



'''simple docstring'''

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

snake_case_			=     logging.get_logger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				return [
				    int(10_00 * (box[0] / width)       ),
				    int(10_00 * (box[1] / height)       ),
				    int(10_00 * (box[2] / width)       ),
				    int(10_00 * (box[3] / height)       ),
				]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :np.ndarray    ,	_SCREAMING_SNAKE_CASE      :Optional[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[str]       ):
				SCREAMING_SNAKE_CASE  :						str      					= to_pil_image(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= pil_image.size
				SCREAMING_SNAKE_CASE  :						int      					= pytesseract.image_to_data(_SCREAMING_SNAKE_CASE    ,	lang=_SCREAMING_SNAKE_CASE    ,	output_type='''dict'''    ,	config=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

				# filter empty words and corresponding coordinates
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= [idx for idx, word in enumerate(_SCREAMING_SNAKE_CASE       ) if not word.strip()]
				SCREAMING_SNAKE_CASE  :						Any      					= [word for idx, word in enumerate(_SCREAMING_SNAKE_CASE       ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE  :						Any      					= [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE       ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE       ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE  :						int      					= [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE       ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE       ) if idx not in irrelevant_indices]

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

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

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

				return words, normalized_boxes




class 		a__     (				_lowercase	):
	__magic_name__		:    Any	    =     ["pixel_values"]


	def __init__(self : Dict,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Dict[str, int] = None,					__UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : float = 1 / 255,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Union[float, Iterable[float]] = None,					__UpperCAmelCase : Union[float, Iterable[float]] = None,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Optional[str] = None,					__UpperCAmelCase : Optional[str] = "",					**__UpperCAmelCase : List[str],					)     ->			None:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= size if size is not None else {'''height''': 224, '''width''': 224}
					SCREAMING_SNAKE_CASE  :						List[str]      					= get_size_dict(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= do_resize
					SCREAMING_SNAKE_CASE  :						List[Any]      					= size
					SCREAMING_SNAKE_CASE  :						List[str]      					= resample
					SCREAMING_SNAKE_CASE  :						List[Any]      					= do_rescale
					SCREAMING_SNAKE_CASE  :						List[Any]      					= rescale_value
					SCREAMING_SNAKE_CASE  :						Dict      					= do_normalize
					SCREAMING_SNAKE_CASE  :						List[str]      					= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					SCREAMING_SNAKE_CASE  :						Dict      					= image_std if image_std is not None else IMAGENET_STANDARD_STD
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= apply_ocr
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= ocr_lang
					SCREAMING_SNAKE_CASE  :						str      					= tesseract_config


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= get_size_dict(__UpperCAmelCase      )
					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  :						int      					= (size['''height'''], size['''width'''])
					return resize(__UpperCAmelCase,					size=__UpperCAmelCase,					resample=__UpperCAmelCase,					data_format=__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : np.ndarray,					__UpperCAmelCase : Union[int, float],					__UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None,					**__UpperCAmelCase : Dict,					)     ->			np.ndarray:

					"""simple docstring"""

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


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : np.ndarray,					__UpperCAmelCase : Union[float, Iterable[float]],					__UpperCAmelCase : Union[float, Iterable[float]],					__UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None,					**__UpperCAmelCase : List[Any],					)     ->			np.ndarray:

					"""simple docstring"""

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






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= do_resize if do_resize is not None else self.do_resize
					SCREAMING_SNAKE_CASE  :						Dict      					= size if size is not None else self.size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= get_size_dict(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= resample if resample is not None else self.resample
					SCREAMING_SNAKE_CASE  :						str      					= do_rescale if do_rescale is not None else self.do_rescale
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= rescale_factor if rescale_factor is not None else self.rescale_factor
					SCREAMING_SNAKE_CASE  :						Dict      					= do_normalize if do_normalize is not None else self.do_normalize
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= image_mean if image_mean is not None else self.image_mean
					SCREAMING_SNAKE_CASE  :						Tuple      					= image_std if image_std is not None else self.image_std
					SCREAMING_SNAKE_CASE  :						int      					= apply_ocr if apply_ocr is not None else self.apply_ocr
					SCREAMING_SNAKE_CASE  :						List[Any]      					= ocr_lang if ocr_lang is not None else self.ocr_lang
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tesseract_config if tesseract_config is not None else self.tesseract_config

