






import heapq
import sys

import numpy as np

A_		:		List[str]		     =			tuple[int, int]




class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self							):
         UpperCamelCase_: Any   				=    []
         UpperCamelCase_: Union[str, Any]   				=    set()





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





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





       def       _a   ( 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_: str   				=    []
           ((UpperCamelCase_)				,(UpperCamelCase_)): Union[str, Any]   				=    heapq.heappop(self.elements							)
           while x != item:
             temp.append((pri, x)							)
             ((UpperCamelCase_)				,(UpperCamelCase_)): Any   				=    heapq.heappop(self.elements							)
           temp.append((priority, item)							)
           for pro, xxx in temp:
             heapq.heappush(self.elements  ,      (pro, xxx)							)





       def       _a   ( self  ,      _lowerCamelCase							):
         if item in self.set:
           self.set.remove(_lowerCamelCase							)
           UpperCamelCase_: List[Any]   				=    []
           ((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       _a   ( self							):
         return self.elements[0][1]





       def       _a   ( self							):
         ((UpperCamelCase_)				,(UpperCamelCase_)): int   				=    heapq.heappop(self.elements							)
         self.set.remove(_lowerCamelCase							)
         return (priority, item)







def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Optional[int]:
  # euclidean distance
  UpperCamelCase_: Optional[Any]   				=    np.array(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    np.array(UpperCAmelCase__  )
  return np.linalg.norm(a - b  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Tuple:
  # integer division by time variable
  return consistent_heuristic(UpperCAmelCase__     ,   UpperCAmelCase__  ) // t

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    str:
  # manhattan distance
  return abs(p[0] - goal[0]  ) + abs(p[1] - goal[1]  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Union[str, Any]   				=    g_function[start] + Wa * heuristics[i](UpperCAmelCase__     ,   UpperCAmelCase__  )
  return ans

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: List[Any]   				=    np.chararray((n, n)  )
  for i in range(UpperCAmelCase__  ):
    for j in range(UpperCAmelCase__  ):
      UpperCamelCase_: Dict   				=    '*'

  for i in range(UpperCAmelCase__  ):
    for j in range(UpperCAmelCase__  ):
      if (j, (n - 1) - i) in blocks:
        UpperCamelCase_: Optional[int]   				=    '#'

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

  for i in range(UpperCAmelCase__  ):
    for j in range(UpperCAmelCase__  ):
      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_: Optional[int]   				=    back_pointer[goal]
  while x != start:
    print(UpperCAmelCase__     ,   end=' '  )
    UpperCamelCase_: Optional[int]   				=    back_pointer[x]
  print(UpperCAmelCase__  )
  sys.exit()

def   snake_case (UpperCAmelCase__  )	->    Optional[int]:
  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 (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   )	->    str:
  for itera in range(UpperCAmelCase__  ):
    open_list[itera].remove_element(UpperCAmelCase__  )
  # print("s", s)
  # print("j", j)
  ((UpperCamelCase_)				,(UpperCamelCase_)): List[str]   				=    s
  UpperCamelCase_: Optional[Any]   				=    (x - 1, y)
  UpperCamelCase_: Dict   				=    (x + 1, y)
  UpperCamelCase_: str   				=    (x, y + 1)
  UpperCamelCase_: Union[str, Any]   				=    (x, y - 1)

  for neighbours in [left, right, up, down]:
    if neighbours not in blocks:
      if valid(UpperCAmelCase__  ) and neighbours not in visited:
        # print("neighbour", neighbours)
        visited.add(UpperCAmelCase__  )
        UpperCamelCase_: Tuple   				=    -1
        UpperCamelCase_: Dict   				=    float('inf'  )

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

def   snake_case ()	->    int:
  UpperCamelCase_: Union[str, Any]   				=    []
  for x in range(1     ,   5  ):
    for y in range(1     ,   6  ):
      some_list.append((x, y)  )

  for x in range(1_5     ,   2_0  ):
    some_list.append((x, 1_7)  )

  for x in range(1_0     ,   1_9  ):
    for y in range(1     ,   1_5  ):
      some_list.append((x, y)  )

