


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tempfile.mkdtemp()

										# fmt: off
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
										# fmt: on
										SCREAMING_SNAKE_CASE_		:     str					=					dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__       )       )       )       )
										SCREAMING_SNAKE_CASE_		:     Any					=					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{'unk_token': '<unk>'}

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file']       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['merges_file']       )
										with open(self.vocab_file			,						'w'			,						encoding='utf-8'       ) as fp:
													fp.write(json.dumps(lowerCAmelCase__       ) + '\n'       )
										with open(self.merges_file			,						'w'			,						encoding='utf-8'       ) as fp:
													fp.write('\n'.join(lowerCAmelCase__       )       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					{
										    'do_resize': True,
										    'size': 2_0,
										    'do_center_crop': True,
										    'crop_size': 1_8,
										    'do_normalize': True,
										    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										}
										SCREAMING_SNAKE_CASE_		:     Dict					=					os.path.join(self.tmpdirname			,						lowerCAmelCase__       )
										with open(self.image_processor_file			,						'w'			,						encoding='utf-8'       ) as fp:
													json.dump(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return CLIPTokenizer.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return CLIPImageProcessor.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										shutil.rmtree(self.tmpdirname       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[np.random.randint(2_5_5			,						size=(3, 3_0, 4_0_0)			,						dtype=np.uinta       )]

										SCREAMING_SNAKE_CASE_		:     str					=					[Image.fromarray(np.moveaxis(lowerCAmelCase__			,						0			,						-1       )       ) for x in image_inputs]

										return image_inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_tokenizer()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_rust_tokenizer()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_image_processor()

										SCREAMING_SNAKE_CASE_		:     int					=					CLIPProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )
										processor_slow.save_pretrained(self.tmpdirname       )
										SCREAMING_SNAKE_CASE_		:     Any					=					CLIPProcessor.from_pretrained(self.tmpdirname			,						use_fast=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					CLIPProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )
										processor_fast.save_pretrained(self.tmpdirname       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					CLIPProcessor.from_pretrained(self.tmpdirname       )

										self.assertEqual(processor_slow.tokenizer.get_vocab()			,						tokenizer_slow.get_vocab()       )
										self.assertEqual(processor_fast.tokenizer.get_vocab()			,						tokenizer_fast.get_vocab()       )
										self.assertEqual(tokenizer_slow.get_vocab()			,						tokenizer_fast.get_vocab()       )
										self.assertIsInstance(processor_slow.tokenizer			,						lowerCAmelCase__       )
										self.assertIsInstance(processor_fast.tokenizer			,						lowerCAmelCase__       )

										self.assertEqual(processor_slow.image_processor.to_json_string()			,						image_processor.to_json_string()       )
										self.assertEqual(processor_fast.image_processor.to_json_string()			,						image_processor.to_json_string()       )
										self.assertIsInstance(processor_slow.image_processor			,						lowerCAmelCase__       )
										self.assertIsInstance(processor_fast.image_processor			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					CLIPProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()       )
										processor.save_pretrained(self.tmpdirname       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_tokenizer(bos_token='(BOS)'			,						eos_token='(EOS)'       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_image_processor(do_normalize=lowerCAmelCase__			,						padding_value=1.0       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					CLIPProcessor.from_pretrained(
										    self.tmpdirname			,						bos_token='(BOS)'			,						eos_token='(EOS)'			,						do_normalize=lowerCAmelCase__			,						padding_value=1.0       )

										self.assertEqual(processor.tokenizer.get_vocab()			,						tokenizer_add_kwargs.get_vocab()       )
										self.assertIsInstance(processor.tokenizer			,						lowerCAmelCase__       )

										self.assertEqual(processor.image_processor.to_json_string()			,						image_processor_add_kwargs.to_json_string()       )
										self.assertIsInstance(processor.image_processor			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     str					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Any					=					CLIPProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					image_processor(lowerCAmelCase__			,						return_tensors='np'       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					processor(images=lowerCAmelCase__			,						return_tensors='np'       )

										for key in input_image_proc.keys():
													self.assertAlmostEqual(input_image_proc[key].sum()			,						input_processor[key].sum()			,						delta=1E-2       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     str					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     str					=					CLIPProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					'lower newer'

										SCREAMING_SNAKE_CASE_		:     Any					=					processor(text=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer(lowerCAmelCase__       )

										for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]			,						encoded_processor[key]       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					CLIPProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     Any					=					processor(text=lowerCAmelCase__			,						images=lowerCAmelCase__       )

										self.assertListEqual(list(inputs.keys()       )			,						['input_ids', 'attention_mask', 'pixel_values']       )

										# test if it raises when no input is passed
										with pytest.raises(lowerCAmelCase__       ):
													processor()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					CLIPProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					processor.batch_decode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer.batch_decode(lowerCAmelCase__       )

										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     str					=					CLIPProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     str					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     Tuple					=					processor(text=lowerCAmelCase__			,						images=lowerCAmelCase__       )

										self.assertListEqual(list(inputs.keys()       )			,						processor.model_input_names       )









from __future__ import annotations

lowerCAmelCase__			:      Union[str, Any]								=[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def 						a__			(				A__,      A__,      A__,      A__,      A__,      ):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the reference grid
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the action grid

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

			SCREAMING_SNAKE_CASE_		:     Dict					=					False  # flag that is set when search is complete
			SCREAMING_SNAKE_CASE_		:     Tuple					=					False  # flag set if we can't find expand

			while not found and not resign:
						if len(A__				) == 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_		:     Any					=					cell.pop()
									SCREAMING_SNAKE_CASE_		:     Any					=					next_cell[2]
									SCREAMING_SNAKE_CASE_		:     str					=					next_cell[3]
									SCREAMING_SNAKE_CASE_		:     Tuple					=					next_cell[1]

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

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
			for i in range(len(A__				)				):
						path.append(invpath[len(A__				) - 1 - i]				)
			return path, action


if __name__ == "__main__":
				lowerCAmelCase__			:      Union[str, Any]								=[
				    [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],
				]

				lowerCAmelCase__			:      Any								=[0, 0]
				# all coordinates are given in format [y,x]
				lowerCAmelCase__			:      List[str]								=[len(grid) - 1, len(grid[0]) - 1]
				lowerCAmelCase__			:      str								=1

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

				lowerCAmelCase__							,lowerCAmelCase__			:      Any								=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])