					SCREAMING_SNAKE_CASE  :						Dict      					= make_list_of_images(__UpperCAmelCase      )

					if not valid_images(__UpperCAmelCase      ):
									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  :						List[str]      					= [to_numpy_array(__UpperCAmelCase      ) for image in images]

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

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

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

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

					SCREAMING_SNAKE_CASE  :						Dict      					= [to_channel_dimension_format(__UpperCAmelCase,					__UpperCAmelCase      ) for image in images]

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= BatchFeature(data={'''pixel_values''': images},					tensor_type=__UpperCAmelCase      )

					if apply_ocr:
									SCREAMING_SNAKE_CASE  :						str      					= words_batch
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= boxes_batch
					return data









'''simple docstring'''

from __future__ import annotations

snake_case_			=     [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the reference grid
				SCREAMING_SNAKE_CASE  :						Dict      					= 1
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the action grid

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= init[0]
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= init[1]
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						List[Any]      					= g + heuristic[x][y]  # cost from starting cell to destination cell
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [[f, g, x, y]]

				SCREAMING_SNAKE_CASE  :						List[str]      					= False  # flag that is set when search is complete
				SCREAMING_SNAKE_CASE  :						Any      					= False  # flag set if we can't find expand

				while not found and not resign:
								if len(_SCREAMING_SNAKE_CASE       ) == 0:
												raise ValueError('''Algorithm is unable to find solution'''       )
								else:  # to choose the least costliest action so as to move closer to the goal
												cell.sort()
												cell.reverse()
												SCREAMING_SNAKE_CASE  :						List[str]      					= cell.pop()
												SCREAMING_SNAKE_CASE  :						List[str]      					= next_cell[2]
												SCREAMING_SNAKE_CASE  :						Dict      					= next_cell[3]
												SCREAMING_SNAKE_CASE  :						str      					= next_cell[1]

												if x == goal[0] and y == goal[1]:
																SCREAMING_SNAKE_CASE  :						Dict      					= True
												else:
																for i in range(len(_SCREAMING_SNAKE_CASE       )       ):  # to try out different valid actions
																				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= x + DIRECTIONS[i][0]
																				SCREAMING_SNAKE_CASE  :						Tuple      					= y + DIRECTIONS[i][1]
																				if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE       ) and ya >= 0 and ya < len(grid[0]       ):
																								if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= g + cost
																												SCREAMING_SNAKE_CASE  :						Any      					= ga + heuristic[xa][ya]
																												cell.append([fa, ga, xa, ya]       )
																												SCREAMING_SNAKE_CASE  :						List[Any]      					= 1
																												SCREAMING_SNAKE_CASE  :						Optional[int]      					= i
				SCREAMING_SNAKE_CASE  :						Dict      					= []
				SCREAMING_SNAKE_CASE  :						List[Any]      					= goal[0]
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= goal[1]
				invpath.append([x, y]       )  # we get the reverse path from here
				while x != init[0] or y != init[1]:
								SCREAMING_SNAKE_CASE  :						str      					= x - DIRECTIONS[action[x][y]][0]
								SCREAMING_SNAKE_CASE  :						str      					= y - DIRECTIONS[action[x][y]][1]
								SCREAMING_SNAKE_CASE  :						Tuple      					= xa
								SCREAMING_SNAKE_CASE  :						Dict      					= ya
								invpath.append([x, y]       )

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				for i in range(len(_SCREAMING_SNAKE_CASE       )       ):
								path.append(invpath[len(_SCREAMING_SNAKE_CASE       ) - 1 - i]       )
				return path, action


if __name__ == "__main__":
	snake_case_			=     [
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 1, 0],
	    [0, 0, 0, 0, 1, 0],
	]

	snake_case_			=     [0, 0]
	# all coordinates are given in format [y,x]
	snake_case_			=     [len(grid) - 1, len(grid[0]) - 1]
	snake_case_			=     1

	# the cost map which pushes the path closer to the goal
	snake_case_			=     [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
	for i in range(len(grid)):
		for j in range(len(grid[0])):
			snake_case_			=     abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				snake_case_			=     99

	snake_case_			,				snake_case_			=     search(grid, init, goal, cost, heuristic)

	print("""ACTION MAP""")
	for i in range(len(action)):
		print(action[i])

	for i in range(len(path)):
		print(path[i])




