



"""simple docstring"""




import heapq
import sys

import numpy as np

_lowerCAmelCase    : Any							=							tuple[int, int]






class      UpperCAmelCase_  :



      def __init__(					self					:     str						):
           _UpperCAmelCase      :     List[str]        =   []
           _UpperCAmelCase      :     str        =   set()



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



      def 						snake_case_ (					self					:     List[Any]						):
           return len(self.elements						) == 0



      def 						snake_case_ (					self					:     Dict					,     A					:     Any					,     A					:     str						):
           if item not in self.set:
                heapq.heappush(self.elements					,     (priority, item)						)
                self.set.add(A						)
           else:
                # update
                # print("update", item)
                _UpperCAmelCase      :     Optional[int]        =   []
                ((_UpperCAmelCase)    ,	(_UpperCAmelCase))      :     Any        =   heapq.heappop(self.elements						)
                while x != item:
                     temp.append((pri, x)						)
                     ((_UpperCAmelCase)    ,	(_UpperCAmelCase))      :     str        =   heapq.heappop(self.elements						)
                temp.append((priority, item)						)
                for pro, xxx in temp:
                     heapq.heappush(self.elements					,     (pro, xxx)						)



      def 						snake_case_ (					self					:     List[Any]					,     A					:     str						):
           if item in self.set:
                self.set.remove(A						)
                _UpperCAmelCase      :     int        =   []
                ((_UpperCAmelCase)    ,	(_UpperCAmelCase))      :     Dict        =   heapq.heappop(self.elements						)
                while x != item:
                     temp.append((pro, x)						)
                     ((_UpperCAmelCase)    ,	(_UpperCAmelCase))      :     Union[str, Any]        =   heapq.heappop(self.elements						)
                for prito, yyy in temp:
                     heapq.heappush(self.elements					,     (prito, yyy)						)



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



      def 						snake_case_ (					self					:     Any						):
           ((_UpperCAmelCase)    ,	(_UpperCAmelCase))      :     Any        =   heapq.heappop(self.elements						)
           self.set.remove(A						)
           return (priority, item)







def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						TPos						,  SCREAMING_SNAKE_CASE__     :						TPos    )  ->       Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[Any]        =   np.array(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   np.array(SCREAMING_SNAKE_CASE__    )
     return np.linalg.norm(a - b    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						TPos						,  SCREAMING_SNAKE_CASE__     :						TPos    )  ->       Optional[Any]:





     '''simple docstring'''

     return consistent_heuristic(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ) // t

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						TPos						,  SCREAMING_SNAKE_CASE__     :						TPos    )  ->       Dict:





     '''simple docstring'''

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						TPos						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						TPos						,  SCREAMING_SNAKE_CASE__     :						dict[TPos, float]    )  ->       int:





     '''simple docstring'''

     _UpperCAmelCase      :     Tuple        =   g_function[start] + Wa * heuristics[i](SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return ans

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   np.chararray((n, n)    )
     for i in range(SCREAMING_SNAKE_CASE__    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               _UpperCAmelCase      :     Optional[int]        =   "*"

     for i in range(SCREAMING_SNAKE_CASE__    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               if (j, (n - 1) - i) in blocks:
                    _UpperCAmelCase      :     Any        =   "#"

     _UpperCAmelCase      :     Optional[Any]        =   "-"
     _UpperCAmelCase      :     List[Any]        =   back_pointer[goal]
     while x != start:
          ((_UpperCAmelCase)    ,	(_UpperCAmelCase))      :     List[Any]        =   x
          # print(x)
          _UpperCAmelCase      :     Optional[Any]        =   "-"
          _UpperCAmelCase      :     int        =   back_pointer[x]
     _UpperCAmelCase      :     Union[str, Any]        =   "-"

     for i in range(SCREAMING_SNAKE_CASE__    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               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      :     int        =   back_pointer[goal]
     while x != start:
          print(SCREAMING_SNAKE_CASE__						,  end=" "    )
          _UpperCAmelCase      :     Optional[Any]        =   back_pointer[x]
     print(SCREAMING_SNAKE_CASE__    )
     sys.exit()

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						TPos    )  ->       List[Any]:





     '''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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Tuple						,  )  ->       Optional[int]:





     '''simple docstring'''

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

     for neighbours in [left, right, up, down]:
          if neighbours not in blocks:
               if valid(SCREAMING_SNAKE_CASE__    ) and neighbours not in visited:
                    # print("neighbour", neighbours)
                    visited.add(SCREAMING_SNAKE_CASE__    )
                    _UpperCAmelCase      :     int        =   -1
                    _UpperCAmelCase      :     int        =   float("inf"    )

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

def 						__snake_case						(				)  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[Any]        =   []
     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    : Optional[int]							=							{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_lowerCAmelCase    : Any							=							[
    (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    : Optional[int]							=							make_common_ground()


