





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 _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]=None       )	->    List[Any]:

							'''simple docstring'''






							assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
							SCREAMING_SNAKE_CASE__       :      str     =						nn.Parameter(SCREAMING_SNAKE_CASE__       )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
														SCREAMING_SNAKE_CASE__       :      Any     =						nn.Parameter(SCREAMING_SNAKE_CASE__       )




def _a (  SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    Dict:

							'''simple docstring'''






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

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




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    int:

							'''simple docstring'''






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

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




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  str       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						weights[0][0][0]
							SCREAMING_SNAKE_CASE__       :      Dict     =						np.asarray(layer_norm_a[0]       )
							SCREAMING_SNAKE_CASE__       :      int     =						np.asarray(layer_norm_a[1]       )
							set_param(
							    torch_block.attention.layer_norm			,				torch.tensor(SCREAMING_SNAKE_CASE__       )			,				torch.tensor(SCREAMING_SNAKE_CASE__       )			,				)

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

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

							# Chunked Feed Forward
							if len(SCREAMING_SNAKE_CASE__       ) == 4:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						intermediate_weights[2]

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

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

							# intermediate out
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						np.asarray(intermediate_weights[4][0]       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						np.asarray(intermediate_weights[4][1]       )
							set_param(
							    torch_block.feed_forward.output.dense			,				torch.tensor(SCREAMING_SNAKE_CASE__       ).transpose(0			,				1       ).contiguous()			,				torch.tensor(SCREAMING_SNAKE_CASE__       )			,				)




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[int]			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  List[str]       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						torch_model.reformer

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

							if isinstance(weights[3]			,				SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      str     =						torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights       )       ):
																					SCREAMING_SNAKE_CASE__       :      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'''
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__       )       )

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

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

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




def _a (  SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  List[Any]       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
							print(f'''Building PyTorch model from configuration: {config}'''       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						ReformerModelWithLMHead(SCREAMING_SNAKE_CASE__       )

							with open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pickle.load(SCREAMING_SNAKE_CASE__       )["weights"]

							set_model_weights_in_torch(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				config.hidden_size       )

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


if __name__ == "__main__":
		_lowerCamelCase :    int  							= 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.'''
		)
		_lowerCamelCase :    Optional[int]  							= parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)













class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						name
														SCREAMING_SNAKE_CASE__       :      Tuple     =						val






							def __str__( self     :					str						)				->			List[Any]:





														"""simple docstring"""
														return F'''{self.__class__.__name__}({self.name}, {self.val})'''






							def __lt__( self     :					str,			_UpperCAmelCase     :					Dict						)				->			Union[str, Any]:





														"""simple docstring"""
														return self.val < other.val


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					str						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{}
														SCREAMING_SNAKE_CASE__       :      str     =						{}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.build_heap(_UpperCAmelCase						)






							def __getitem__( self     :					Union[str, Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return self.get_value(_UpperCAmelCase						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[int]						)				->			Any:





														"""simple docstring"""
														return (idx - 1) // 2






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return idx * 2 + 1






							def  A_     ( self     :					str,			_UpperCAmelCase     :					List[str]						)				->			Union[str, Any]:





														"""simple docstring"""
														return idx * 2 + 2






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					int						)				->			Optional[int]:





														"""simple docstring"""
														return self.heap_dict[key]






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Dict						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						len(_UpperCAmelCase						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_parent_idx(_UpperCAmelCase						)

														for idx, i in enumerate(_UpperCAmelCase						):
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						idx
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						i.val

														for i in range(_UpperCAmelCase,			-1,			-1						):
																					self.sift_down(_UpperCAmelCase,			_UpperCAmelCase						)
														return array






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Tuple						)				->			Tuple:





														"""simple docstring"""
														while True:
																					SCREAMING_SNAKE_CASE__       :      str     =						self.get_left_child_idx(_UpperCAmelCase						)  # noqa: E741
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_right_child_idx(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						idx
																					if l < len(_UpperCAmelCase						) and array[l] < array[idx]:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						l
																					if r < len(_UpperCAmelCase						) and array[r] < array[smallest]:
																												SCREAMING_SNAKE_CASE__       :      int     =						r

																					if smallest != idx:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						array[smallest], array[idx]
																												(
																												    (
																												    SCREAMING_SNAKE_CASE__
																												)   ,(
																												    SCREAMING_SNAKE_CASE__
																												)   ,
																												)       :      Optional[Any]     =						(
																												    self.idx_of_element[array[smallest]],
																												    self.idx_of_element[array[idx]],
																												)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						smallest
																					else:
																												break






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self.get_parent_idx(_UpperCAmelCase						)
														while p >= 0 and self.heap[p] > self.heap[idx]:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						self.heap[idx], self.heap[p]
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						(
																					    self.idx_of_element[self.heap[idx]],
																					    self.idx_of_element[self.heap[p]],
																					)
																					SCREAMING_SNAKE_CASE__       :      Dict     =						p
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_parent_idx(_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.heap[0]






							def  A_     ( self     :					Tuple						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.heap[-1], self.heap[0]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						(
														    self.idx_of_element[self.heap[-1]],
														    self.idx_of_element[self.heap[0]],
														)

														SCREAMING_SNAKE_CASE__       :      Any     =						self.heap.pop()
														del self.idx_of_element[x]
														self.sift_down(0,			self.heap						)
														return x






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														self.heap.append(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						len(self.heap						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node.val
														self.sift_up(len(self.heap						) - 1						)






							def  A_     ( self     :					Optional[int]						)				->			int:





														"""simple docstring"""
														return len(self.heap						) == 0






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					str						)				->			Dict:





														"""simple docstring"""
														assert (
														    self.heap[self.idx_of_element[node]].val > new_value
														), "newValue must be less that current value"
														SCREAMING_SNAKE_CASE__       :      Tuple     =						new_value
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_value
														self.sift_up(self.idx_of_element[node]						)


_lowerCamelCase :    Tuple  							= Node('''R''', -1)
_lowerCamelCase :    int  							= Node('''B''', 6)
_lowerCamelCase :    str  							= Node('''A''', 3)
_lowerCamelCase :    Optional[Any]  							= Node('''X''', 1)
_lowerCamelCase :    str  							= Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCamelCase :    int  							= MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
		print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()






