






'''simple docstring'''







import heapq
import sys

import numpy as np

__lowerCAmelCase									=   tuple[int, int]





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      str       )      ->      Any:
							a_		:	Optional[Any]									=   []
							a_		:	int									=   set()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      str:
							if not self.empty():
											return self.elements[0][0]
							else:
											return float('''inf'''       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							return len(self.elements       ) == 0






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      Union[str, Any]:
							if item not in self.set:
											heapq.heappush(self.elements	,    (priority, item)       )
											self.set.add(__SCREAMING_SNAKE_CASE       )
							else:
											# update
											# print("update", item)
											a_		:	Any									=   []
											((a_)					,      (a_))		:	Any									=   heapq.heappop(self.elements       )
											while x != item:
															temp.append((pri, x)       )
															((a_)					,      (a_))		:	Optional[Any]									=   heapq.heappop(self.elements       )
											temp.append((priority, item)       )
											for pro, xxx in temp:
															heapq.heappush(self.elements	,    (pro, xxx)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      Dict:
							if item in self.set:
											self.set.remove(__SCREAMING_SNAKE_CASE       )
											a_		:	str									=   []
											((a_)					,      (a_))		:	Union[str, Any]									=   heapq.heappop(self.elements       )
											while x != item:
															temp.append((pro, x)       )
															((a_)					,      (a_))		:	Dict									=   heapq.heappop(self.elements       )
											for prito, yyy in temp:
															heapq.heappush(self.elements	,    (prito, yyy)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[Any]:
							return self.elements[0][1]







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      List[Any]:
							((a_)					,      (a_))		:	Union[str, Any]									=   heapq.heappop(self.elements       )
							self.set.remove(__SCREAMING_SNAKE_CASE       )
							return (priority, item)





def 		_UpperCAmelCase (				__A				: TPos    ,		__A				: TPos   ):
				# euclidean distance
				a_		:	List[str]									=   np.array(__A   )
				a_		:	Tuple									=   np.array(__A   )
				return np.linalg.norm(a - b   )





def 		_UpperCAmelCase (				__A				: TPos    ,		__A				: TPos   ):
				# integer division by time variable
				return consistent_heuristic(__A    ,		__A   ) // t





def 		_UpperCAmelCase (				__A				: TPos    ,		__A				: TPos   ):
				# manhattan distance
				return abs(p[0] - goal[0]   ) + abs(p[1] - goal[1]   )





def 		_UpperCAmelCase (				__A				: TPos    ,		__A				: int    ,		__A				: TPos    ,		__A				: dict[TPos, float]   ):
				a_		:	Tuple									=   g_function[start] + Wa * heuristics[i](__A    ,		__A   )
				return ans





def 		_UpperCAmelCase (				__A				: Optional[int]    ,		__A				: Optional[int]    ,		__A				: List[Any]   ):
				a_		:	Any									=   np.chararray((n, n)   )
				for i in range(__A   ):
								for j in range(__A   ):
												a_		:	Tuple									=   '''*'''

				for i in range(__A   ):
								for j in range(__A   ):
												if (j, (n - 1) - i) in blocks:
																a_		:	Any									=   '''#'''

				a_		:	int									=   '''-'''
				a_		:	Optional[Any]									=   back_pointer[goal]
				while x != start:
								((a_)					,      (a_))		:	Dict									=   x
								# print(x)
								a_		:	List[str]									=   '''-'''
								a_		:	Union[str, Any]									=   back_pointer[x]
				a_		:	Tuple									=   '''-'''

				for i in range(__A   ):
								for j in range(__A   ):
												if (i, j) == (0, n - 1):
																print(grid[i][j]    ,		end=''' '''   )
																print('''<-- End position'''    ,		end=''' '''   )
												else:
																print(grid[i][j]    ,		end=''' '''   )
								print()
				print('''^'''   )
				print('''Start position'''   )
				print()
				print('''# is an obstacle'''   )
				print('''- is the path taken by algorithm'''   )
				print('''PATH TAKEN BY THE ALGORITHM IS:-'''   )
				a_		:	List[Any]									=   back_pointer[goal]
				while x != start:
								print(__A    ,		end=''' '''   )
								a_		:	Dict									=   back_pointer[x]
				print(__A   )
				sys.exit()





def 		_UpperCAmelCase (				__A				: TPos   ):
				if p[0] < 0 or p[0] > n - 1:
								return False
				if p[1] < 0 or p[1] > n - 1:
								return False
				return True





def 		_UpperCAmelCase (				__A				: Tuple    ,		__A				: Tuple    ,		__A				: Optional[int]    ,		__A				: List[Any]    ,		__A				: int    ,		__A				: Optional[int]    ,		__A				: Optional[Any]    ,		__A				: Tuple    ,		):
				for itera in range(__A   ):
								open_list[itera].remove_element(__A   )
				# print("s", s)
				# print("j", j)
				((a_)					,      (a_))		:	List[Any]									=   s
				a_		:	List[Any]									=   (x - 1, y)
				a_		:	Optional[Any]									=   (x + 1, y)
				a_		:	Dict									=   (x, y + 1)
				a_		:	Optional[Any]									=   (x, y - 1)

