
from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

__lowerCAmelCase					   =       logging.get_logger(__name__)


def    a      ( a		,						a		,						a	)     ->Any:




			'''simple docstring'''


			return [
			    int(1000 * (box[0] / width)	),
			    int(1000 * (box[1] / height)	),
			    int(1000 * (box[2] / width)	),
			    int(1000 * (box[3] / height)	),
			]


def    a      ( a		,						a		,						a	)     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= to_pil_image(a	)
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= pil_image.size
			SCREAMING_SNAKE_CASE     						= pytesseract.image_to_data(a		,						lang=a		,						output_type='''dict'''		,						config=a	)
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

			# filter empty words and corresponding coordinates
			SCREAMING_SNAKE_CASE     						= [idx for idx, word in enumerate(a	) if not word.strip()]
			SCREAMING_SNAKE_CASE     						= [word for idx, word in enumerate(a	) if idx not in irrelevant_indices]
			SCREAMING_SNAKE_CASE     						= [coord for idx, coord in enumerate(a	) if idx not in irrelevant_indices]
			SCREAMING_SNAKE_CASE     						= [coord for idx, coord in enumerate(a	) if idx not in irrelevant_indices]
			SCREAMING_SNAKE_CASE     						= [coord for idx, coord in enumerate(a	) if idx not in irrelevant_indices]
			SCREAMING_SNAKE_CASE     						= [coord for idx, coord in enumerate(a	) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			SCREAMING_SNAKE_CASE     						= []
			for x, y, w, h in zip(a		,						a		,						a		,						a	):
						SCREAMING_SNAKE_CASE     						= [x, y, x + w, y + h]
						actual_boxes.append(a	)

			# finally, normalize the bounding boxes
			SCREAMING_SNAKE_CASE     						= []
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(a		,						a		,						a	)	)

			assert len(a	) == len(a	), "Not as many words as there are bounding boxes"

			return words, normalized_boxes





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					str    		=	['pixel_values']







	def __init__(     self      :str   ,     lowercase      :bool = True   ,     lowercase      :Dict[str, int] = None   ,     lowercase      :PILImageResampling = PILImageResampling.BILINEAR   ,     lowercase      :bool = True   ,     lowercase      :float = 1 / 2_5_5   ,     lowercase      :bool = True   ,     lowercase      :Union[float, Iterable[float]] = None   ,     lowercase      :Union[float, Iterable[float]] = None   ,     lowercase      :bool = True   ,     lowercase      :Optional[str] = None   ,     lowercase      :Optional[str] = ""   ,     **lowercase      :Optional[int]   ,     )		->      None:







				"""simple docstring"""






				super().__init__(**lowercase			)
				SCREAMING_SNAKE_CASE     						= size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
				SCREAMING_SNAKE_CASE     						= get_size_dict(lowercase			)

				SCREAMING_SNAKE_CASE     						= do_resize
				SCREAMING_SNAKE_CASE     						= size
				SCREAMING_SNAKE_CASE     						= resample
				SCREAMING_SNAKE_CASE     						= do_rescale
				SCREAMING_SNAKE_CASE     						= rescale_value
				SCREAMING_SNAKE_CASE     						= do_normalize
				SCREAMING_SNAKE_CASE     						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				SCREAMING_SNAKE_CASE     						= image_std if image_std is not None else IMAGENET_STANDARD_STD
				SCREAMING_SNAKE_CASE     						= apply_ocr
				SCREAMING_SNAKE_CASE     						= ocr_lang
				SCREAMING_SNAKE_CASE     						= tesseract_config







	def  snake_case__     (     self      :Optional[int]   ,     lowercase      :np.ndarray   ,     lowercase      :Dict[str, int]   ,     lowercase      :PILImageResampling = PILImageResampling.BILINEAR   ,     lowercase      :Optional[Union[str, ChannelDimension]] = None   ,     **lowercase      :Union[str, Any]   ,     )		->      np.ndarray:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= get_size_dict(lowercase			)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""			)
				SCREAMING_SNAKE_CASE     						= (size['''height'''], size['''width'''])
				return resize(lowercase   ,     size=lowercase   ,     resample=lowercase   ,     data_format=lowercase   ,     **lowercase			)







	def  snake_case__     (     self      :int   ,     lowercase      :np.ndarray   ,     lowercase      :Union[int, float]   ,     lowercase      :Optional[Union[str, ChannelDimension]] = None   ,     **lowercase      :Tuple   ,     )		->      np.ndarray:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :np.ndarray   ,     lowercase      :Union[float, Iterable[float]]   ,     lowercase      :Union[float, Iterable[float]]   ,     lowercase      :Optional[Union[str, ChannelDimension]] = None   ,     **lowercase      :Dict   ,     )		->      np.ndarray:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Optional[int]   ,     lowercase      :ImageInput   ,     lowercase      :bool = None   ,     lowercase      :Dict[str, int] = None   ,     lowercase      :str=None   ,     lowercase      :bool = None   ,     lowercase      :float = None   ,     lowercase      :bool = None   ,     lowercase      :Union[float, Iterable[float]] = None   ,     lowercase      :Union[float, Iterable[float]] = None   ,     lowercase      :bool = None   ,     lowercase      :Optional[str] = None   ,     lowercase      :Optional[str] = None   ,     lowercase      :Optional[Union[str, TensorType]] = None   ,     lowercase      :ChannelDimension = ChannelDimension.FIRST   ,     **lowercase      :Optional[int]   ,     )		->      PIL.Image.Image:







				"""simple docstring"""






				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(lowercase			)
				SCREAMING_SNAKE_CASE     						= resample if resample is not None else self.resample
				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     						= apply_ocr if apply_ocr is not None else self.apply_ocr
				SCREAMING_SNAKE_CASE     						= ocr_lang if ocr_lang is not None else self.ocr_lang
				SCREAMING_SNAKE_CASE     						= tesseract_config if tesseract_config is not None else self.tesseract_config

				SCREAMING_SNAKE_CASE     						= make_list_of_images(lowercase			)

				if not valid_images(lowercase			):
							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_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('''If do_normalize is True, image_mean and image_std must be specified.'''			)

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

				# Tesseract OCR to get words + normalized bounding boxes
				if apply_ocr:
							requires_backends(self   ,     '''pytesseract'''			)
							SCREAMING_SNAKE_CASE     						= []
							SCREAMING_SNAKE_CASE     						= []
							for image in images:
										SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= apply_tesseract(lowercase   ,     lowercase   ,     lowercase			)
										words_batch.append(lowercase			)
										boxes_batch.append(lowercase			)

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

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

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

				SCREAMING_SNAKE_CASE     						= [to_channel_dimension_format(lowercase   ,     lowercase			) for image in images]

				SCREAMING_SNAKE_CASE     						= BatchFeature(data={'''pixel_values''': images}   ,     tensor_type=lowercase			)

				if apply_ocr:
							SCREAMING_SNAKE_CASE     						= words_batch
							SCREAMING_SNAKE_CASE     						= boxes_batch
				return data

def    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= len(a	)
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE     						= arr.index(max(arr[0:cur]	)	)
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE     						= arr[mi::-1] + arr[mi + 1 : len(a	)]
						# Reverse whole list
						SCREAMING_SNAKE_CASE     						= arr[cur - 1 :: -1] + arr[cur : len(a	)]
						cur -= 1
			return arr


if __name__ == "__main__":
							__lowerCAmelCase					   =       input('Enter numbers separated by a comma:\n').strip()
							__lowerCAmelCase					   =       [int(item) for item in user_input.split(',')]
							print(pancake_sort(unsorted))