






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    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None	)->  Optional[Any]:





						"""simple docstring"""



						assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
						_UpperCAmelCase									=			nn.Parameter(_UpperCAmelCase	)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
												_UpperCAmelCase									=			nn.Parameter(_UpperCAmelCase	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						_UpperCAmelCase									=			np.asarray(weights[0]	)
						_UpperCAmelCase									=			np.asarray(weights[1]	)
						_UpperCAmelCase									=			np.asarray(weights[2]	)

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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			np.asarray(weights[0]	)
						_UpperCAmelCase									=			np.asarray(weights[1]	)
						_UpperCAmelCase									=			np.asarray(weights[2]	)
						_UpperCAmelCase									=			np.asarray(weights[3]	)

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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			weights[0][0][0]
						_UpperCAmelCase									=			np.asarray(layer_norm_a[0]	)
						_UpperCAmelCase									=			np.asarray(layer_norm_a[1]	)
						set_param(
						    torch_block.attention.layer_norm ,		torch.tensor(_UpperCAmelCase	) ,		torch.tensor(_UpperCAmelCase	) ,		)

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

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

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

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

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

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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			torch_model.reformer

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

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

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

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

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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			ReformerConfig.from_json_file(_UpperCAmelCase	)
						print(F"""Building PyTorch model from configuration: {config}"""	)
						_UpperCAmelCase									=			ReformerModelWithLMHead(_UpperCAmelCase	)

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

						set_model_weights_in_torch(_UpperCAmelCase ,		_UpperCAmelCase ,		config.hidden_size	)

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


if __name__ == "__main__":
		_a      	=  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.'''
		)
		_a      	=  parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				pruning_method
           __a						       =				mask_init
           __a						       =				mask_scale





