
import argparse
from pathlib import Path

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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE = None					,				__SCREAMING_SNAKE_CASE = None					,				__SCREAMING_SNAKE_CASE = None					,				):
	if config_name_or_path is None:
		lowercase	    =       'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

	if generator_tokenizer_name_or_path is None:
		lowercase	    =       generator_name_or_path

	if question_encoder_tokenizer_name_or_path is None:
		lowercase	    =       question_encoder_name_or_path

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

	# Save model.
	lowercase	    =       RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)

	lowercase	    =       gen_config
	lowercase	    =       question_encoder_config

	lowercase	    =       model_class.from_pretrained_question_encoder_generator(
	    __SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				config=__SCREAMING_SNAKE_CASE				)
	rag_model.save_pretrained(__SCREAMING_SNAKE_CASE				)

	# Sanity check.
	model_class.from_pretrained(__SCREAMING_SNAKE_CASE				)

	# Save tokenizers.
	lowercase	    =       AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE				)
	gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'				)
	lowercase	    =       AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE				)
	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,
	)








import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: List[Any]							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





