





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,
)

UpperCamelCase__											= logging.getLogger(__name__)

UpperCamelCase__											= {"facebook/bart-base": BartForConditionalGeneration}
UpperCamelCase__											= {"facebook/bart-base": BartTokenizer}





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			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."""       )

							UpperCamelCase__				     =			parser.parse_args()

							return args





def     _UpperCamelCase     (a__   :Dict   ,			a__   :Any="cpu"       ):






							"""simple docstring"""





							UpperCamelCase__				     =			model_dict[model_name].from_pretrained(a__       ).to(a__       )
							UpperCamelCase__				     =			tokenizer_dict[model_name].from_pretrained(a__       )

							if model_name in ["facebook/bart-base"]:
														UpperCamelCase__				     =			0
														UpperCamelCase__				     =			None
														UpperCamelCase__				     =			0

							return huggingface_model, tokenizer





def     _UpperCamelCase     (a__   :Tuple   ,			a__   :List[Any]   ,			a__   :Any   ,			a__   :Dict   ,			a__   :List[str]       ):






							"""simple docstring"""





							model.eval()

							UpperCamelCase__				     =			None
							UpperCamelCase__				     =			torch.jit.script(BARTBeamSearchGenerator(a__       )       )

							with torch.no_grad():
														UpperCamelCase__				     =			"""My friends are cool but they eat too many carbs."""
														UpperCamelCase__				     =			tokenizer([ARTICLE_TO_SUMMARIZE]   ,			max_length=1024   ,			return_tensors="""pt"""       ).to(model.device       )

														UpperCamelCase__				     =			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__       )       )

														UpperCamelCase__				     =			remove_dup_initializers(os.path.abspath(a__       )       )

														logger.info("""Deduplicated and optimized model written to {}""".format(a__       )       )

														UpperCamelCase__				     =			onnxruntime.InferenceSession(a__       )
														UpperCamelCase__				     =			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     ():






							"""simple docstring"""





							UpperCamelCase__				     =			parse_args()
							UpperCamelCase__				     =			5
							UpperCamelCase__				     =			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()

							UpperCamelCase__				     =			torch.device(args.device       )

							UpperCamelCase__     ,     UpperCamelCase__				     =			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:
														UpperCamelCase__				     =			args.max_length

							if args.num_beams:
														UpperCamelCase__				     =			args.num_beams

							if args.output_file_path:
														UpperCamelCase__				     =			args.output_file_path
							else:
														UpperCamelCase__				     =			"""BART.onnx"""

							logger.info("""Exporting model to ONNX"""       )
							export_and_validate_model(a__   ,			a__   ,			a__   ,			a__   ,			a__       )


if __name__ == "__main__":
					main()











import heapq
import sys

import numpy as np

UpperCamelCase__											= tuple[int, int]

class        __SCREAMING_SNAKE_CASE					:






							def __init__(						self  ):
														UpperCamelCase__				     =			[]
														UpperCamelCase__				     =			set()






							def 				_lowerCamelCase		(						self  ):
														if not self.empty():
																					return self.elements[0][0]
														else:
																					return float("""inf"""  )






							def 				_lowerCamelCase		(						self  ):
														return len(self.elements  ) == 0






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														if item not in self.set:
																					heapq.heappush(self.elements  , (priority, item)  )
																					self.set.add(__lowerCAmelCase  )
														else:
																					# update
																					# print("update", item)
																					UpperCamelCase__				     =			[]
																					((UpperCamelCase__)     ,     (UpperCamelCase__))				     =			heapq.heappop(self.elements  )
																					while x != item:
																												temp.append((pri, x)  )
																												((UpperCamelCase__)     ,     (UpperCamelCase__))				     =			heapq.heappop(self.elements  )
																					temp.append((priority, item)  )
																					for pro, xxx in temp:
																												heapq.heappush(self.elements  , (pro, xxx)  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														if item in self.set:
																					self.set.remove(__lowerCAmelCase  )
																					UpperCamelCase__				     =			[]
																					((UpperCamelCase__)     ,     (UpperCamelCase__))				     =			heapq.heappop(self.elements  )
																					while x != item:
																												temp.append((pro, x)  )
																												((UpperCamelCase__)     ,     (UpperCamelCase__))				     =			heapq.heappop(self.elements  )
																					for prito, yyy in temp:
																												heapq.heappush(self.elements  , (prito, yyy)  )






