






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


SCREAMING_SNAKE_CASE__		:							List[Any] 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							List[str] 				=				{
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'roc_bert'




     def __init__(				self	,						_lowerCAmelCase=30522	,						_lowerCAmelCase=768	,						_lowerCAmelCase=12	,						_lowerCAmelCase=12	,						_lowerCAmelCase=3072	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=2	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-12	,						_lowerCAmelCase=True	,						_lowerCAmelCase=0	,						_lowerCAmelCase="absolute"	,						_lowerCAmelCase=None	,						_lowerCAmelCase=True	,						_lowerCAmelCase=True	,						_lowerCAmelCase=768	,						_lowerCAmelCase=910	,						_lowerCAmelCase=512	,						_lowerCAmelCase=24858	,						_lowerCAmelCase=True	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			vocab_size
          UpperCAmelCase__      :  Tuple				      =			max_position_embeddings
          UpperCAmelCase__      :  Any				      =			hidden_size
          UpperCAmelCase__      :  int				      =			num_hidden_layers
          UpperCAmelCase__      :  Tuple				      =			num_attention_heads
          UpperCAmelCase__      :  Dict				      =			intermediate_size
          UpperCAmelCase__      :  Tuple				      =			hidden_act
          UpperCAmelCase__      :  List[str]				      =			hidden_dropout_prob
          UpperCAmelCase__      :  Dict				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  int				      =			initializer_range
          UpperCAmelCase__      :  List[Any]				      =			type_vocab_size
          UpperCAmelCase__      :  List[str]				      =			layer_norm_eps
          UpperCAmelCase__      :  List[str]				      =			use_cache
          UpperCAmelCase__      :  int				      =			enable_pronunciation
          UpperCAmelCase__      :  Tuple				      =			enable_shape
          UpperCAmelCase__      :  str				      =			pronunciation_embed_dim
          UpperCAmelCase__      :  Tuple				      =			pronunciation_vocab_size
          UpperCAmelCase__      :  Optional[Any]				      =			shape_embed_dim
          UpperCAmelCase__      :  Optional[int]				      =			shape_vocab_size
          UpperCAmelCase__      :  List[Any]				      =			concat_input
          UpperCAmelCase__      :  Optional[int]				      =			position_embedding_type
          UpperCAmelCase__      :  Any				      =			classifier_dropout
          super().__init__(pad_token_id=_lowerCAmelCase	,						**_lowerCAmelCase		)










import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

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

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  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			:						Any        =  parser.parse_args()
	A			:						str        =  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)

