


'''simple docstring'''






import argparse
from pathlib import Path

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


def    _snake_case  (	A					,       A					,       A					,       A					,       A = None					,       A = None					,       A = None					,       )		->       Optional[Any]:
							if config_name_or_path is None:
														lowerCAmelCase__			  =      '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

							if generator_tokenizer_name_or_path is None:
														lowerCAmelCase__			  =      generator_name_or_path

							if question_encoder_tokenizer_name_or_path is None:
														lowerCAmelCase__			  =      question_encoder_name_or_path

							lowerCAmelCase__			  =      RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

							# Save model.
							lowerCAmelCase__			  =      RagConfig.from_pretrained(A			)
							lowerCAmelCase__			  =      AutoConfig.from_pretrained(A			)
							lowerCAmelCase__			  =      AutoConfig.from_pretrained(A			)

							lowerCAmelCase__			  =      gen_config
							lowerCAmelCase__			  =      question_encoder_config

							lowerCAmelCase__			  =      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.
							lowerCAmelCase__			  =      AutoTokenizer.from_pretrained(A			)
							gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
							lowerCAmelCase__			  =      AutoTokenizer.from_pretrained(A			)
							question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				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``'''
		    ),
		)

		__UpperCAmelCase       							=				parser.parse_args()

		__UpperCAmelCase       							=				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 logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d