				for neighbours in [left, right, up, down]:
								if neighbours not in blocks:
												if valid(__A   ) and neighbours not in visited:
																# print("neighbour", neighbours)
																visited.add(__A   )
																a_		:	List[Any]									=   -1
																a_		:	List[str]									=   float('''inf'''   )

												if valid(__A   ) and g_function[neighbours] > g_function[s] + 1:
																a_		:	Dict									=   g_function[s] + 1
																a_		:	List[str]									=   s
																if neighbours not in close_list_anchor:
																				open_list[0].put(__A    ,		key(__A    ,		0    ,		__A    ,		__A   )   )
																				if neighbours not in close_list_inad:
																								for var in range(1    ,		__A   ):
																												if key(__A    ,		__A    ,		__A    ,		__A   ) <= Wa * key(
																												    __A    ,		0    ,		__A    ,		__A   ):
																																open_list[j].put(
																																    __A    ,		key(__A    ,		__A    ,		__A    ,		__A   )   )





def 		_UpperCAmelCase (				):
				a_		:	str									=   []
				for x in range(1    ,		5   ):
								for y in range(1    ,		6   ):
												some_list.append((x, y)   )

				for x in range(15    ,		20   ):
								some_list.append((x, 17)   )

				for x in range(10    ,		19   ):
								for y in range(1    ,		15   ):
												some_list.append((x, y)   )

    # L block
				for x in range(1    ,		4   ):
								for y in range(12    ,		19   ):
												some_list.append((x, y)   )
				for x in range(3    ,		13   ):
								for y in range(16    ,		19   ):
												some_list.append((x, y)   )
				return some_list


__lowerCAmelCase									=   {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__lowerCAmelCase									=   [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
__lowerCAmelCase									=   make_common_ground()


__lowerCAmelCase									=   blocks_blk
# hyper parameters
__lowerCAmelCase									=   1
__lowerCAmelCase									=   1
__lowerCAmelCase									=   20
__lowerCAmelCase									=   3  # one consistent and two other inconsistent

# start and end destination
__lowerCAmelCase									=   (0, 0)
__lowerCAmelCase									=   (n - 1, n - 1)

__lowerCAmelCase									=   1





def 		_UpperCAmelCase (				__A				: TPos    ,		__A				: TPos    ,		__A				: int   ):
				a_		:	List[str]									=   {start: 0, goal: float('''inf'''   )}
				a_		:	Union[str, Any]									=   {start: -1, goal: -1}
				a_		:	int									=   []
				a_		:	List[Any]									=   set()

				for i in range(__A   ):
								open_list.append(PriorityQueue()   )
								open_list[i].put(__A    ,		key(__A    ,		__A    ,		__A    ,		__A   )   )

				a_		:	list[int]									=   []
				a_		:	list[int]									=   []
				while open_list[0].minkey() < float('''inf'''   ):
								for i in range(1    ,		__A   ):
												# print(open_list[0].minkey(), open_list[i].minkey())
												if open_list[i].minkey() <= Wa * open_list[0].minkey():
																global t
																t += 1
																if g_function[goal] <= open_list[i].minkey():
																				if g_function[goal] < float('''inf'''   ):
																								do_something(__A    ,		__A    ,		__A   )
																else:
																				a_					,      a_		:	Optional[Any]									=   open_list[i].top_show()
																				visited.add(__A   )
																				expand_state(
																				    __A    ,		__A    ,		__A    ,		__A    ,		__A    ,		__A    ,		__A    ,		__A    ,		)
																				close_list_inad.append(__A   )
												else:
																if g_function[goal] <= open_list[0].minkey():
																				if g_function[goal] < float('''inf'''   ):
																								do_something(__A    ,		__A    ,		__A   )
																else:
																				a_		:	List[str]									=   open_list[0].top_show()
																				visited.add(__A   )
																				expand_state(
																				    __A    ,		0    ,		__A    ,		__A    ,		__A    ,		__A    ,		__A    ,		__A    ,		)
																				close_list_anchor.append(__A   )
				print('''No path found to goal'''   )
				print()
				for i in range(n - 1    ,		-1    ,		-1   ):
								for j in range(__A   ):
												if (j, i) in blocks:
																print('''#'''    ,		end=''' '''   )
												elif (j, i) in back_pointer:
																if (j, i) == (n - 1, n - 1):
																				print('''*'''    ,		end=''' '''   )
																else:
																				print('''-'''    ,		end=''' '''   )
												else:
																print('''*'''    ,		end=''' '''   )
												if (j, i) == (n - 1, n - 1):
																print('''<-- End position'''    ,		end=''' '''   )
								print()
				print('''^'''   )
				print('''Start position'''   )
				print()
				print('''# is an obstacle'''   )
				print('''- is the path taken by algorithm'''   )


if __name__ == "__main__":
		multi_a_star(start, goal, n_heuristic)












