


'''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     a   (    __a				,      __a				,      __a=None     )						->			List[Any]:



					'''simple docstring'''






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


def     a   (    __a				,      __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				np.asarray(weights[0]     )
					UpperCamelCase__     :Dict							      =				np.asarray(weights[1]     )
					UpperCamelCase__     :Optional[int]							      =				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     a   (    __a				,      __a				,      __a     )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				np.asarray(weights[0]     )
					UpperCamelCase__     :List[Any]							      =				np.asarray(weights[1]     )
					UpperCamelCase__     :str							      =				np.asarray(weights[2]     )
					UpperCamelCase__     :Optional[int]							      =				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     a   (    __a				,      __a				,      __a     )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				weights[0][0][0]
					UpperCamelCase__     :str							      =				np.asarray(layer_norm_a[0]     )
					UpperCamelCase__     :Any							      =				np.asarray(layer_norm_a[1]     )
					set_param(
					    torch_block.attention.layer_norm				,      torch.tensor(_A     )				,      torch.tensor(_A     )				,      )

					# lsh weights + output
					UpperCamelCase__     :Union[str, Any]							      =				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
					UpperCamelCase__     :Dict							      =				weights[2][0][1][2]

					# Chunked Feed Forward
					if len(_A     ) == 4:
										UpperCamelCase__     :Optional[int]							      =				intermediate_weights[2]

					# layernorm 2
					UpperCamelCase__     :List[str]							      =				np.asarray(intermediate_weights[0][0]     )
					UpperCamelCase__     :Union[str, Any]							      =				np.asarray(intermediate_weights[0][1]     )
					set_param(
					    torch_block.feed_forward.layer_norm				,      torch.tensor(_A     )				,      torch.tensor(_A     )				,      )

					# intermediate dense
					UpperCamelCase__     :Optional[Any]							      =				np.asarray(intermediate_weights[1][0]     )
					UpperCamelCase__     :Optional[Any]							      =				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
					UpperCamelCase__     :str							      =				np.asarray(intermediate_weights[4][0]     )
					UpperCamelCase__     :Tuple							      =				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     a   (    __a				,      __a				,      __a     )						->			List[Any]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[int]							      =				torch_model.reformer

					# word embeds
					UpperCamelCase__     :List[Any]							      =				np.asarray(weights[1]     )
					set_param(
					    torch_model_reformer.embeddings.word_embeddings				,      torch.tensor(_A     )				,      )

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

					UpperCamelCase__     :Optional[int]							      =				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     ):
										UpperCamelCase__     :Optional[int]							      =				trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
										set_block_weights_in_torch(_A				,      _A				,      _A     )

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

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


def     a   (    __a				,      __a				,      __a     )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				ReformerConfig.from_json_file(_A     )
					print(f'''Building PyTorch model from configuration: {config}'''     )
					UpperCamelCase__     :List[Any]							      =				ReformerModelWithLMHead(_A     )

					with open(_A				,      '''rb'''     ) as f:
										UpperCamelCase__     :Tuple							      =				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__":
						__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.'''
						)
						__snake_case							   =  parser.parse_args()
						convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''
import numpy as np


def     a   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				int(np.ceil((x_end - xa) / h     )     )
					UpperCamelCase__     :Optional[int]							      =				np.zeros((n + 1,)     )
					UpperCamelCase__     :List[str]							      =				ya
					UpperCamelCase__     :Tuple							      =				xa

					for k in range(__a     ):
										UpperCamelCase__     :Dict							      =				f(__a				,      y[k]     )
										UpperCamelCase__     :List[Any]							      =				f(x + 0.5 * h				,      y[k] + 0.5 * h * ka     )
										UpperCamelCase__     :Dict							      =				f(x + 0.5 * h				,      y[k] + 0.5 * h * ka     )
										UpperCamelCase__     :List[Any]							      =				f(x + h				,      y[k] + h * ka     )
										UpperCamelCase__     :List[str]							      =				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
										x += h

					return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()