'''simple docstring'''






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

__a										=					logging.get_logger(__name__)


__a										=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a										=					{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

__a										=					{'''facebook/blenderbot-3B''': 128}





class     A__							(       UpperCamelCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Any    				=     VOCAB_FILES_NAMES
   UpperCamelCase_       :						int    				=     PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase_       :						Dict    				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase_       :						str    				=     ["input_ids", "attention_mask"]
   UpperCamelCase_       :						Dict    				=     BlenderbotTokenizer




   def __init__( self     :		List[Any]						,     lowerCAmelCase__     :		int=None						,     lowerCAmelCase__     :		Any=None						,     lowerCAmelCase__     :		Tuple=None						,     lowerCAmelCase__     :		Dict="replace"						,     lowerCAmelCase__     :		Optional[int]="<s>"						,     lowerCAmelCase__     :		Optional[Any]="</s>"						,     lowerCAmelCase__     :		Any="</s>"						,     lowerCAmelCase__     :		List[Any]="<s>"						,     lowerCAmelCase__     :		List[Any]="<unk>"						,     lowerCAmelCase__     :		Any="<pad>"						,     lowerCAmelCase__     :		List[str]="<mask>"						,     lowerCAmelCase__     :		List[str]=False						,     lowerCAmelCase__     :		Optional[int]=True						,     **lowerCAmelCase__     :		Optional[Any]						,     )       ->    Optional[int]:







      """simple docstring"""




      super().__init__(
          lowerCAmelCase__						,     lowerCAmelCase__						,     tokenizer_file=lowerCAmelCase__						,     errors=lowerCAmelCase__						,     bos_token=lowerCAmelCase__						,     eos_token=lowerCAmelCase__						,     sep_token=lowerCAmelCase__						,     cls_token=lowerCAmelCase__						,     unk_token=lowerCAmelCase__						,     pad_token=lowerCAmelCase__						,     mask_token=lowerCAmelCase__						,     add_prefix_space=lowerCAmelCase__						,     trim_offsets=lowerCAmelCase__						,     **lowerCAmelCase__						,     )

      _UpperCAmelCase							:	Any							=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
      if pre_tok_state.get("add_prefix_space"						,     lowerCAmelCase__			) != add_prefix_space:
         _UpperCAmelCase							:	Optional[Any]							=   getattr(lowerCAmelCase__						,     pre_tok_state.pop("type"			)			)
         _UpperCAmelCase							:	Optional[int]							=   add_prefix_space
         _UpperCAmelCase							:	Any							=   pre_tok_class(**lowerCAmelCase__			)

      _UpperCAmelCase							:	Dict							=   add_prefix_space

      _UpperCAmelCase							:	List[str]							=   "post_processor"
      _UpperCAmelCase							:	str							=   getattr(self.backend_tokenizer						,     lowerCAmelCase__						,     lowerCAmelCase__			)
      if tokenizer_component_instance:
         _UpperCAmelCase							:	Optional[Any]							=   json.loads(tokenizer_component_instance.__getstate__()			)

         # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
         if "sep" in state:
            _UpperCAmelCase							:	Optional[Any]							=   tuple(state["sep"]			)
         if "cls" in state:
            _UpperCAmelCase							:	Optional[Any]							=   tuple(state["cls"]			)

         _UpperCAmelCase							:	Optional[Any]							=   False

         if state.get("add_prefix_space"						,     lowerCAmelCase__			) != add_prefix_space:
            _UpperCAmelCase							:	int							=   add_prefix_space
            _UpperCAmelCase							:	Optional[int]							=   True

         if state.get("trim_offsets"						,     lowerCAmelCase__			) != trim_offsets:
            _UpperCAmelCase							:	Optional[int]							=   trim_offsets
            _UpperCAmelCase							:	int							=   True

         if changes_to_apply:
            _UpperCAmelCase							:	Optional[Any]							=   getattr(lowerCAmelCase__						,     state.pop("type"			)			)
            _UpperCAmelCase							:	str							=   component_class(**lowerCAmelCase__			)
            setattr(self.backend_tokenizer						,     lowerCAmelCase__						,     lowerCAmelCase__			)




