_square=lowerCAmelCase_						)
       SCREAMING_SNAKE_CASE_							=			do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE_							=			rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE_							=			do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE_							=			image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE_							=			image_std if image_std is not None else self.image_std
       SCREAMING_SNAKE_CASE_							=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       SCREAMING_SNAKE_CASE_							=			make_list_of_images(lowerCAmelCase_						)

       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.'						)

       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:
             SCREAMING_SNAKE_CASE_							=			[convert_to_rgb(lowerCAmelCase_						) for image in images]

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

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

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

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

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

       SCREAMING_SNAKE_CASE_							=			[to_channel_dimension_format(lowerCAmelCase_				,				lowerCAmelCase_						) for image in images]

       SCREAMING_SNAKE_CASE_							=			{"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 				snake_case      (   A__	,A__	,A__						):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			UpperCAmelCase_     : Dict			     =					(low + high) // 2
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Any			     =					max_subarray(A__	,A__	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					max_subarray(A__	,mid + 1	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					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 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					float("-inf"						), -1
			UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					float("-inf"						), -1

			UpperCAmelCase_     : int | float			     =					0
			for i in range(A__	,low - 1	,-1						):
						summ += arr[i]
						if summ > left_sum:
									UpperCAmelCase_     : str			     =					summ
									UpperCAmelCase_     : Any			     =					i

			UpperCAmelCase_     : Dict			     =					0
			for i in range(mid + 1	,high + 1						):
						summ += arr[i]
						if summ > right_sum:
									UpperCAmelCase_     : List[Any]			     =					summ
									UpperCAmelCase_     : Optional[Any]			     =					i

			return max_left, max_right, (left_sum + right_sum)

def 				snake_case      (   A__						):
			UpperCAmelCase_     : str			     =					[randint(1	,A__						) for _ in range(A__						)]
			UpperCAmelCase_     : str			     =					time.time()
			max_subarray(A__	,0	,input_size - 1						)
			UpperCAmelCase_     : int			     =					time.time()
			return end - start

def 				snake_case      (   ):
			UpperCAmelCase_     : int			     =					[10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
			UpperCAmelCase_     : List[str]			     =					[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()



