from __future__ import annotations






def 							__UpperCAmelCase     (							UpperCAmelCase							)->							Dict:



			"""simple docstring"""







			lowercase       		=		2
			lowercase       		=		[]
			while i * i <= n:
						if n % i:
									i += 1
						else:
									n //= i
									factors.append(_lowerCAmelCase							)
			if n > 1:
						factors.append(_lowerCAmelCase							)
			return factors


if __name__ == "__main__":
						import doctest

						doctest.testmod()







"""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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# 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(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# 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(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# 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(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

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

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

						# Chunked Feed Forward
						if len(_lowerCAmelCase						) == 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(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# 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(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

						# 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(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

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

						if isinstance(weights[3] ,    _lowerCAmelCase						):
												__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(_lowerCAmelCase						)						)

						__lowerCAmelCase     =  weights[5]
						assert len(torch_model_reformer.encoder.layers						) * 4 == len(
						    _lowerCAmelCase						), "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(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# 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(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  pickle.load(_lowerCAmelCase						)["""weights"""]

						set_model_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    config.hidden_size						)

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	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_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
