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_	(		snake_case__  ,    snake_case__  ,    snake_case__=None				)	->      Tuple:
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
							lowerCAmelCase      						=						nn.Parameter(lowercase_				)
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
														lowerCAmelCase      						=						nn.Parameter(lowercase_				)

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__  ,    snake_case__				)	->      List[str]:
							# set torch weights for 1-to-1 comparison
							lowerCAmelCase      						=						np.asarray(weights[0]				)
							lowerCAmelCase      						=						np.asarray(weights[1]				)
							lowerCAmelCase      						=						np.asarray(weights[2]				)

							set_param(
							    torch_layer.self_attention.query_key  ,    torch.tensor(lowercase_				).transpose(1  ,    2				).contiguous().view(-1  ,    lowercase_				)  ,    )
							set_param(
							    torch_layer.self_attention.value  ,    torch.tensor(lowercase_				).transpose(1  ,    2				).contiguous().view(-1  ,    lowercase_				)  ,    )
							set_param(
							    torch_layer.output.dense  ,    torch.tensor(lowercase_				).view(-1  ,    lowercase_				).contiguous().transpose(0  ,    1				)  ,    )

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__  ,    snake_case__				)	->      List[Any]:
							# set torch weights for 1-to-1 comparison
							lowerCAmelCase      						=						np.asarray(weights[0]				)
							lowerCAmelCase      						=						np.asarray(weights[1]				)
							lowerCAmelCase      						=						np.asarray(weights[2]				)
							lowerCAmelCase      						=						np.asarray(weights[3]				)

							set_param(
							    torch_layer.self_attention.query  ,    torch.tensor(lowercase_				).transpose(1  ,    2				).contiguous().view(-1  ,    lowercase_				)  ,    )
							set_param(
							    torch_layer.self_attention.key  ,    torch.tensor(lowercase_				).transpose(1  ,    2				).contiguous().view(-1  ,    lowercase_				)  ,    )
							set_param(
							    torch_layer.self_attention.value  ,    torch.tensor(lowercase_				).transpose(1  ,    2				).contiguous().view(-1  ,    lowercase_				)  ,    )
							set_param(
							    torch_layer.output.dense  ,    torch.tensor(lowercase_				).view(-1  ,    lowercase_				).contiguous().transpose(0  ,    1				)  ,    )

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__  ,    snake_case__				)	->      Dict:
							# layernorm 1
							lowerCAmelCase      						=						weights[0][0][0]
							lowerCAmelCase      						=						np.asarray(layer_norm_a[0]				)
							lowerCAmelCase      						=						np.asarray(layer_norm_a[1]				)
							set_param(
							    torch_block.attention.layer_norm  ,    torch.tensor(lowercase_				)  ,    torch.tensor(lowercase_				)  ,    )

							# lsh weights + output
							lowerCAmelCase      						=						weights[0][1]
							if len(lowercase_				) < 4:
														set_layer_weights_in_torch_lsh(lowercase_  ,    torch_block.attention  ,    lowercase_				)
							else:
														set_layer_weights_in_torch_local(lowercase_  ,    torch_block.attention  ,    lowercase_				)

							# intermediate weighs
							lowerCAmelCase      						=						weights[2][0][1][2]

							# Chunked Feed Forward
							if len(lowercase_				) == 4:
														lowerCAmelCase      						=						intermediate_weights[2]

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

							# intermediate dense
							lowerCAmelCase      						=						np.asarray(intermediate_weights[1][0]				)
							lowerCAmelCase      						=						np.asarray(intermediate_weights[1][1]				)
							set_param(
							    torch_block.feed_forward.dense.dense  ,    torch.tensor(lowercase_				).transpose(0  ,    1				).contiguous()  ,    torch.tensor(lowercase_				)  ,    )

							# intermediate out
							lowerCAmelCase      						=						np.asarray(intermediate_weights[4][0]				)
							lowerCAmelCase      						=						np.asarray(intermediate_weights[4][1]				)
							set_param(
							    torch_block.feed_forward.output.dense  ,    torch.tensor(lowercase_				).transpose(0  ,    1				).contiguous()  ,    torch.tensor(lowercase_				)  ,    )

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__  ,    snake_case__				)	->      Dict:
							# reformer model
							lowerCAmelCase      						=						torch_model.reformer

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

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

							lowerCAmelCase      						=						weights[5]
							assert len(torch_model_reformer.encoder.layers				) * 4 == len(
							    lowercase_				), "HF and trax model do not have the same number of layers"
							for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers				):
														lowerCAmelCase      						=						trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(lowercase_  ,    lowercase_  ,    lowercase_				)

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

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

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__  ,    snake_case__				)	->      List[Any]:
							# Initialise PyTorch model
							lowerCAmelCase      						=						ReformerConfig.from_json_file(lowercase_				)
							print(f"Building PyTorch model from configuration: {config}"				)
							lowerCAmelCase      						=						ReformerModelWithLMHead(lowercase_				)

							with open(lowercase_  ,    '''rb'''				) as f:
														lowerCAmelCase      						=						pickle.load(lowercase_				)['''weights''']

							set_model_weights_in_torch(lowercase_  ,    lowercase_  ,    config.hidden_size				)

							# Save pytorch-model
							print(f"Save PyTorch model to {pytorch_dump_path}"				)
							torch.save(model.state_dict()  ,    lowercase_				)


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__     :							str            =      parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










import doctest
from collections import deque

import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		[2, 1, 2, -1]
             _lowerCamelCase				:  Optional[int]						=		[1, 2, 3, 4]



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		len(self.first_signal)
             _lowerCamelCase				:  str						=		len(self.second_signal)

             _lowerCamelCase				:  str						=		max(a__   ,				a__)

             # create a zero matrix of max_length x max_length
             _lowerCamelCase				:  Optional[Any]						=		[[0] * max_length for i in range(a__)]

             # fills the smaller signal with zeros to make both signals of same length
             if length_first_signal < length_second_signal:
                    self.first_signal += [0] * (max_length - length_first_signal)
             elif length_first_signal > length_second_signal:
                    self.second_signal += [0] * (max_length - length_second_signal)
             for i in range(a__):
                    _lowerCamelCase				:  Optional[Any]						=		deque(self.second_signal)
                    rotated_signal.rotate(a__)
                    for j, item in enumerate(a__):
                           matrix[i][j] += item

        # multiply the matrix with the first signal
             _lowerCamelCase				:  List[Any]						=		np.matmul(np.transpose(a__)   ,				np.transpose(self.first_signal))

             # rounding-off to two decimal places
             return [round(a__   ,				2) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()