							def 				_lowerCamelCase		(						self  ):
														return self.elements[0][1]







							def 				_lowerCamelCase		(						self  ):
														((UpperCamelCase__)     ,     (UpperCamelCase__))				     =			heapq.heappop(self.elements  )
														self.set.remove(__lowerCAmelCase  )
														return (priority, item)






def     _UpperCamelCase     (a__   :TPos   ,			a__   :TPos       ):






							"""simple docstring"""





							UpperCamelCase__				     =			np.array(a__       )
							UpperCamelCase__				     =			np.array(a__       )
							return np.linalg.norm(a - b       )





def     _UpperCamelCase     (a__   :TPos   ,			a__   :TPos       ):






							"""simple docstring"""





							return consistent_heuristic(a__   ,			a__       ) // t





def     _UpperCamelCase     (a__   :TPos   ,			a__   :TPos       ):






							"""simple docstring"""





							return abs(p[0] - goal[0]       ) + abs(p[1] - goal[1]       )





def     _UpperCamelCase     (a__   :TPos   ,			a__   :int   ,			a__   :TPos   ,			a__   :dict[TPos, float]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			g_function[start] + Wa * heuristics[i](a__   ,			a__       )
							return ans





def     _UpperCamelCase     (a__   :Optional[Any]   ,			a__   :Union[str, Any]   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			np.chararray((n, n)       )
							for i in range(a__       ):
														for j in range(a__       ):
																					UpperCamelCase__				     =			"""*"""

							for i in range(a__       ):
														for j in range(a__       ):
																					if (j, (n - 1) - i) in blocks:
																												UpperCamelCase__				     =			"""#"""

							UpperCamelCase__				     =			"""-"""
							UpperCamelCase__				     =			back_pointer[goal]
							while x != start:
														((UpperCamelCase__)     ,     (UpperCamelCase__))				     =			x
														# print(x)
														UpperCamelCase__				     =			"""-"""
														UpperCamelCase__				     =			back_pointer[x]
							UpperCamelCase__				     =			"""-"""

							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:-"""       )
							UpperCamelCase__				     =			back_pointer[goal]
							while x != start:
														print(a__   ,			end=""" """       )
														UpperCamelCase__				     =			back_pointer[x]
							print(a__       )
							sys.exit()





def     _UpperCamelCase     (a__   :TPos       ):






							"""simple docstring"""





							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__   :List[Any]   ,			a__   :Tuple   ,			a__   :Union[str, Any]   ,			a__   :Any   ,			a__   :str   ,			a__   :Optional[Any]   ,			a__   :List[Any]   ,			a__   :List[str]   ,			):






							"""simple docstring"""





							for itera in range(a__       ):
														open_list[itera].remove_element(a__       )
							# print("s", s)
							# print("j", j)
							((UpperCamelCase__)     ,     (UpperCamelCase__))				     =			s
							UpperCamelCase__				     =			(x - 1, y)
							UpperCamelCase__				     =			(x + 1, y)
							UpperCamelCase__				     =			(x, y + 1)
							UpperCamelCase__				     =			(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__       )
																												UpperCamelCase__				     =			-1
																												UpperCamelCase__				     =			float("""inf"""       )

																					if valid(a__       ) and g_function[neighbours] > g_function[s] + 1:
																												UpperCamelCase__				     =			g_function[s] + 1
																												UpperCamelCase__				     =			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     ():






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							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


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

UpperCamelCase__											= [
    (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),
]
UpperCamelCase__											= make_common_ground()


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

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

UpperCamelCase__											= 1





def     _UpperCamelCase     (a__   :TPos   ,			a__   :TPos   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			{start: 0, goal: float("""inf"""       )}
							UpperCamelCase__				     =			{start: -1, goal: -1}
							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			set()

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

							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			[]
							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:
																																			UpperCamelCase__     ,     UpperCamelCase__				     =			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:
																																			UpperCamelCase__				     =			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)




