




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a    				=      logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case							)    ->					int:
						_UpperCAmelCase	     =      RobertaPreLayerNormConfig.from_pretrained(
						    lowerCAmelCase__      ,  architectures=["""RobertaPreLayerNormForMaskedLM"""]							)

						# convert state_dict
						_UpperCAmelCase	     =      torch.load(hf_hub_download(repo_id=lowerCAmelCase__      ,  filename="""pytorch_model.bin"""							)							)
						_UpperCAmelCase	     =      {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("""roberta."""							):
																		_UpperCAmelCase	     =      """roberta_prelayernorm.""" + tensor_key[len("""roberta."""							) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(""".self.LayerNorm.weight"""							) or tensor_key.endswith(""".self.LayerNorm.bias"""							):
																		continue

												_UpperCAmelCase	     =      tensor_value

						_UpperCAmelCase	     =      RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=lowerCAmelCase__      ,  config=lowerCAmelCase__      ,  state_dict=lowerCAmelCase__							)
						model.save_pretrained(lowerCAmelCase__							)

						# convert tokenizer
						_UpperCAmelCase	     =      AutoTokenizer.from_pretrained(lowerCAmelCase__							)
						tokenizer.save_pretrained(lowerCAmelCase__							)


if __name__ == "__main__":
				a    				=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint-repo",
				    default=None,
				    type=str,
				    required=True,
				    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				a    				=      parser.parse_args()
				convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_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)



