



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  tempfile.mkdtemp()

						# fmt: off
						lowerCAmelCase__		  =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']						)
						with open(self.vocab_file    ,'w'    ,encoding='utf-8'						) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)

						lowerCAmelCase__		  =  {
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,a_						)
						with open(self.image_processor_file    ,'w'    ,encoding='utf-8'						) as fp:
										json.dump(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return BertTokenizer.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return ViTImageProcessor.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						shutil.rmtree(self.tmpdirname						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [np.random.randint(255    ,size=(3, 30, 400)    ,dtype=np.uinta						)]

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

						return image_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_tokenizer()
						lowerCAmelCase__		  =  self.get_image_processor()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						processor.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()    ,image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						lowerCAmelCase__		  =  self.get_tokenizer(bos_token='(BOS)'    ,eos_token='(EOS)'						)
						lowerCAmelCase__		  =  self.get_image_processor(do_normalize=a_    ,padding_value=1.0						)

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname    ,bos_token='(BOS)'    ,eos_token='(EOS)'    ,do_normalize=a_    ,padding_value=1.0						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  image_processor(a_    ,return_tensors='np'						)
						lowerCAmelCase__		  =  processor(images=a_    ,return_tensors='np'						)

						for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()    ,input_processor[key].sum()    ,delta=1e-2						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'

						lowerCAmelCase__		  =  processor(text=a_						)

						lowerCAmelCase__		  =  tokenizer(a_						)

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







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

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

						# test if it raises when no input is passed
						with self.assertRaises(a_						):
										processor()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__		  =  processor.batch_decode(a_						)
						lowerCAmelCase__		  =  tokenizer.batch_decode(a_						)

						self.assertListEqual(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

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





import numpy as np


class 	__snake_case   :
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  (0, 0)
						lowerCAmelCase__		  =  None
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
		def __eq__(							self    ,a_						):







						"""simple docstring"""
						return self.position == cell.position



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.position						)



class 	__snake_case   :
		def __init__(							self    ,a_=(5, 5)						):







						"""simple docstring"""
						lowerCAmelCase__		  =  np.zeros(a_						)
						lowerCAmelCase__		  =  world_size[0]
						lowerCAmelCase__		  =  world_size[1]
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.w						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						lowerCAmelCase__		  =  cell.position[0]
						lowerCAmelCase__		  =  cell.position[1]
						lowerCAmelCase__		  =  []
						for n in neughbour_cord:
										lowerCAmelCase__		  =  current_x + n[0]
										lowerCAmelCase__		  =  current_y + n[1]
										if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
														lowerCAmelCase__		  =  Cell()
														lowerCAmelCase__		  =  (x, y)
														lowerCAmelCase__		  =  cell
														neighbours.append(a_						)
						return neighbours







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  []
				_open.append(snake_case__					)

				while _open:
								lowerCAmelCase__		  =  np.argmin([n.f for n in _open]					)
								lowerCAmelCase__		  =  _open[min_f]
								_closed.append(_open.pop(snake_case__					)					)
								if current == goal:
												break
								for n in world.get_neigbours(snake_case__					):
												for c in _closed:
																if c == n:
																				continue
												lowerCAmelCase__		  =  current.g + 1
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  n.position
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  goal.position
												lowerCAmelCase__		  =  (ya - ya) ** 2 + (xa - xa) ** 2
												lowerCAmelCase__		  =  n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(snake_case__					)
				lowerCAmelCase__		  =  []
				while current.parent is not None:
								path.append(current.position					)
								lowerCAmelCase__		  =  current.parent
				path.append(current.position					)
				return path[::-1]


if __name__ == "__main__":
	_lowerCAmelCase	:   str    =   Gridworld()
	# Start position and goal
	_lowerCAmelCase	:   List[Any]    =   Cell()
	_lowerCAmelCase	:   List[str]    =   (0, 0)
	_lowerCAmelCase	:   Optional[int]    =   Cell()
	_lowerCAmelCase	:   int    =   (4, 4)
	print(f"""path from {start.position} to {goal.position}""")
	_lowerCAmelCase	:   Optional[int]    =   astar(world, start, goal)
	#   Just for visual reasons.
	for i in s:
		_lowerCAmelCase	:   int    =   1
	print(world.w)
