






'''simple docstring'''







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Path   ,    __lowerCAmelCase		:			str = None   ,    __lowerCAmelCase		:			str = None   ,    __lowerCAmelCase		:			str = None   ,    ):
						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(__lowerCAmelCase						)
						lowerCamelCase__         =  AutoConfig.from_pretrained(__lowerCAmelCase						)
						lowerCamelCase__         =  AutoConfig.from_pretrained(__lowerCAmelCase						)

						lowerCamelCase__         =  gen_config
						lowerCamelCase__         =  question_encoder_config

						lowerCamelCase__         =  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__         =  AutoTokenizer.from_pretrained(__lowerCAmelCase						)
						gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""						)
						lowerCamelCase__         =  AutoTokenizer.from_pretrained(__lowerCAmelCase						)
						question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""						)


if __name__ == "__main__":
							UpperCamelCase    :      List[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``'
							    ),
							)

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

							UpperCamelCase    :      Any  			=		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 argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase    :      Optional[Any]  			=		['small', 'medium', 'large']

UpperCamelCase    :      Dict  			=		'lm_head.decoder.weight'
UpperCamelCase    :      int  			=		'lm_head.weight'
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						lowerCamelCase__         =  d.pop(__lowerCAmelCase						)
						os.makedirs(__lowerCAmelCase   ,    exist_ok=__lowerCAmelCase						)
						torch.save(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)


if __name__ == "__main__":
							UpperCamelCase    :      Any  			=		argparse.ArgumentParser()
							parser.add_argument('--dialogpt_path', default='.', type=str)
							UpperCamelCase    :      Dict  			=		parser.parse_args()
							for MODEL in DIALOGPT_MODELS:
														UpperCamelCase    :      Any  			=		os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
														UpperCamelCase    :      str  			=		F'./DialoGPT-{MODEL}'
														convert_dialogpt_checkpoint(
														    checkpoint_path,
														    pytorch_dump_folder_path,
														)

