


"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration


def        lowercase     (SCREAMING_SNAKE_CASE_						:							str    )  ->		Union[str, Any]:
						SCREAMING_SNAKE_CASE						=   [
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						    """decoder.output_projection.weight""",
						]
						for k in ignore_keys:
												state_dict.pop(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE    )


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Dict    )  ->		str:
						SCREAMING_SNAKE_CASE						=   emb.weight.shape
						SCREAMING_SNAKE_CASE						=   nn.Linear(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     bias=_SCREAMING_SNAKE_CASE    )
						SCREAMING_SNAKE_CASE						=   emb.weight.data
						return lin_layer


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Tuple					,     SCREAMING_SNAKE_CASE_						:							Optional[int]="facebook/mbart-large-en-ro"					,     SCREAMING_SNAKE_CASE_						:							Tuple=False					,     SCREAMING_SNAKE_CASE_						:							Any=False    )  ->		List[str]:
						SCREAMING_SNAKE_CASE						=   torch.load(_SCREAMING_SNAKE_CASE					,     map_location='cpu'    )["""model"""]
						remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
						SCREAMING_SNAKE_CASE						=   state_dict["""encoder.embed_tokens.weight"""].shape[0]

						SCREAMING_SNAKE_CASE						=   MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE					,     vocab_size=_SCREAMING_SNAKE_CASE    )
						if mbart_aa and finetuned:
												SCREAMING_SNAKE_CASE						=   """relu"""

						SCREAMING_SNAKE_CASE						=   state_dict["""decoder.embed_tokens.weight"""]
						SCREAMING_SNAKE_CASE						=   MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
						model.model.load_state_dict(_SCREAMING_SNAKE_CASE    )

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

						return model


if __name__ == "__main__":
	__UpperCamelCase		= 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''')
	__UpperCamelCase		= parser.parse_args()
	__UpperCamelCase		= 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)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:     int         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class        UpperCamelCase						(				__a				):
						a__		:List[str]      =			'''megatron-bert'''





						def __init__(self			,     __UpperCamelCase=29_056			,     __UpperCamelCase=1_024			,     __UpperCamelCase=24			,     __UpperCamelCase=16			,     __UpperCamelCase=4_096			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=512			,     __UpperCamelCase=2			,     __UpperCamelCase=0.02			,     __UpperCamelCase=1E-1_2			,     __UpperCamelCase=0			,     __UpperCamelCase="absolute"			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(pad_token_id=__UpperCamelCase			,     **__UpperCamelCase						)

												UpperCamelCase_		:							Union[str, Any]  =		vocab_size
												UpperCamelCase_		:							Union[str, Any]  =		hidden_size
												UpperCamelCase_		:							Any  =		num_hidden_layers
												UpperCamelCase_		:							str  =		num_attention_heads
												UpperCamelCase_		:							List[Any]  =		hidden_act
												UpperCamelCase_		:							Dict  =		intermediate_size
												UpperCamelCase_		:							Optional[int]  =		hidden_dropout_prob
												UpperCamelCase_		:							Dict  =		attention_probs_dropout_prob
												UpperCamelCase_		:							Any  =		max_position_embeddings
												UpperCamelCase_		:							str  =		type_vocab_size
												UpperCamelCase_		:							Tuple  =		initializer_range
												UpperCamelCase_		:							List[str]  =		layer_norm_eps
												UpperCamelCase_		:							Tuple  =		position_embedding_type
												UpperCamelCase_		:							Tuple  =		use_cache






