



"""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        lowerCAmelCase_(   lowercase_     :							List[str]						,   lowercase_     :							Any						,   lowercase_     :							Optional[Any]=None							)	->       Optional[Any]:
				# set parameter of one layer
				assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
				_lowerCamelCase        =				nn.Parameter(lowercase_							)
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
								_lowerCamelCase        =				nn.Parameter(lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							Optional[int]						,   lowercase_     :							str							)	->       Any:
				# set torch weights for 1-to-1 comparison
				_lowerCamelCase        =				np.asarray(weights[0]							)
				_lowerCamelCase        =				np.asarray(weights[1]							)
				_lowerCamelCase        =				np.asarray(weights[2]							)

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



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							Union[str, Any]						,   lowercase_     :							Dict							)	->       Optional[int]:
				# set torch weights for 1-to-1 comparison
				_lowerCamelCase        =				np.asarray(weights[0]							)
				_lowerCamelCase        =				np.asarray(weights[1]							)
				_lowerCamelCase        =				np.asarray(weights[2]							)
				_lowerCamelCase        =				np.asarray(weights[3]							)

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



def        lowerCAmelCase_(   lowercase_     :							Union[str, Any]						,   lowercase_     :							Tuple						,   lowercase_     :							int							)	->       int:
				# layernorm 1
				_lowerCamelCase        =				weights[0][0][0]
				_lowerCamelCase        =				np.asarray(layer_norm_a[0]							)
				_lowerCamelCase        =				np.asarray(layer_norm_a[1]							)
				set_param(
				    torch_block.attention.layer_norm						,   torch.tensor(lowercase_							)						,   torch.tensor(lowercase_							)						,   )

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

				# intermediate weighs
				_lowerCamelCase        =				weights[2][0][1][2]

				# Chunked Feed Forward
				if len(lowercase_							) == 4:
								_lowerCamelCase        =				intermediate_weights[2]

				# layernorm 2
				_lowerCamelCase        =				np.asarray(intermediate_weights[0][0]							)
				_lowerCamelCase        =				np.asarray(intermediate_weights[0][1]							)
				set_param(
				    torch_block.feed_forward.layer_norm						,   torch.tensor(lowercase_							)						,   torch.tensor(lowercase_							)						,   )

				# intermediate dense
				_lowerCamelCase        =				np.asarray(intermediate_weights[1][0]							)
				_lowerCamelCase        =				np.asarray(intermediate_weights[1][1]							)
				set_param(
				    torch_block.feed_forward.dense.dense						,   torch.tensor(lowercase_							).transpose(0						,   1							).contiguous()						,   torch.tensor(lowercase_							)						,   )

				# intermediate out
				_lowerCamelCase        =				np.asarray(intermediate_weights[4][0]							)
				_lowerCamelCase        =				np.asarray(intermediate_weights[4][1]							)
				set_param(
				    torch_block.feed_forward.output.dense						,   torch.tensor(lowercase_							).transpose(0						,   1							).contiguous()						,   torch.tensor(lowercase_							)						,   )



def        lowerCAmelCase_(   lowercase_     :							Union[str, Any]						,   lowercase_     :							Tuple						,   lowercase_     :							Any							)	->       str:
				# reformer model
				_lowerCamelCase        =				torch_model.reformer

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

				if isinstance(weights[3]						,   lowercase_							):
								_lowerCamelCase        =				torch_model_reformer.embeddings.position_embeddings
								for emb_idx in range(len(position_embeddings.weights							)							):
												_lowerCamelCase        =				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"""
												_lowerCamelCase        =				nn.Parameter(torch.tensor(lowercase_							)							)

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

				# output layer norm
				_lowerCamelCase        =				np.asarray(weights[7][0]							)
				_lowerCamelCase        =				np.asarray(weights[7][1]							)
				set_param(
				    torch_model_reformer.encoder.layer_norm						,   torch.tensor(lowercase_							)						,   torch.tensor(lowercase_							)						,   )

				# output embeddings
				_lowerCamelCase        =				np.asarray(weights[9][0]							)
				_lowerCamelCase        =				np.asarray(weights[9][1]							)
				set_param(
				    torch_model.lm_head.decoder						,   torch.tensor(lowercase_							).transpose(0						,   1							).contiguous()						,   torch.tensor(lowercase_							)						,   )



def        lowerCAmelCase_(   lowercase_     :							Union[str, Any]						,   lowercase_     :							Dict						,   lowercase_     :							Union[str, Any]							)	->       Optional[Any]:
				# Initialise PyTorch model
				_lowerCamelCase        =				ReformerConfig.from_json_file(lowercase_							)
				print(F"""Building PyTorch model from configuration: {config}"""							)
				_lowerCamelCase        =				ReformerModelWithLMHead(lowercase_							)

				with open(lowercase_						,   '''rb'''							) as f:
								_lowerCamelCase        =				pickle.load(lowercase_							)['''weights''']

				set_model_weights_in_torch(lowercase_						,   lowercase_						,   config.hidden_size							)

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


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





"""simple docstring"""
from typing import Any


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				data
									_lowerCamelCase        =				None


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self   ):
									_lowerCamelCase        =				None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.head
									while temp is not None:
													print(temp.data				,       end=''' '''   )
													_lowerCamelCase        =				temp.next
									print()

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				Node(lowerCamelCase__   )
									_lowerCamelCase        =				self.head
									_lowerCamelCase        =				new_node





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									if node_data_a == node_data_a:
													return
									else:
													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													if node_a is None or node_a is None:
																	return

													_lowerCamelCase  ,     _lowerCamelCase        =				node_a.data, node_a.data


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      LinkedList()
			for i in range(5, 0, -1):
						ll.push(i)

			ll.print_list()

			ll.swap_nodes(1, 4)
			print('''After swapping''')
			ll.print_list()
