



'''simple docstring'''
import argparse
from pathlib import Path

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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = None  ,  ):



						if config_name_or_path is None:
												_UpperCamelCase :		List[Any]			    =       'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

						if generator_tokenizer_name_or_path is None:
												_UpperCamelCase :		Optional[int]			    =       generator_name_or_path

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

						_UpperCamelCase :		Union[str, Any]			    =       RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

						# Save model.
						_UpperCamelCase :		Optional[Any]			    =       RagConfig.from_pretrained(_lowerCAmelCase							)
						_UpperCamelCase :		Optional[int]			    =       AutoConfig.from_pretrained(_lowerCAmelCase							)
						_UpperCamelCase :		Optional[Any]			    =       AutoConfig.from_pretrained(_lowerCAmelCase							)

						_UpperCamelCase :		Union[str, Any]			    =       gen_config
						_UpperCamelCase :		Optional[int]			    =       question_encoder_config

						_UpperCamelCase :		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.
						_UpperCamelCase :		Dict			    =       AutoTokenizer.from_pretrained(_lowerCAmelCase							)
						gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'							)
						_UpperCamelCase :		Union[str, Any]			    =       AutoTokenizer.from_pretrained(_lowerCAmelCase							)
						question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'							)


if __name__ == "__main__":
					snake_case_			:     Tuple  	=  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``'
					    ),
					)

					snake_case_			:     List[str]  	=  parser.parse_args()

					snake_case_			:     str  	=  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'''
from torch import nn






def A__   (					UpperCAmelCase_							):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(f'Unsupported activation function: {act_fn}'							)


