

import argparse
from pathlib import Path

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



def    __UpperCAmelCase (	__a      :			int		,__a      :			str		,__a      :			str		,__a      :			Path		,__a      :			str = None		,__a      :			str = None		,__a      :			str = None		,)			->     List[Any]:

					"""simple docstring"""

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

					if generator_tokenizer_name_or_path is None:
										_a	:    Dict         =		generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										_a	:    str         =		question_encoder_name_or_path

					_a	:    Optional[Any]         =		RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

					# Save model.
					_a	:    Optional[Any]         =		RagConfig.from_pretrained(__a	)
					_a	:    str         =		AutoConfig.from_pretrained(__a	)
					_a	:    str         =		AutoConfig.from_pretrained(__a	)

					_a	:    Dict         =		gen_config
					_a	:    Union[str, Any]         =		question_encoder_config

					_a	:    List[str]         =		model_class.from_pretrained_question_encoder_generator(
					    __a		,__a		,config=__a	)
					rag_model.save_pretrained(__a	)

					# Sanity check.
					model_class.from_pretrained(__a	)

					# Save tokenizers.
					_a	:    Dict         =		AutoTokenizer.from_pretrained(__a	)
					gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''	)
					_a	:    List[str]         =		AutoTokenizer.from_pretrained(__a	)
					question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''	)


if __name__ == "__main__":
			a__				=		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__				=		parser.parse_args()

			a__				=		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 logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


a__				=		{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



