




import argparse
from pathlib import Path

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




def 			__magic_name__				( A		:					List[str],  A		:					str,  A		:					str,  A		:					Path,  A		:					str = None,  A		:					str = None,  A		:					str = None,  ):






			'''simple docstring'''







			if config_name_or_path is None:
						a    =			"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

			if generator_tokenizer_name_or_path is None:
						a    =			generator_name_or_path

			if question_encoder_tokenizer_name_or_path is None:
						a    =			question_encoder_name_or_path

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

			# Save model.
			a    =			RagConfig.from_pretrained(A   )
			a    =			AutoConfig.from_pretrained(A   )
			a    =			AutoConfig.from_pretrained(A   )

			a    =			gen_config
			a    =			question_encoder_config

			a    =			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.
			a    =			AutoTokenizer.from_pretrained(A   )
			gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"   )
			a    =			AutoTokenizer.from_pretrained(A   )
			question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"   )


if __name__ == "__main__":
		__lowerCAmelCase		:				List[str]       			=		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``'
		    ),
		)

		__lowerCAmelCase		:				Any       			=		parser.parse_args()

		__lowerCAmelCase		:				Optional[int]       			=		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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__lowerCAmelCase		:				int       			=		{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					"""albert"""





				def __init__(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]=3_00_00					,      __lowerCamelCase				:     Union[str, Any]=1_28					,      __lowerCamelCase				:     Any=40_96					,      __lowerCamelCase				:     str=12					,      __lowerCamelCase				:     Union[str, Any]=1					,      __lowerCamelCase				:     Union[str, Any]=64					,      __lowerCamelCase				:     Dict=1_63_84					,      __lowerCamelCase				:     List[Any]=1					,      __lowerCamelCase				:     Optional[Any]="gelu_new"					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     Tuple=5_12					,      __lowerCamelCase				:     Optional[Any]=2					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     Optional[Any]=1e-12					,      __lowerCamelCase				:     Union[str, Any]=0.1					,      __lowerCamelCase				:     Any="absolute"					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     List[str]=2					,      __lowerCamelCase				:     Any=3					,      **__lowerCamelCase				:     int					,      )					->     int:
							super().__init__(pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase							)

							a    =			vocab_size
							a    =			embedding_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_hidden_groups
							a    =			num_attention_heads
							a    =			inner_group_num
							a    =			hidden_act
							a    =			intermediate_size
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			initializer_range
							a    =			layer_norm_eps
							a    =			classifier_dropout_prob
							a    =			position_embedding_type




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										a    =			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										a    =			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ]							)