_lowerCAmelCase    : Union[str, Any]							=							blocks_blk
# hyper parameters
_lowerCAmelCase    : Dict							=							1
_lowerCAmelCase    : Any							=							1
_lowerCAmelCase    : int							=							20
_lowerCAmelCase    : int							=							3  # one consistent and two other inconsistent

# start and end destination
_lowerCAmelCase    : Union[str, Any]							=							(0, 0)
_lowerCAmelCase    : Tuple							=							(n - 1, n - 1)

_lowerCAmelCase    : Union[str, Any]							=							1

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						TPos						,  SCREAMING_SNAKE_CASE__     :						TPos						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   {start: 0, goal: float("inf"    )}
     _UpperCAmelCase      :     Dict        =   {start: -1, goal: -1}
     _UpperCAmelCase      :     Any        =   []
     _UpperCAmelCase      :     Tuple        =   set()

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

     _UpperCAmelCase      :     list[int]        =   []
     _UpperCAmelCase      :     list[int]        =   []
     while open_list[0].minkey() < float("inf"    ):
          for i in range(1						,  SCREAMING_SNAKE_CASE__    ):
               # 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(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
                    else:
                         _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   open_list[i].top_show()
                         visited.add(SCREAMING_SNAKE_CASE__    )
                         expand_state(
                             SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  )
                         close_list_inad.append(SCREAMING_SNAKE_CASE__    )
               else:
                    if g_function[goal] <= open_list[0].minkey():
                         if g_function[goal] < float("inf"    ):
                              do_something(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
                    else:
                         _UpperCAmelCase      :     Optional[int]        =   open_list[0].top_show()
                         visited.add(SCREAMING_SNAKE_CASE__    )
                         expand_state(
                             SCREAMING_SNAKE_CASE__						,  0						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  )
                         close_list_anchor.append(SCREAMING_SNAKE_CASE__    )
     print("No path found to goal"    )
     print()
     for i in range(n - 1						,  -1						,  -1    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               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 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


_lowerCAmelCase    : List[str]							=							[
    # 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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     for pegasus_name, hf_name in PATTERNS:
          _UpperCAmelCase      :     str        =   k.replace(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return k

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    )  ->       PegasusForConditionalGeneration:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusConfig(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   torch_model.model.state_dict()
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     for k, v in tf_weights.items():
          _UpperCAmelCase      :     Union[str, Any]        =   rename_state_dict_key(SCREAMING_SNAKE_CASE__    )
          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      :     Any        =   v.T
          _UpperCAmelCase      :     str        =   torch.tensor(SCREAMING_SNAKE_CASE__						,  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      :     Tuple        =   torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]    )
     _UpperCAmelCase      :     Any        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   {k: torch.zeros_like(SCREAMING_SNAKE_CASE__    ) for k, v in sd.items() if k.endswith("bias"    ) and k not in mapping}
     mapping.update(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__						,  strict=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   [
         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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict="./ckpt/aeslc/model.ckpt-32000"    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   tf.train.list_variables(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     _UpperCAmelCase      :     Optional[Any]        =   ["Adafactor", "global_step"]
     for name, shape in tqdm(SCREAMING_SNAKE_CASE__						,  desc="converting tf checkpoint to dict"    ):
          _UpperCAmelCase      :     Union[str, Any]        =   any(pat in name for pat in ignore_name    )
          if skip_key:
               continue
          _UpperCAmelCase      :     int        =   tf.train.load_variable(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   array
     return tf_weights

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   Path(SCREAMING_SNAKE_CASE__    ).parent.name
     _UpperCAmelCase      :     Tuple        =   task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
     _UpperCAmelCase      :     Dict        =   PegasusTokenizer.from_pretrained("sshleifer/pegasus"						,  model_max_length=SCREAMING_SNAKE_CASE__    )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(SCREAMING_SNAKE_CASE__    )

     # convert model
     _UpperCAmelCase      :     Union[str, Any]        =   get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          _UpperCAmelCase      :     Optional[int]        =   task_specific_params
     _UpperCAmelCase      :     str        =   convert_pegasus(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     torch_model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   torch_model.state_dict()
     sd.pop("model.decoder.embed_positions.weight"    )
     sd.pop("model.encoder.embed_positions.weight"    )
     torch.save(SCREAMING_SNAKE_CASE__						,  Path(SCREAMING_SNAKE_CASE__    ) / "pytorch_model.bin"    )


if __name__ == "__main__":
 _lowerCAmelCase    : Union[str, Any]							=							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.")
 _lowerCAmelCase    : Union[str, Any]							=							parser.parse_args()
 if args.save_dir is None:
  _lowerCAmelCase    : Tuple							=							Path(args.tf_ckpt_path).parent.name
  _lowerCAmelCase    : Dict							=							os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
