
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=['''image_processor''', '''tokenizer''']
				_lowercase     						='''BlipImageProcessor'''
				_lowercase     						=('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      False
							super().__init__(_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      self.image_processor




				def __call__(   self							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = True							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = 0							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = True							,							_UpperCamelCase = None							,							**_UpperCamelCase							,							)							-> BatchEncoding:
							if images is None and text is None:
										raise ValueError("You have to specify either images or text."	)

							# Get only text
							if images is None:
										lowerCAmelCase_         =      self.tokenizer
										lowerCAmelCase_         =      self.tokenizer(
										    text=_UpperCamelCase							,							add_special_tokens=_UpperCamelCase							,							padding=_UpperCamelCase							,							truncation=_UpperCamelCase							,							max_length=_UpperCamelCase							,							stride=_UpperCamelCase							,							pad_to_multiple_of=_UpperCamelCase							,							return_attention_mask=_UpperCamelCase							,							return_overflowing_tokens=_UpperCamelCase							,							return_special_tokens_mask=_UpperCamelCase							,							return_offsets_mapping=_UpperCamelCase							,							return_token_type_ids=_UpperCamelCase							,							return_length=_UpperCamelCase							,							verbose=_UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase							,							)
										return text_encoding

							# add pixel_values
							lowerCAmelCase_         =      self.image_processor(_UpperCamelCase							,							return_tensors=_UpperCamelCase	)

							if text is not None:
										lowerCAmelCase_         =      self.tokenizer(
										    text=_UpperCamelCase							,							add_special_tokens=_UpperCamelCase							,							padding=_UpperCamelCase							,							truncation=_UpperCamelCase							,							max_length=_UpperCamelCase							,							stride=_UpperCamelCase							,							pad_to_multiple_of=_UpperCamelCase							,							return_attention_mask=_UpperCamelCase							,							return_overflowing_tokens=_UpperCamelCase							,							return_special_tokens_mask=_UpperCamelCase							,							return_offsets_mapping=_UpperCamelCase							,							return_token_type_ids=_UpperCamelCase							,							return_length=_UpperCamelCase							,							verbose=_UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase							,							)
							else:
										lowerCAmelCase_         =      None

							if text_encoding is not None:
										encoding_image_processor.update(_UpperCamelCase	)

							return encoding_image_processor




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> Dict:
							return self.tokenizer.batch_decode(*_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> Any:
							return self.tokenizer.decode(*_UpperCamelCase							,							**_UpperCamelCase	)




				@property
				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.tokenizer.model_input_names
							lowerCAmelCase_         =      self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





from __future__ import annotations

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the reference grid
			lowerCAmelCase_         =      1
			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the action grid

			lowerCAmelCase_         =      init[0]
			lowerCAmelCase_         =      init[1]
			lowerCAmelCase_         =      0
			lowerCAmelCase_         =      g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_         =      [[f, g, x, y]]

			lowerCAmelCase_         =      False  # flag that is set when search is complete
			lowerCAmelCase_         =      False  # flag set if we can't find expand

			while not found and not resign:
						if len(__lowerCAmelCase							) == 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()
									lowerCAmelCase_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

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

			lowerCAmelCase_         =      []
			for i in range(len(__lowerCAmelCase							)							):
						path.append(invpath[len(__lowerCAmelCase							) - 1 - i]							)
			return path, action


if __name__ == "__main__":
			_A  =	[
			    [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],
			]

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

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

			_A, _A  =	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])



