


import argparse
from pathlib import Path

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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ = None ,			UpperCamelCase__ = None ,			UpperCamelCase__ = None ,			)      ->		Optional[Any]:







		"""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(UpperCamelCase__	)
		A					       = AutoConfig.from_pretrained(UpperCamelCase__	)
		A					       = AutoConfig.from_pretrained(UpperCamelCase__	)

		A					       = gen_config
		A					       = question_encoder_config

		A					       = model_class.from_pretrained_question_encoder_generator(
		    UpperCamelCase__ ,			UpperCamelCase__ ,			config=UpperCamelCase__	)
		rag_model.save_pretrained(UpperCamelCase__	)

		# Sanity check.
		model_class.from_pretrained(UpperCamelCase__	)

		# Save tokenizers.
		A					       = AutoTokenizer.from_pretrained(UpperCamelCase__	)
		gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'	)
		A					       = AutoTokenizer.from_pretrained(UpperCamelCase__	)
		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						:				Optional[Any]             =      parser.parse_args()

	UpperCamelCase						:				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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase						:				int             =      logging.get_logger(__name__)

UpperCamelCase						:				Optional[int]             =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}



class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):
						lowerCAmelCase       =     """bit"""
						lowerCAmelCase       =     ["""preactivation""", """bottleneck"""]
						lowerCAmelCase       =     ["""SAME""", """VALID"""]







						def __init__(							self   :    List[str]			,			_lowercase   :    int=3			,			_lowercase   :    List[str]=64			,			_lowercase   :    Optional[int]=[256, 512, 1_024, 2_048]			,			_lowercase   :    Optional[int]=[3, 4, 6, 3]			,			_lowercase   :    int="preactivation"			,			_lowercase   :    Union[str, Any]="relu"			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Tuple=32			,			_lowercase   :    Optional[int]=0.0			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Dict=32			,			_lowercase   :    Union[str, Any]=1			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Any=None			,			**_lowercase   :    str			,			):
								super().__init__(**_lowercase		)
								if layer_type not in self.layer_types:
										raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
								if global_padding is not None:
										if global_padding.upper() in self.supported_padding:
												A					       = global_padding.upper()
										else:
												raise ValueError(f'Padding strategy {global_padding} not supported'		)
								A					       = num_channels
								A					       = embedding_size
								A					       = hidden_sizes
								A					       = depths
								A					       = layer_type
								A					       = hidden_act
								A					       = global_padding
								A					       = num_groups
								A					       = drop_path_rate
								A					       = embedding_dynamic_padding
								A					       = output_stride
								A					       = width_factor

								A					       = ['stem'] + [f'stage{idx}' for idx in range(1			,			len(_lowercase		) + 1		)]
								A       , A					       = get_aligned_output_features_output_indices(
								    out_features=_lowercase			,			out_indices=_lowercase			,			stage_names=self.stage_names		)





