

import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A = None				,		_A = None				,		_A = None				,		):





					'''simple docstring'''




					if config_name_or_path is None:
										SCREAMING_SNAKE_CASE__   =   '''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__   =   generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										SCREAMING_SNAKE_CASE__   =   question_encoder_name_or_path

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

					# Save model.
					SCREAMING_SNAKE_CASE__   =   RagConfig.from_pretrained(_A    )
					SCREAMING_SNAKE_CASE__   =   AutoConfig.from_pretrained(_A    )
					SCREAMING_SNAKE_CASE__   =   AutoConfig.from_pretrained(_A    )

					SCREAMING_SNAKE_CASE__   =   gen_config
					SCREAMING_SNAKE_CASE__   =   question_encoder_config

					SCREAMING_SNAKE_CASE__   =   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.
					SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained(_A    )
					gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''    )
					SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained(_A    )
					question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''    )


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

							_SCREAMING_SNAKE_CASE					:  Dict             =      parser.parse_args()

							_SCREAMING_SNAKE_CASE					:  Dict             =      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,
							)





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


