






import argparse
from pathlib import Path

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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase = None       ,    UpperCamelCase = None       ,    UpperCamelCase = None       ,    )   -> Dict:




							if config_name_or_path is None:
														lowerCamelCase__      :			Any 						= "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

							if generator_tokenizer_name_or_path is None:
														lowerCamelCase__      :			Tuple 						= generator_name_or_path

							if question_encoder_tokenizer_name_or_path is None:
														lowerCamelCase__      :			List[str] 						= question_encoder_name_or_path

							lowerCamelCase__      :			Optional[Any] 						= RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

							# Save model.
							lowerCamelCase__      :			Union[str, Any] 						= RagConfig.from_pretrained(_lowerCAmelCase   )
							lowerCamelCase__      :			List[Any] 						= AutoConfig.from_pretrained(_lowerCAmelCase   )
							lowerCamelCase__      :			Optional[Any] 						= AutoConfig.from_pretrained(_lowerCAmelCase   )

							lowerCamelCase__      :			Tuple 						= gen_config
							lowerCamelCase__      :			Any 						= question_encoder_config

							lowerCamelCase__      :			List[Any] 						= 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.
							lowerCamelCase__      :			str 						= AutoTokenizer.from_pretrained(_lowerCAmelCase   )
							gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""   )
							lowerCamelCase__      :			List[Any] 						= 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			:    Tuple					  =parser.parse_args()

			_A			:    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,
			)









'''simple docstring'''







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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


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

_A			:    Dict					  ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:    Tuple					  ={
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_A			:    List[Any]					  ={
    '''gpt-neox-20b''': 2_048,
}

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """attention_mask"""]




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Union[str, Any]="<|endoftext|>"    ,							UpperCamelCase__:       Tuple=False    ,							**UpperCamelCase__:       str    ,							):
														super().__init__(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							bos_token=UpperCamelCase__    ,							eos_token=UpperCamelCase__    ,							add_prefix_space=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

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

														lowerCamelCase__      :			Dict 						= add_prefix_space




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= self._tokenizer.model.save(UpperCamelCase__    ,							name=UpperCamelCase__  )
														return tuple(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       "Conversation"  ):
														lowerCamelCase__      :			str 						= []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  ) + [self.eos_token_id]  )

														if len(UpperCamelCase__  ) > self.model_max_length:
																					lowerCamelCase__      :			int 						= input_ids[-self.model_max_length :]
														return input_ids

