





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__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				List[str]=None     ):
		assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
		SCREAMING_SNAKE_CASE__								=   nn.Parameter(__a     )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
				SCREAMING_SNAKE_CASE__								=   nn.Parameter(__a     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Optional[int]     ):
		SCREAMING_SNAKE_CASE__								=   np.asarray(weights[0]     )
		SCREAMING_SNAKE_CASE__								=   np.asarray(weights[1]     )
		SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Any       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   np.asarray(weights[0]     )
		SCREAMING_SNAKE_CASE__								=   np.asarray(weights[1]     )
		SCREAMING_SNAKE_CASE__								=   np.asarray(weights[2]     )
		SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   weights[0][0][0]
		SCREAMING_SNAKE_CASE__								=   np.asarray(layer_norm_a[0]     )
		SCREAMING_SNAKE_CASE__								=   np.asarray(layer_norm_a[1]     )
		set_param(
		    torch_block.attention.layer_norm       , torch.tensor(__a     )       , torch.tensor(__a     )       , )

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

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

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

		# intermediate dense
		SCREAMING_SNAKE_CASE__								=   np.asarray(intermediate_weights[1][0]     )
		SCREAMING_SNAKE_CASE__								=   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
		SCREAMING_SNAKE_CASE__								=   np.asarray(intermediate_weights[4][0]     )
		SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[Any]     ):
		SCREAMING_SNAKE_CASE__								=   torch_model.reformer

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

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

		SCREAMING_SNAKE_CASE__								=   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     ):
				SCREAMING_SNAKE_CASE__								=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(__a       , __a       , __a     )

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

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






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

		with open(__a       , """rb"""     ) as f:
				SCREAMING_SNAKE_CASE__								=   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__":
	_lowerCamelCase 					=      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 					=      parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""







def     UpperCAmelCase_					(				__a   :			float      ,							__a   :			float						):




			'''simple docstring'''



			if density <= 0:
						raise ValueError('Impossible fluid density'						)
			if bulk_modulus <= 0:
						raise ValueError('Impossible bulk modulus'						)

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
	import doctest

	doctest.testmod()





