





'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch






@is_pipeline_test
class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	a    :				str											=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	a    :				Dict											=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							TextaTextGenerationPipeline(model=_lowerCamelCase     ,tokenizer=_lowerCamelCase )
				return generator, ["Something to write", "Something else"]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							generator('''Something there''' )
				self.assertEqual(_lowerCamelCase     ,[{'''generated_text''': ANY(_lowerCamelCase )}] )
				# These are encoder decoder, they don't just append to incoming string
				self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there''' ) )

				__lowercase										=							generator(['''This is great !''', '''Something else''']     ,num_return_sequences=2     ,do_sample=_lowerCamelCase )
				self.assertEqual(
				    _lowerCamelCase     ,[
				        [{'''generated_text''': ANY(_lowerCamelCase )}, {'''generated_text''': ANY(_lowerCamelCase )}],
				        [{'''generated_text''': ANY(_lowerCamelCase )}, {'''generated_text''': ANY(_lowerCamelCase )}],
				    ]     ,)

				__lowercase										=							generator(
				    ['''This is great !''', '''Something else''']     ,num_return_sequences=2     ,batch_size=2     ,do_sample=_lowerCamelCase )
				self.assertEqual(
				    _lowerCamelCase     ,[
				        [{'''generated_text''': ANY(_lowerCamelCase )}, {'''generated_text''': ANY(_lowerCamelCase )}],
				        [{'''generated_text''': ANY(_lowerCamelCase )}, {'''generated_text''': ANY(_lowerCamelCase )}],
				    ]     ,)

				with self.assertRaises(_lowerCamelCase ):
							generator(4 )


	@require_torch
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							pipeline('''text2text-generation'''     ,model='''patrickvonplaten/t5-tiny-random'''     ,framework='''pt''' )
				# do_sample=False necessary for reproducibility
				__lowercase										=							generator('''Something there'''     ,do_sample=_lowerCamelCase )
				self.assertEqual(_lowerCamelCase     ,[{'''generated_text''': ''''''}] )

				__lowercase										=							3
				__lowercase										=							generator(
				    '''Something there'''     ,num_return_sequences=_lowerCamelCase     ,num_beams=_lowerCamelCase     ,)
				__lowercase										=							[
				    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
				    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
				    {'''generated_text''': ''''''},
				]
				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )

				__lowercase										=							generator('''This is a test'''     ,do_sample=_lowerCamelCase     ,num_return_sequences=2     ,return_tensors=_lowerCamelCase )
				self.assertEqual(
				    _lowerCamelCase     ,[
				        {'''generated_token_ids''': ANY(torch.Tensor )},
				        {'''generated_token_ids''': ANY(torch.Tensor )},
				    ]     ,)
				__lowercase										=							generator.model.config.eos_token_id
				__lowercase										=							'''<pad>'''
				__lowercase										=							generator(
				    ['''This is a test''', '''This is a second test''']     ,do_sample=_lowerCamelCase     ,num_return_sequences=2     ,batch_size=2     ,return_tensors=_lowerCamelCase     ,)
				self.assertEqual(
				    _lowerCamelCase     ,[
				        [
				            {'''generated_token_ids''': ANY(torch.Tensor )},
				            {'''generated_token_ids''': ANY(torch.Tensor )},
				        ],
				        [
				            {'''generated_token_ids''': ANY(torch.Tensor )},
				            {'''generated_token_ids''': ANY(torch.Tensor )},
				        ],
				    ]     ,)


	@require_tf
	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							pipeline('''text2text-generation'''     ,model='''patrickvonplaten/t5-tiny-random'''     ,framework='''tf''' )
				# do_sample=False necessary for reproducibility
				__lowercase										=							generator('''Something there'''     ,do_sample=_lowerCamelCase )
				self.assertEqual(_lowerCamelCase     ,[{'''generated_text''': ''''''}] )












'''simple docstring'''



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_SCREAMING_SNAKE_CASE					=    0

_SCREAMING_SNAKE_CASE					=    [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

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

_SCREAMING_SNAKE_CASE					=    tuple[int, int]






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							pos_x
				__lowercase										=							pos_y
				__lowercase										=							(pos_y, pos_x)
				__lowercase										=							goal_x
				__lowercase										=							goal_y
				__lowercase										=							g_cost
				__lowercase										=							parent
				__lowercase										=							self.calculate_heuristic()
				__lowercase										=							self.g_cost + self.h_cost


	def 						_UpperCAmelCase     (self )							->							float:







				'''simple docstring'''





