

"""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    UpperCamelCase       (      _lowerCAmelCase  :    List[str],	_lowerCAmelCase  :    Any,	_lowerCAmelCase  :    Tuple=None			)					->      Dict:
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	_UpperCAmelCase     :				int          =       nn.Parameter(__a			)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		_UpperCAmelCase     :				Optional[Any]          =       nn.Parameter(__a			)
def    UpperCamelCase       (      _lowerCAmelCase  :    List[Any],	_lowerCAmelCase  :    Tuple,	_lowerCAmelCase  :    int			)					->      Dict:
	_UpperCAmelCase     :				int          =       np.asarray(weights[0]			)
	_UpperCAmelCase     :				int          =       np.asarray(weights[1]			)
	_UpperCAmelCase     :				List[Any]          =       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    UpperCamelCase       (      _lowerCAmelCase  :    List[Any],	_lowerCAmelCase  :    Optional[Any],	_lowerCAmelCase  :    List[Any]			)					->      Union[str, Any]:
	_UpperCAmelCase     :				int          =       np.asarray(weights[0]			)
	_UpperCAmelCase     :				Tuple          =       np.asarray(weights[1]			)
	_UpperCAmelCase     :				Optional[int]          =       np.asarray(weights[2]			)
	_UpperCAmelCase     :				Tuple          =       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    UpperCamelCase       (      _lowerCAmelCase  :    str,	_lowerCAmelCase  :    Dict,	_lowerCAmelCase  :    Union[str, Any]			)					->      Optional[int]:
	_UpperCAmelCase     :				Union[str, Any]          =       weights[0][0][0]
	_UpperCAmelCase     :				List[Any]          =       np.asarray(layer_norm_a[0]			)
	_UpperCAmelCase     :				Optional[int]          =       np.asarray(layer_norm_a[1]			)
	set_param(
	    torch_block.attention.layer_norm,	torch.tensor(__a			),	torch.tensor(__a			),	)

	# lsh weights + output
	_UpperCAmelCase     :				Tuple          =       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     :				List[Any]          =       weights[2][0][1][2]

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

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

	# intermediate dense
	_UpperCAmelCase     :				Tuple          =       np.asarray(intermediate_weights[1][0]			)
	_UpperCAmelCase     :				str          =       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     :				List[str]          =       np.asarray(intermediate_weights[4][0]			)
	_UpperCAmelCase     :				Any          =       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    UpperCamelCase       (      _lowerCAmelCase  :    List[str],	_lowerCAmelCase  :    Union[str, Any],	_lowerCAmelCase  :    Union[str, Any]			)					->      Optional[Any]:
	_UpperCAmelCase     :				Union[str, Any]          =       torch_model.reformer

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

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

	_UpperCAmelCase     :				Tuple          =       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[Any]          =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(__a,	__a,	__a			)

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

	# output embeddings
	_UpperCAmelCase     :				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    UpperCamelCase       (      _lowerCAmelCase  :    Any,	_lowerCAmelCase  :    Optional[Any],	_lowerCAmelCase  :    Union[str, Any]			)					->      Optional[int]:
	_UpperCAmelCase     :				Optional[int]          =       ReformerConfig.from_json_file(__a			)
	print(f'''Building PyTorch model from configuration: {config}'''			)
	_UpperCAmelCase     :				List[str]          =       ReformerModelWithLMHead(__a			)

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












import numpy as np







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

		return 1 / (1 + np.exp(-vector       ))







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

		return vector * sigmoid(1.702 * vector       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



