
'''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__			(     __A    ,       __A    ,       __A=None			) ->			List[Any]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
	_snake_case     		=	nn.Parameter(lowerCamelCase__			)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
		_snake_case     		=	nn.Parameter(lowerCamelCase__			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			List[str]:
	# set torch weights for 1-to-1 comparison
	_snake_case     		=	np.asarray(weights[0]			)
	_snake_case     		=	np.asarray(weights[1]			)
	_snake_case     		=	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__			(     __A    ,       __A    ,       __A			) ->			Optional[Any]:
	# set torch weights for 1-to-1 comparison
	_snake_case     		=	np.asarray(weights[0]			)
	_snake_case     		=	np.asarray(weights[1]			)
	_snake_case     		=	np.asarray(weights[2]			)
	_snake_case     		=	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__			(     __A    ,       __A    ,       __A			) ->			Optional[Any]:
	# layernorm 1
	_snake_case     		=	weights[0][0][0]
	_snake_case     		=	np.asarray(layer_norm_a[0]			)
	_snake_case     		=	np.asarray(layer_norm_a[1]			)
	set_param(
	    torch_block.attention.layer_norm    ,       torch.tensor(lowerCamelCase__			)    ,       torch.tensor(lowerCamelCase__			)    ,       )

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

	# Chunked Feed Forward
	if len(lowerCamelCase__			) == 4:
		_snake_case     		=	intermediate_weights[2]

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

	# intermediate dense
	_snake_case     		=	np.asarray(intermediate_weights[1][0]			)
	_snake_case     		=	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
	_snake_case     		=	np.asarray(intermediate_weights[4][0]			)
	_snake_case     		=	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__			(     __A    ,       __A    ,       __A			) ->			Optional[int]:
	# reformer model
	_snake_case     		=	torch_model.reformer

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

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

	_snake_case     		=	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			):
		_snake_case     		=	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__			)

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

	# output embeddings
	_snake_case     		=	np.asarray(weights[9][0]			)
	_snake_case     		=	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__			(     __A    ,       __A    ,       __A			) ->			Tuple:
	# Initialise PyTorch model
	_snake_case     		=	ReformerConfig.from_json_file(lowerCamelCase__			)
	print(F'Building PyTorch model from configuration: {config}'			)
	_snake_case     		=	ReformerModelWithLMHead(lowerCamelCase__			)

	with open(lowerCamelCase__    ,       'rb'			) as f:
		_snake_case     		=	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__":
					lowercase					:							str   =      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."
					)
					lowercase					:							int   =      parser.parse_args()
					convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''







import math







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * power_factor







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * math.sqrt(1 - power_factor**2			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