				__lowercase										=							self.pos_x - self.goal_x
				__lowercase										=							self.pos_y - self.goal_y
				if HEURISTIC == 1:
							return abs(_lowerCamelCase ) + abs(_lowerCamelCase )
				else:
							return sqrt(dy**2 + dx**2 )


	def __lt__(self     ,_lowerCamelCase )							->							bool:







				'''simple docstring'''





				return self.f_cost < other.f_cost






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							Node(start[1]     ,start[0]     ,goal[1]     ,goal[0]     ,0     ,_lowerCamelCase )
				__lowercase										=							Node(goal[1]     ,goal[0]     ,goal[1]     ,goal[0]     ,99999     ,_lowerCamelCase )

				__lowercase										=							[self.start]
				__lowercase										=							[]

				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





				while self.open_nodes:
							# Open Nodes are sorted using __lt__
							self.open_nodes.sort()
							__lowercase										=							self.open_nodes.pop(0 )

							if current_node.pos == self.target.pos:
										return self.retrace_path(_lowerCamelCase )

							self.closed_nodes.append(_lowerCamelCase )
							__lowercase										=							self.get_successors(_lowerCamelCase )

							for child_node in successors:
										if child_node in self.closed_nodes:
													continue

										if child_node not in self.open_nodes:
													self.open_nodes.append(_lowerCamelCase )
										else:
													# retrieve the best current path
													__lowercase										=							self.open_nodes.pop(self.open_nodes.index(_lowerCamelCase ) )

													if child_node.g_cost < better_node.g_cost:
																self.open_nodes.append(_lowerCamelCase )
													else:
																self.open_nodes.append(_lowerCamelCase )

				return [self.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[Node]:







				'''simple docstring'''





				__lowercase										=							[]
				for action in delta:
							__lowercase										=							parent.pos_x + action[1]
							__lowercase										=							parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_lowerCamelCase ) - 1):
										continue

							if grid[pos_y][pos_x] != 0:
										continue

							successors.append(
							    Node(
							        _lowerCamelCase     ,_lowerCamelCase     ,self.target.pos_y     ,self.target.pos_x     ,parent.g_cost + 1     ,_lowerCamelCase     ,) )
				return successors


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





				__lowercase										=							node
				__lowercase										=							[]
				while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x) )
							__lowercase										=							current_node.parent
				path.reverse()
				return path






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





				while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
							self.fwd_astar.open_nodes.sort()
							self.bwd_astar.open_nodes.sort()
							__lowercase										=							self.fwd_astar.open_nodes.pop(0 )
							__lowercase										=							self.bwd_astar.open_nodes.pop(0 )

							if current_bwd_node.pos == current_fwd_node.pos:
										return self.retrace_bidirectional_path(
										    _lowerCamelCase     ,_lowerCamelCase )

							self.fwd_astar.closed_nodes.append(_lowerCamelCase )
							self.bwd_astar.closed_nodes.append(_lowerCamelCase )

							__lowercase										=							current_bwd_node
							__lowercase										=							current_fwd_node

							__lowercase										=							{
							    self.fwd_astar: self.fwd_astar.get_successors(_lowerCamelCase ),
							    self.bwd_astar: self.bwd_astar.get_successors(_lowerCamelCase ),
							}

							for astar in [self.fwd_astar, self.bwd_astar]:
										for child_node in successors[astar]:
													if child_node in astar.closed_nodes:
																continue

													if child_node not in astar.open_nodes:
																astar.open_nodes.append(_lowerCamelCase )
													else:
																# retrieve the best current path
																__lowercase										=							astar.open_nodes.pop(
																    astar.open_nodes.index(_lowerCamelCase ) )

																if child_node.g_cost < better_node.g_cost:
																			astar.open_nodes.append(_lowerCamelCase )
																else:
																			astar.open_nodes.append(_lowerCamelCase )

				return [self.fwd_astar.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





				__lowercase										=							self.fwd_astar.retrace_path(_lowerCamelCase )
				__lowercase										=							self.bwd_astar.retrace_path(_lowerCamelCase )
				bwd_path.pop()
				bwd_path.reverse()
				__lowercase										=							fwd_path + bwd_path
				return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	_SCREAMING_SNAKE_CASE					=    (0, 0)
	_SCREAMING_SNAKE_CASE					=    (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    AStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    a_star.search()
	_SCREAMING_SNAKE_CASE					=    time.time() - start_time
	print(f'''AStar execution time = {end_time:f} seconds''')

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    BidirectionalAStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    time.time() - bd_start_time
	print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





