'''simple docstring'''
import heapq
import sys

import numpy as np

a=  tuple[int, int]






class 			__lowercase		:







     """simple docstring"""






     def __init__(		self    ):
         __UpperCamelCase	:	List[Any]		    =  []
         __UpperCamelCase	:	Any		    =  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	:	Optional[int]		    =  []
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	Tuple		    =  heapq.heappop(self.elements    )
             while x != item:
                 temp.append((pri, x)    )
                 ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	Dict		    =  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	:	Tuple		    =  []
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	List[str]		    =  heapq.heappop(self.elements    )
             while x != item:
                 temp.append((pro, x)    )
                 ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	str		    =  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))	:	List[Any]		    =  heapq.heappop(self.elements    )
         self.set.remove(_lowerCamelCase    )
         return (priority, item)







def  _UpperCamelCase (     _a	:	TPos				,     _a	:	TPos			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  np.array(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  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	:	int		    =  g_function[start] + Wa * heuristics[i](_a				,     _a			)
    return ans







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	List[Any]				,     _a	:	Optional[int]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  np.chararray((n, n)			)
    for i in range(_a			):
        for j in range(_a			):
            __UpperCamelCase	:	Any		    =  '*'

    for i in range(_a			):
        for j in range(_a			):
            if (j, (n - 1) - i) in blocks:
                __UpperCamelCase	:	Dict		    =  '#'

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

    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	:	Any		    =  back_pointer[goal]
    while x != start:
        print(_a				,     end=' '			)
        __UpperCamelCase	:	Tuple		    =  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	:	List[Any]				,     _a	:	Optional[Any]				,     _a	:	Tuple				,     _a	:	List[Any]				,     _a	:	Optional[int]				,     _a	:	Optional[int]				,     ):




    """simple docstring"""



    for itera in range(_a			):
        open_list[itera].remove_element(_a			)
    # print("s", s)
    # print("j", j)
    ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	Union[str, Any]		    =  s
    __UpperCamelCase	:	List[str]		    =  (x - 1, y)
    __UpperCamelCase	:	Union[str, Any]		    =  (x + 1, y)
    __UpperCamelCase	:	Dict		    =  (x, y + 1)
    __UpperCamelCase	:	Tuple		    =  (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	:	List[str]		    =  -1
                __UpperCamelCase	:	List[Any]		    =  float('inf'			)

            if valid(_a			) and g_function[neighbours] > g_function[s] + 1:
                __UpperCamelCase	:	Optional[int]		    =  g_function[s] + 1
                __UpperCamelCase	:	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 (     ):




    """simple docstring"""



    __UpperCamelCase	:	Dict		    =  []
    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=  {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a=  [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (1_0, 1),
    (1_1, 1),
    (1_2, 1),
    (1_3, 1),
    (1_4, 1),
    (1_5, 1),
    (1_6, 1),
    (1_7, 1),
    (1_8, 1),
    (1_9, 1),
]
a=  make_common_ground()


a=  blocks_blk
# hyper parameters
a=  1
a=  1
a=  2_0
a=  3  # one consistent and two other inconsistent

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

a=  1







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




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  {start: 0, goal: float('inf'			)}
    __UpperCamelCase	:	List[str]		    =  {start: -1, goal: -1}
    __UpperCamelCase	:	Optional[int]		    =  []
    __UpperCamelCase	:	str		    =  set()

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

    __UpperCamelCase	:	list[int]		    =  []
    __UpperCamelCase	:	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:
                    __UpperCamelCase    ,					__UpperCamelCase	:	Tuple		    =  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	:	Optional[int]		    =  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 pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCamelCase (     _a	:	int				,     _a	:	Tuple				,     _a	:	Dict=None			):




    """simple docstring"""



    assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
    __UpperCamelCase	:	List[Any]		    =  nn.Parameter(_a			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
        __UpperCamelCase	:	Dict		    =  nn.Parameter(_a			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	str				,     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	str		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[2]			)

    set_param(
        torch_layer.self_attention.query_key				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.value				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.output.dense				,     torch.tensor(_a			).view(-1				,     _a			).contiguous().transpose(0				,     1			)				,     )







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[2]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[3]			)

    set_param(
        torch_layer.self_attention.query				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.key				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.value				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.output.dense				,     torch.tensor(_a			).view(-1				,     _a			).contiguous().transpose(0				,     1			)				,     )







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




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  weights[0][0][0]
    __UpperCamelCase	:	Tuple		    =  np.asarray(layer_norm_a[0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(layer_norm_a[1]			)
    set_param(
        torch_block.attention.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # lsh weights + output
    __UpperCamelCase	:	int		    =  weights[0][1]
    if len(_a			) < 4:
        set_layer_weights_in_torch_lsh(_a				,     torch_block.attention				,     _a			)
    else:
        set_layer_weights_in_torch_local(_a				,     torch_block.attention				,     _a			)

    # intermediate weighs
    __UpperCamelCase	:	int		    =  weights[2][0][1][2]

    # Chunked Feed Forward
    if len(_a			) == 4:
        __UpperCamelCase	:	Optional[Any]		    =  intermediate_weights[2]

    # layernorm 2
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[0][0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(intermediate_weights[0][1]			)
    set_param(
        torch_block.feed_forward.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # intermediate dense
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(intermediate_weights[1][0]			)
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[1][1]			)
    set_param(
        torch_block.feed_forward.dense.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )

    # intermediate out
    __UpperCamelCase	:	Dict		    =  np.asarray(intermediate_weights[4][0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(intermediate_weights[4][1]			)
    set_param(
        torch_block.feed_forward.output.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







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




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch_model.reformer

    # word embeds
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    set_param(
        torch_model_reformer.embeddings.word_embeddings				,     torch.tensor(_a			)				,     )

    if isinstance(weights[3]				,     _a			):
        __UpperCamelCase	:	Dict		    =  torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights			)			):
            __UpperCamelCase	:	str		    =  np.asarray(weights[3][emb_idx][0]			)
            assert (
                position_embeddings.weights[emb_idx].shape == emb_weights.shape
            ), f"""{position_embeddings[emb_idx]} emb does not match"""
            __UpperCamelCase	:	Tuple		    =  nn.Parameter(torch.tensor(_a			)			)

    __UpperCamelCase	:	int		    =  weights[5]
    assert len(torch_model_reformer.encoder.layers			) * 4 == len(
        _a			), "HF and trax model do not have the same number of layers"
    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
        __UpperCamelCase	:	Any		    =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(_a				,     _a				,     _a			)

    # output layer norm
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(weights[7][0]			)
    __UpperCamelCase	:	Any		    =  np.asarray(weights[7][1]			)
    set_param(
        torch_model_reformer.encoder.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # output embeddings
    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[9][0]			)
    __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(weights[9][1]			)
    set_param(
        torch_model.lm_head.decoder				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  ReformerConfig.from_json_file(_a			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    __UpperCamelCase	:	List[str]		    =  ReformerModelWithLMHead(_a			)

    with open(_a				,     'rb'			) as f:
        __UpperCamelCase	:	Tuple		    =  pickle.load(_a			)['weights']

    set_model_weights_in_torch(_a				,     _a				,     config.hidden_size			)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    torch.save(model.state_dict()				,     _a			)


if __name__ == "__main__":
  a=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained Reformer model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  a=  parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



