



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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=None       ):
				# set parameter of one layer
				assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
				__a		 =       nn.Parameter(__lowerCAmelCase       )
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
								__a		 =       nn.Parameter(__lowerCAmelCase       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
				# set torch weights for 1-to-1 comparison
				__a		 =       np.asarray(weights[0]       )
				__a		 =       np.asarray(weights[1]       )
				__a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
				# set torch weights for 1-to-1 comparison
				__a		 =       np.asarray(weights[0]       )
				__a		 =       np.asarray(weights[1]       )
				__a		 =       np.asarray(weights[2]       )
				__a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
				# layernorm 1
				__a		 =       weights[0][0][0]
				__a		 =       np.asarray(layer_norm_a[0]       )
				__a		 =       np.asarray(layer_norm_a[1]       )
				set_param(
				    torch_block.attention.layer_norm     ,				torch.tensor(__lowerCAmelCase       )     ,				torch.tensor(__lowerCAmelCase       )     ,				)

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

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

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

				# intermediate dense
				__a		 =       np.asarray(intermediate_weights[1][0]       )
				__a		 =       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
				__a		 =       np.asarray(intermediate_weights[4][0]       )
				__a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
				# reformer model
				__a		 =       torch_model.reformer

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

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

				__a		 =       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       ):
								__a		 =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
								set_block_weights_in_torch(__lowerCAmelCase     ,				__lowerCAmelCase     ,				__lowerCAmelCase       )

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

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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
				# Initialise PyTorch model
				__a		 =       ReformerConfig.from_json_file(__lowerCAmelCase       )
				print(f'Building PyTorch model from configuration: {config}'       )
				__a		 =       ReformerModelWithLMHead(__lowerCAmelCase       )

				with open(__lowerCAmelCase     ,				'''rb'''       ) as f:
								__a		 =       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__":
	__snake_case		:Union[str, 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.'''
	)
	__snake_case		:Optional[int]   	=  parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case		:Tuple   	=  logging.getLogger(__name__)

if __name__ == "__main__":
 __snake_case		:Union[str, Any]   	=  argparse.ArgumentParser(
     description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
 )
 parser.add_argument(
     '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
 )
 parser.add_argument(
     '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
 )
 parser.add_argument('''--vocab_size''', default=3_0522, type=int)
 __snake_case		:List[str]   	=  parser.parse_args()

 logger.info(f'Loading data from {args.data_file}')
 with open(args.data_file, '''rb''') as fp:
  __snake_case		:Optional[Any]   	=  pickle.load(fp)

 logger.info('''Counting occurrences for MLM.''')
 __snake_case		:Dict   	=  Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 __snake_case		:Optional[Any]   	=  [0] * args.vocab_size
 for k, v in counter.items():
  __snake_case		:Any   	=  v

 logger.info(f'Dump to {args.token_counts_dump}')
 with open(args.token_counts_dump, '''wb''') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





