





import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCamelCase__											= [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]





def     _UpperCamelCase     (a__   :str       ):






							"""simple docstring"""





							for pegasus_name, hf_name in PATTERNS:
														UpperCamelCase__				     =			k.replace(a__   ,			a__       )
							return k





def     _UpperCamelCase     (a__   :dict   ,			a__   :dict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			DEFAULTS.copy()
							cfg_kwargs.update(a__       )
							UpperCamelCase__				     =			PegasusConfig(**a__       )
							UpperCamelCase__				     =			PegasusForConditionalGeneration(a__       )
							UpperCamelCase__				     =			torch_model.model.state_dict()
							UpperCamelCase__				     =			{}
							for k, v in tf_weights.items():
														UpperCamelCase__				     =			rename_state_dict_key(a__       )
														if new_k not in sd:
																					raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""       )

														if "dense" in k or "proj" in new_k:
																					UpperCamelCase__				     =			v.T
														UpperCamelCase__				     =			torch.tensor(a__   ,			dtype=sd[new_k].dtype       )
														assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
							# make sure embedding.padding_idx is respected
							UpperCamelCase__				     =			torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
							UpperCamelCase__				     =			mapping["""shared.weight"""]
							UpperCamelCase__				     =			mapping["""shared.weight"""]
							UpperCamelCase__				     =			{k: torch.zeros_like(a__       ) for k, v in sd.items() if k.endswith("""bias"""       ) and k not in mapping}
							mapping.update(**a__       )
							UpperCamelCase__     ,     UpperCamelCase__				     =			torch_model.model.load_state_dict(a__   ,			strict=a__       )
							UpperCamelCase__				     =			[
							    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
							]
							assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
							assert extra == [], f"""no matches found for the following tf keys {extra}"""
							return torch_model





def     _UpperCamelCase     (a__   :List[Any]="./ckpt/aeslc/model.ckpt-32000"       ):






							"""simple docstring"""





							UpperCamelCase__				     =			tf.train.list_variables(a__       )
							UpperCamelCase__				     =			{}
							UpperCamelCase__				     =			["""Adafactor""", """global_step"""]
							for name, shape in tqdm(a__   ,			desc="""converting tf checkpoint to dict"""       ):
														UpperCamelCase__				     =			any(pat in name for pat in ignore_name       )
														if skip_key:
																					continue
														UpperCamelCase__				     =			tf.train.load_variable(a__   ,			a__       )
														UpperCamelCase__				     =			array
							return tf_weights





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							UpperCamelCase__				     =			Path(a__       ).parent.name
							UpperCamelCase__				     =			task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
							UpperCamelCase__				     =			PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""   ,			model_max_length=a__       )
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(a__       )

							# convert model
							UpperCamelCase__				     =			get_tf_weights_as_numpy(a__       )
							UpperCamelCase__				     =			task_specific_params[f"""summarization_{dataset}"""]
							if dataset == "large":
														UpperCamelCase__				     =			task_specific_params
							UpperCamelCase__				     =			convert_pegasus(a__   ,			a__       )
							torch_model.save_pretrained(a__       )
							UpperCamelCase__				     =			torch_model.state_dict()
							sd.pop("""model.decoder.embed_positions.weight"""       )
							sd.pop("""model.encoder.embed_positions.weight"""       )
							torch.save(a__   ,			Path(a__       ) / """pytorch_model.bin"""       )


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
					parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
					UpperCamelCase__											= parser.parse_args()
					if args.save_dir is None:
										UpperCamelCase__											= Path(args.tf_ckpt_path).parent.name
										UpperCamelCase__											= os.path.join("pegasus", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)











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)




