



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(snake_case_    )
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
								__a							=    nn.Parameter(snake_case_    )





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

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

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

				# Chunked Feed Forward
				if len(snake_case_    ) == 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(snake_case_    )       ,			torch.tensor(snake_case_    )       ,			)

				# 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(snake_case_    ).transpose(0       ,			1    ).contiguous()       ,			torch.tensor(snake_case_    )       ,			)

				# 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(snake_case_    ).transpose(0       ,			1    ).contiguous()       ,			torch.tensor(snake_case_    )       ,			)





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(snake_case_    )       ,			)

				if isinstance(weights[3]       ,			snake_case_    ):
								__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(snake_case_    )    )

				__a							=    weights[5]
				assert len(torch_model_reformer.encoder.layers    ) * 4 == len(
				    snake_case_    ), "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(snake_case_       ,			snake_case_       ,			snake_case_    )

				# 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(snake_case_    )       ,			torch.tensor(snake_case_    )       ,			)

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





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

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

				set_model_weights_in_torch(snake_case_       ,			snake_case_       ,			config.hidden_size    )

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


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










import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case						:Optional[Any]						=					logging.get_logger(__name__)


__snake_case						:List[Any]						=					'''▁'''

__snake_case						:List[Any]						=					{'''vocab_file''': '''spiece.model'''}

__snake_case						:Tuple						=					{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__snake_case						:List[Any]						=					{
    '''google/reformer-crime-and-punishment''': 52_4288,
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   int						 =						VOCAB_FILES_NAMES
       UpperCamelCase__  :   str						 =						PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__  :   Dict						 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__  :   Optional[int]						 =						['''input_ids''', '''attention_mask''']



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Any=[]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					List[Any]					,		):


           '''simple docstring'''

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE					,		unk_token=__SCREAMING_SNAKE_CASE					,		additional_special_tokens=__SCREAMING_SNAKE_CASE					,		sp_model_kwargs=self.sp_model_kwargs					,		**__SCREAMING_SNAKE_CASE					,		)

           __a							=    vocab_file
           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return self.sp_model.get_piece_size()



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab



       def __getstate__(		self							:					Dict):


           '''simple docstring'''

           __a							=    self.__dict__.copy()
           __a							=    None
           return state



       def __setstate__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    d

           # for backward compatibility
           if not hasattr(self					,		'''sp_model_kwargs'''):
               __a							=    {}

           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           return self.sp_model.encode(__SCREAMING_SNAKE_CASE					,		out_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
               __a							=    self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
           return token



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    []
           __a							=    ''''''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE) + token
                   __a							=    []
               else:
                   current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
           return out_string.strip()





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a							=    os.path.join(
               __SCREAMING_SNAKE_CASE					,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

           if os.path.abspath(self.vocab_file) != os.path.abspath(__SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file					,		__SCREAMING_SNAKE_CASE)
           elif not os.path.isfile(self.vocab_file):
               with open(__SCREAMING_SNAKE_CASE					,		'''wb''') as fi:
                   __a							=    self.sp_model.serialized_model_proto()
                   fi.write(__SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)