'''simple docstring'''







import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    level=os.environ.get('LOGLEVEL', 'INFO').upper(),
    stream=sys.stdout,
)

__lowerCAmelCase									=   logging.getLogger(__name__)

__lowerCAmelCase									=   {'facebook/bart-base': BartForConditionalGeneration}
__lowerCAmelCase									=   {'facebook/bart-base': BartTokenizer}





def 		_UpperCAmelCase (				):
				a_		:	int									=   argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''   )
				parser.add_argument(
				    '''--validation_file'''    ,		type=__A    ,		default=__A    ,		help='''A csv or a json file containing the validation data.'''   )
				parser.add_argument(
				    '''--max_length'''    ,		type=__A    ,		default=5    ,		help='''The maximum total input sequence length after tokenization.'''    ,		)
				parser.add_argument(
				    '''--num_beams'''    ,		type=__A    ,		default=__A    ,		help=(
				        '''Number of beams to use for evaluation. This argument will be '''
				        '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
				    )    ,		)
				parser.add_argument(
				    '''--model_name_or_path'''    ,		type=__A    ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''    ,		required=__A    ,		)
				parser.add_argument(
				    '''--config_name'''    ,		type=__A    ,		default=__A    ,		help='''Pretrained config name or path if not the same as model_name'''    ,		)
				parser.add_argument(
				    '''--device'''    ,		type=__A    ,		default='''cpu'''    ,		help='''Device where the model will be run'''    ,		)
				parser.add_argument('''--output_file_path'''    ,		type=__A    ,		default=__A    ,		help='''Where to store the final ONNX file.'''   )

				a_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

				if model_name in ["facebook/bart-base"]:
								a_		:	str									=   0
								a_		:	Any									=   None
								a_		:	int									=   0

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

				a_		:	Union[str, Any]									=   None
				a_		:	str									=   torch.jit.script(BARTBeamSearchGenerator(__A   )   )

				with torch.no_grad():
								a_		:	Any									=   '''My friends are cool but they eat too many carbs.'''
								a_		:	Dict									=   tokenizer([ARTICLE_TO_SUMMARIZE]    ,		max_length=10_24    ,		return_tensors='''pt'''   ).to(model.device   )

								a_		:	Dict									=   model.generate(
								    inputs['''input_ids''']    ,		attention_mask=inputs['''attention_mask''']    ,		num_beams=__A    ,		max_length=__A    ,		early_stopping=__A    ,		decoder_start_token_id=model.config.decoder_start_token_id    ,		)

								torch.onnx.export(
								    __A    ,		(
								        inputs['''input_ids'''],
								        inputs['''attention_mask'''],
								        num_beams,
								        max_length,
								        model.config.decoder_start_token_id,
								    )    ,		__A    ,		opset_version=14    ,		input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']    ,		output_names=['''output_ids''']    ,		dynamic_axes={
								        '''input_ids''': {0: '''batch''', 1: '''seq'''},
								        '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
								    }    ,		example_outputs=__A    ,		)

								logger.info('''Model exported to {}'''.format(__A   )   )

								a_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

								logger.info('''Deduplicated and optimized model written to {}'''.format(__A   )   )

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[Any]									=   ort_sess.run(
								    __A    ,		{
								        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
								        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
								        '''num_beams''': np.array(__A   ),
								        '''max_length''': np.array(__A   ),
								        '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id   ),
								    }    ,		)

								np.testing.assert_allclose(summary_ids.cpu().numpy()    ,		ort_out[0]    ,		rtol=1E-3    ,		atol=1E-3   )

								logger.info('''Model outputs from torch and ONNX Runtime are similar.'''   )
								logger.info('''Success.'''   )





def 		_UpperCAmelCase (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[str]									=   4

				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,		datefmt='''%m/%d/%Y %H:%M:%S'''    ,		level=logging.INFO    ,		)

				logger.setLevel(logging.INFO   )
				transformers.utils.logging.set_verbosity_error()

				a_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   load_model_tokenizer(args.model_name_or_path    ,		__A   )

				if model.config.decoder_start_token_id is None:
								raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''   )

				model.to(__A   )

				if args.max_length:
								a_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

				if args.output_file_path:
								a_		:	str									=   args.output_file_path
				else:
								a_		:	Union[str, Any]									=   '''BART.onnx'''

				logger.info('''Exporting model to ONNX'''   )
				export_and_validate_model(__A    ,		__A    ,		__A    ,		__A    ,		__A   )


if __name__ == "__main__":
		main()




