




import argparse
from pathlib import Path

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




def 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case      ,  snake_case      ,  snake_case      ,  snake_case = None      ,  snake_case = None      ,  snake_case = None      ,  )    ->					Union[str, Any]:
						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(lowerCAmelCase__							)
						_UpperCAmelCase	     =      AutoConfig.from_pretrained(lowerCAmelCase__							)
						_UpperCAmelCase	     =      AutoConfig.from_pretrained(lowerCAmelCase__							)

						_UpperCAmelCase	     =      gen_config
						_UpperCAmelCase	     =      question_encoder_config

						_UpperCAmelCase	     =      model_class.from_pretrained_question_encoder_generator(
						    lowerCAmelCase__      ,  lowerCAmelCase__      ,  config=lowerCAmelCase__							)
						rag_model.save_pretrained(lowerCAmelCase__							)

						# Sanity check.
						model_class.from_pretrained(lowerCAmelCase__							)

						# Save tokenizers.
						_UpperCAmelCase	     =      AutoTokenizer.from_pretrained(lowerCAmelCase__							)
						gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""							)
						_UpperCAmelCase	     =      AutoTokenizer.from_pretrained(lowerCAmelCase__							)
						question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""							)


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

				a    				=      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"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

			if finetuned:
						lowerCamelCase_					=  make_linear_from_emb(model.model.shared						)

			return model


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
						)
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument(
						    """--hf_config""",
						    default="""facebook/mbart-large-cc25""",
						    type=str,
						    help="""Which huggingface architecture to use: mbart-large""",
						)
						parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
						parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
						A_    							=					parser.parse_args()
						A_    							=					convert_fairseq_mbart_checkpoint_from_disk(
						    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
						)
						model.save_pretrained(args.pytorch_dump_folder_path)