   @property
   # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
   def 			_lowerCAmelCase		( self     :		Any			)       ->    List[Any]:







      """simple docstring"""




      if self._mask_token is None:
         if self.verbose:
            logger.error("Using mask_token, but it is not set yet."			)
         return None
      return str(self._mask_token			)




   @mask_token.setter
   def 			_lowerCAmelCase		( self     :		Optional[Any]						,     lowerCAmelCase__     :		int			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   AddedToken(lowerCAmelCase__						,     lstrip=lowerCAmelCase__						,     rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__						,     lowerCAmelCase__			) else value
      _UpperCAmelCase							:	Optional[int]							=   value




   def 			_lowerCAmelCase		( self     :		int						,     *lowerCAmelCase__     :		Any						,     **lowerCAmelCase__     :		Optional[Any]			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   kwargs.get("is_split_into_words"						,     lowerCAmelCase__			)
      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*lowerCAmelCase__						,     **lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		Dict						,     *lowerCAmelCase__     :		List[Any]						,     **lowerCAmelCase__     :		List[str]			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   kwargs.get("is_split_into_words"						,     lowerCAmelCase__			)

      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*lowerCAmelCase__						,     **lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		List[Any]						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		Optional[str] = None			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   self._tokenizer.model.save(lowerCAmelCase__						,     name=lowerCAmelCase__			)
      return tuple(lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		Optional[Any]						,     lowerCAmelCase__     :		List[int]						,     lowerCAmelCase__     :		Optional[List[int]] = None			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   [self.sep_token_id]
      _UpperCAmelCase							:	Tuple							=   [self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]




   def 			_lowerCAmelCase		( self     :		List[Any]						,     lowerCAmelCase__     :		List[int]						,     lowerCAmelCase__     :		Optional[List[int]] = None			)       ->    Optional[Any]:







      """simple docstring"""




      return token_ids_a + [self.eos_token_id]




   def 			_lowerCAmelCase		( self     :		Tuple						,     lowerCAmelCase__     :		"Conversation"			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   []
      for is_user, text in conversation.iter_texts():
         if is_user:
            # We need to space prefix as it's being done within blenderbot
            inputs.append(" " + text			)
         else:
            # Generated responses should contain them already.
            inputs.append(lowerCAmelCase__			)

      _UpperCAmelCase							:	List[Any]							=   "  ".join(lowerCAmelCase__			)
      _UpperCAmelCase							:	Tuple							=   self.encode(lowerCAmelCase__			)
      if len(lowerCAmelCase__			) > self.model_max_length:
         _UpperCAmelCase							:	Tuple							=   input_ids[-self.model_max_length :]
         logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""			)
      return input_ids

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_		(       __lowercase   :	Dict       ,					__lowercase   :	int       ,					__lowercase   :	Optional[Any]=None ) ->  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(__lowercase )
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
						_snake_case		=			nn.Parameter(__lowercase )






def        a_		(       __lowercase   :	Any       ,					__lowercase   :	Dict       ,					__lowercase   :	Union[str, Any] ) ->  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] )

			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        a_		(       __lowercase   :	str       ,					__lowercase   :	Tuple       ,					__lowercase   :	Any ) ->  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(__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        a_		(       __lowercase   :	Dict       ,					__lowercase   :	List[str]       ,					__lowercase   :	Union[str, Any] ) ->  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(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

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

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

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

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






def        a_		(       __lowercase   :	Tuple       ,					__lowercase   :	Tuple       ,					__lowercase   :	Dict ) ->  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(__lowercase )       ,					)

			if isinstance(weights[3]       ,					__lowercase ):
						_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(__lowercase ) )

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

			# 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(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

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






def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	Dict       ,					__lowercase   :	List[Any] ) ->  Optional[int]:
			# Initialise PyTorch model
			_snake_case		=			ReformerConfig.from_json_file(__lowercase )
			print(f'''Building PyTorch model from configuration: {config}''' )
			_snake_case		=			ReformerModelWithLMHead(__lowercase )

			with open(__lowercase       ,					'rb' ) as f:
						_snake_case		=			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__":
			_lowerCamelCase       :				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.'''
			)
			_lowerCamelCase       :				List[Any]       =			parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)