import copy
import random

from transformers import CLIPTokenizer






class 		a__						(					A__					):

					def __init__(    self      :       Tuple,*_A      :       int,**_A      :       str    ):







							"""simple docstring"""






							super().__init__(*_A,**_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    {}

					def 				__UpperCamelCase   (    self      :       int,_A      :       Tuple,*_A      :       Optional[int],**_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    super().add_tokens(_A,*_A,**_A    )
							if num_added_tokens == 0:
									raise ValueError(
									    F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
									    " `placeholder_token` that is not already in the tokenizer."    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,*_A      :       Union[str, Any],_A      :       Union[str, Any]=1,**_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    []
							if num_vec_per_token == 1:
									self.try_adding_tokens(_A,*_A,**_A    )
									output.append(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		int				      =    []
									for i in range(_A    ):
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    placeholder_token + F'_{i}'
											self.try_adding_tokens(_A,*_A,**_A    )
											output.append(_A    )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
							for token in self.token_map:
									if token in placeholder_token:
											raise ValueError(
											    F'The tokenizer already has placeholder token {token} that can get confused with'
											    F' {placeholder_token}keep placeholder tokens independent'    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    output

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[Any],_A      :       str=False,_A      :       List[Any]=1.0    ):







							"""simple docstring"""






							if isinstance(_A,_A    ):
									SCREAMING_SNAKE_CASE_  :		Dict				      =    []
									for i in range(len(_A    )    ):
											output.append(self.replace_placeholder_tokens_in_text(text[i],vector_shuffle=_A    )    )
									return output
							for placeholder_token in self.token_map:
									if placeholder_token in text:
											SCREAMING_SNAKE_CASE_  :		str				      =    self.token_map[placeholder_token]
											SCREAMING_SNAKE_CASE_  :		Dict				      =    tokens[: 1 + int(len(_A    ) * prop_tokens_to_load    )]
											if vector_shuffle:
													SCREAMING_SNAKE_CASE_  :		List[str]				      =    copy.copy(_A    )
													random.shuffle(_A    )
											SCREAMING_SNAKE_CASE_  :		List[str]				      =    text.replace(_A," ".join(_A    )    )
							return text

					def __call__(    self      :       List[Any],_A      :       Any,*_A      :       List[str],_A      :       List[str]=False,_A      :       Dict=1.0,**_A      :       Dict    ):







							"""simple docstring"""






							return super().__call__(
							    self.replace_placeholder_tokens_in_text(
							        _A,vector_shuffle=_A,prop_tokens_to_load=_A    ),*_A,**_A,)






					def 				__UpperCamelCase   (    self      :       Any,_A      :       Tuple,*_A      :       Dict,_A      :       str=False,_A      :       List[str]=1.0,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							return super().encode(
							    self.replace_placeholder_tokens_in_text(
							        _A,vector_shuffle=_A,prop_tokens_to_load=_A    ),*_A,**_A,)

import argparse
from pathlib import Path

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




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Path   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , ):
		"""simple docstring"""







		if config_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

		if generator_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    generator_name_or_path

		if question_encoder_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    question_encoder_name_or_path

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

		# Save model.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    RagConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    AutoConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		int				      =    AutoConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    gen_config
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    question_encoder_config

		SCREAMING_SNAKE_CASE_  :		Dict				      =    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.
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[Any]       =  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``'''
		    ),
		)

		__lowerCamelCase					:	str       =  parser.parse_args()

		__lowerCamelCase					:	int       =  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,
		)
