






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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# set torch weights for 1-to-1 comparison
					a__          =  np.asarray(weights[0])
					a__          =  np.asarray(weights[1])
					a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# set torch weights for 1-to-1 comparison
					a__          =  np.asarray(weights[0])
					a__          =  np.asarray(weights[1])
					a__          =  np.asarray(weights[2])
					a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# layernorm 1
					a__          =  weights[0][0][0]
					a__          =  np.asarray(layer_norm_a[0])
					a__          =  np.asarray(layer_norm_a[1])
					set_param(
					    torch_block.attention.layer_norm ,     torch.tensor(lowerCamelCase_) ,     torch.tensor(lowerCamelCase_) ,     )

					# lsh weights + output
					a__          =  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
					a__          =  weights[2][0][1][2]

					# Chunked Feed Forward
					if len(lowerCamelCase_) == 4:
										a__          =  intermediate_weights[2]

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

					# intermediate dense
					a__          =  np.asarray(intermediate_weights[1][0])
					a__          =  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
					a__          =  np.asarray(intermediate_weights[4][0])
					a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# reformer model
					a__          =  torch_model.reformer

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

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

					a__          =  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):
										a__          =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
										set_block_weights_in_torch(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

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

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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# Initialise PyTorch model
					a__          =  ReformerConfig.from_json_file(lowerCamelCase_)
					print(f'Building PyTorch model from configuration: {config}')
					a__          =  ReformerModelWithLMHead(lowerCamelCase_)

					with open(lowerCamelCase_ ,     '''rb''') as f:
										a__          =  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__":
	__a       :	Dict             = 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       :	Optional[Any]             = parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""







def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					assert column_title.isupper()
					a__          =  0
					a__          =  len(lowerCamelCase_) - 1
					a__          =  0

					while index >= 0:
										a__          =  (ord(column_title[index]) - 64) * pow(26 ,     lowerCamelCase_)
										answer += value
										power += 1
										index -= 1

					return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()