    # L block
  for x in range(1     ,   4  ):
    for y in range(1_2     ,   1_9  ):
      some_list.append((x, y)  )
  for x in range(3     ,   1_3  ):
    for y in range(1_6     ,   1_9  ):
      some_list.append((x, y)  )
  return some_list


A_		:		Union[str, Any]		     =			{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

A_		:		str		     =			[
    (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),
]
A_		:		List[Any]		     =			make_common_ground()


A_		:		Optional[Any]		     =			blocks_blk
# hyper parameters
A_		:		List[str]		     =			1
A_		:		Tuple		     =			1
A_		:		int		     =			20
A_		:		int		     =			3  # one consistent and two other inconsistent

# start and end destination
A_		:		str		     =			(0, 0)
A_		:		List[str]		     =			(n - 1, n - 1)

A_		:		Dict		     =			1

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Optional[Any]:
  UpperCamelCase_: str   				=    {start: 0, goal: float('inf'  )}
  UpperCamelCase_: Any   				=    {start: -1, goal: -1}
  UpperCamelCase_: Tuple   				=    []
  UpperCamelCase_: Optional[int]   				=    set()

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

  UpperCamelCase_: list[int]   				=    []
  UpperCamelCase_: list[int]   				=    []
  while open_list[0].minkey() < float('inf'  ):
    for i in range(1     ,   UpperCAmelCase__  ):
      # 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(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
        else:
          UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    open_list[i].top_show()
          visited.add(UpperCAmelCase__  )
          expand_state(
              UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   )
          close_list_inad.append(UpperCAmelCase__  )
      else:
        if g_function[goal] <= open_list[0].minkey():
          if g_function[goal] < float('inf'  ):
            do_something(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
        else:
          UpperCamelCase_: Tuple   				=    open_list[0].top_show()
          visited.add(UpperCAmelCase__  )
          expand_state(
              UpperCAmelCase__     ,   0     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   )
          close_list_anchor.append(UpperCAmelCase__  )
  print('No path found to goal'  )
  print()
  for i in range(n - 1     ,   -1     ,   -1  ):
    for j in range(UpperCAmelCase__  ):
      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)







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


A_		:		Tuple		     =			[
    # 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 (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    PegasusForConditionalGeneration:
  UpperCamelCase_: List[str]   				=    DEFAULTS.copy()
  cfg_kwargs.update(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusConfig(**UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusForConditionalGeneration(UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    torch_model.model.state_dict()
  UpperCamelCase_: str   				=    {}
  for k, v in tf_weights.items():
    UpperCamelCase_: Dict   				=    rename_state_dict_key(UpperCAmelCase__  )
    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_: int   				=    v.T
    UpperCamelCase_: Union[str, Any]   				=    torch.tensor(UpperCAmelCase__     ,   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_: int   				=    mapping['shared.weight']
  UpperCamelCase_: Union[str, Any]   				=    mapping['shared.weight']
  UpperCamelCase_: Dict   				=    {k: torch.zeros_like(UpperCAmelCase__  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
  mapping.update(**UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    torch_model.model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    [
      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 (UpperCAmelCase__="./ckpt/aeslc/model.ckpt-32000"  )	->    Dict:
  UpperCamelCase_: Union[str, Any]   				=    tf.train.list_variables(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    {}
  UpperCamelCase_: Dict   				=    ['Adafactor', 'global_step']
  for name, shape in tqdm(UpperCAmelCase__     ,   desc='converting tf checkpoint to dict'  ):
    UpperCamelCase_: Union[str, Any]   				=    any(pat in name for pat in ignore_name  )
    if skip_key:
      continue
    UpperCamelCase_: Dict   				=    tf.train.load_variable(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: Optional[Any]   				=    array
  return tf_weights

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[Any]   				=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,   model_max_length=UpperCAmelCase__  )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(UpperCAmelCase__  )

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


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