



import argparse
from pathlib import Path

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     )		->							int:
		if config_name_or_path is None:
				UpperCAmelCase_						=				"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

		if generator_tokenizer_name_or_path is None:
				UpperCAmelCase_						=				generator_name_or_path

		if question_encoder_tokenizer_name_or_path is None:
				UpperCAmelCase_						=				question_encoder_name_or_path

		UpperCAmelCase_						=				RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

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

		UpperCAmelCase_						=				gen_config
		UpperCAmelCase_						=				question_encoder_config

		UpperCAmelCase_						=				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.
		UpperCAmelCase_						=				AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
		gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/" )
		UpperCAmelCase_						=				AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
		question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/" )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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     				=						parser.parse_args()

			SCREAMING_SNAKE_CASE     				=						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 MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				load_tf_weights_in_mobilebert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT 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."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

