






'''simple docstring'''

import argparse
from pathlib import Path

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






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	Optional[Any]						,			a		:	Optional[Any]						,			a		:	List[str]						,			a		:	Optional[int] = None						,			a		:	Optional[Any] = None						,			a		:	Dict = None						,			)			->			Tuple:





						"""simple docstring"""







						if config_name_or_path is None:
												lowercase_      :   Dict        =			'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

						if generator_tokenizer_name_or_path is None:
												lowercase_      :   Optional[Any]        =			generator_name_or_path

						if question_encoder_tokenizer_name_or_path is None:
												lowercase_      :   Any        =			question_encoder_name_or_path

						lowercase_      :   Tuple        =			RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

						# Save model.
						lowercase_      :   str        =			RagConfig.from_pretrained(__snake_case							)
						lowercase_      :   Union[str, Any]        =			AutoConfig.from_pretrained(__snake_case							)
						lowercase_      :   Any        =			AutoConfig.from_pretrained(__snake_case							)

						lowercase_      :   Optional[Any]        =			gen_config
						lowercase_      :   List[Any]        =			question_encoder_config

						lowercase_      :   Tuple        =			model_class.from_pretrained_question_encoder_generator(
						    __snake_case						,			__snake_case						,			config=__snake_case							)
						rag_model.save_pretrained(__snake_case							)

						# Sanity check.
						model_class.from_pretrained(__snake_case							)

						# Save tokenizers.
						lowercase_      :   Optional[Any]        =			AutoTokenizer.from_pretrained(__snake_case							)
						gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'							)
						lowercase_      :   Tuple        =			AutoTokenizer.from_pretrained(__snake_case							)
						question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'							)


if __name__ == "__main__":
						A:      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``"
						    ),
						)

						A:      int  				=				parser.parse_args()

						A:      Tuple  				=				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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:      int  				=				{
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Union[str, Any]  				=				[
						    "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TrajectoryTransformerModel",
						    "TrajectoryTransformerPreTrainedModel",
						    "load_tf_weights_in_trajectory_transformer",
						]


if TYPE_CHECKING:
						from .configuration_trajectory_transformer import (
						    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TrajectoryTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trajectory_transformer import (
												    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TrajectoryTransformerModel,
												    TrajectoryTransformerPreTrainedModel,
												    load_tf_weights_in_trajectory_transformer,
												)


else:
						import sys

						A:      int  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
