






import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ = None		,   lowerCamelCase_ = None		,   lowerCamelCase_ = None		,   ):






				'''simple docstring'''
				if config_name_or_path is None:
								lowercase__    =			"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

				if generator_tokenizer_name_or_path is None:
								lowercase__    =			generator_name_or_path

				if question_encoder_tokenizer_name_or_path is None:
								lowercase__    =			question_encoder_name_or_path

				lowercase__    =			RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

				# Save model.
				lowercase__    =			RagConfig.from_pretrained(__lowerCAmelCase				)
				lowercase__    =			AutoConfig.from_pretrained(__lowerCAmelCase				)
				lowercase__    =			AutoConfig.from_pretrained(__lowerCAmelCase				)

				lowercase__    =			gen_config
				lowercase__    =			question_encoder_config

				lowercase__    =			model_class.from_pretrained_question_encoder_generator(
				    __lowerCAmelCase		,   __lowerCAmelCase		,   config=__lowerCAmelCase				)
				rag_model.save_pretrained(__lowerCAmelCase				)

				# Sanity check.
				model_class.from_pretrained(__lowerCAmelCase				)

				# Save tokenizers.
				lowercase__    =			AutoTokenizer.from_pretrained(__lowerCAmelCase				)
				gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''				)
				lowercase__    =			AutoTokenizer.from_pretrained(__lowerCAmelCase				)
				question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''				)


if __name__ == "__main__":
							A__	:			int			   = argparse.ArgumentParser()
							parser.add_argument(
							    '--model_type',
							    choices=['rag_sequence', 'rag_token'],
							    required=True,
							    type=str,
							    help='RAG model type: rag_sequence, rag_token',
							)
							parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
							parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
							parser.add_argument(
							    '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
							)

							parser.add_argument(
							    '--generator_tokenizer_name_or_path',
							    type=str,
							    help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
							)
							parser.add_argument(
							    '--question_encoder_tokenizer_name_or_path',
							    type=str,
							    help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
							)
							parser.add_argument(
							    '--config_name_or_path',
							    type=str,
							    help=(
							        'Identifier of the model config to use, if not provided, resolves to a base config for a given'
							        ' ``model_type``'
							    ),
							)

							A__	:			Dict			   = parser.parse_args()

							A__	:			List[Any]			   = Path(args.dest)
							dest_dir.mkdir(exist_ok=True)

							consolidate(
							    args.model_type,
							    args.generator_name_or_path,
							    args.question_encoder_name_or_path,
							    dest_dir,
							    args.config_name_or_path,
							    args.generator_tokenizer_name_or_path,
							    args.question_encoder_tokenizer_name_or_path,
							)












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

lowerCamelCase	:							Union[str, Any]				=logging.get_logger(__name__)


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

lowerCamelCase	:							str				={
    '''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'''
    },
}

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





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Union[str, Any]        = VOCAB_FILES_NAMES
 _lowerCAmelCase       :       Dict        = PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase       :       List[Any]        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase       :       int        = ['''input_ids''', '''attention_mask''']
 _lowerCAmelCase       :       Tuple        = BlenderbotTokenizer







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


      '''simple docstring'''






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

      UpperCamelCase__			:   List[Any]       				=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
      if pre_tok_state.get("add_prefix_space"							,   SCREAMING_SNAKE_CASE						) != add_prefix_space:
           UpperCamelCase__			:   str       				=  getattr(SCREAMING_SNAKE_CASE							,   pre_tok_state.pop("type"						)						)
           UpperCamelCase__			:   Union[str, Any]       				=  add_prefix_space
           UpperCamelCase__			:   Union[str, Any]       				=  pre_tok_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  add_prefix_space

      UpperCamelCase__			:   Optional[int]       				=  "post_processor"
      UpperCamelCase__			:   Union[str, Any]       				=  getattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      if tokenizer_component_instance:
           UpperCamelCase__			:   Tuple       				=  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__			:   Tuple       				=  tuple(state["sep"]						)
           if "cls" in state:
                UpperCamelCase__			:   Optional[int]       				=  tuple(state["cls"]						)

           UpperCamelCase__			:   List[Any]       				=  False

           if state.get("add_prefix_space"							,   SCREAMING_SNAKE_CASE						) != add_prefix_space:
                UpperCamelCase__			:   str       				=  add_prefix_space
                UpperCamelCase__			:   Optional[Any]       				=  True

           if state.get("trim_offsets"							,   SCREAMING_SNAKE_CASE						) != trim_offsets:
                UpperCamelCase__			:   Optional[Any]       				=  trim_offsets
                UpperCamelCase__			:   Tuple       				=  True

           if changes_to_apply:
                UpperCamelCase__			:   Optional[int]       				=  getattr(SCREAMING_SNAKE_CASE							,   state.pop("type"						)						)
                UpperCamelCase__			:   Dict       				=  component_class(**SCREAMING_SNAKE_CASE						)
                setattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 					__lowercase					(		self					:   Union[str, 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 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  AddedToken(SCREAMING_SNAKE_CASE							,   lstrip=SCREAMING_SNAKE_CASE							,   rstrip=SCREAMING_SNAKE_CASE						) if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) else value
      UpperCamelCase__			:   str       				=  value







 def 					__lowercase					(		self					:   Any							,   *SCREAMING_SNAKE_CASE					:   List[str]							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)
      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(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   *SCREAMING_SNAKE_CASE					:   int							,   **SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)

      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(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[str] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE							,   name=SCREAMING_SNAKE_CASE						)
      return tuple(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  [self.sep_token_id]
      UpperCamelCase__			:   int       				=  [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 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      return token_ids_a + [self.eos_token_id]







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   "Conversation"						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  []
      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(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   List[str]       				=  "  ".join(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  self.encode(SCREAMING_SNAKE_CASE						)
      if len(SCREAMING_SNAKE_CASE						) > self.model_max_length:
           UpperCamelCase__			:   Dict       				=  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