


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






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






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[Any]       	=   emb.weight.shape
		lowerCAmelCase__  :     str       	=   nn.Linear(_a      ,  _a      ,  bias=_a			)
		lowerCAmelCase__  :     str       	=   emb.weight.data
		return lin_layer






def 	lowerCamelCase_       (			_a      ,  _a="uclanlp/plbart-base"      ,  _a=False      ,  _a=False			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   torch.load(_a      ,  map_location='''cpu'''			)['''model''']
		remove_ignore_keys_(_a			)
		lowerCAmelCase__  :     Union[str, Any]       	=   state_dict['''encoder.embed_tokens.weight'''].shape[0]

		lowerCAmelCase__  :     Dict       	=   PLBartConfig.from_pretrained(_a      ,  vocab_size=_a			)

		lowerCAmelCase__  :     Any       	=   state_dict['''decoder.embed_tokens.weight''']
		if not classification:
				lowerCAmelCase__  :     Any       	=   PLBartForConditionalGeneration(_a			)
				model.model.load_state_dict(_a			)
				if finetuned:
						lowerCAmelCase__  :     Optional[Any]       	=   make_linear_from_emb(model.model.shared			)

		else:
				lowerCAmelCase__  :     int       	=   {}
				for key, value in state_dict.copy().items():
						if key.startswith('''classification_heads.sentence_classification_head'''			):
								lowerCAmelCase__  :     Dict       	=   value
								state_dict.pop(_a			)
				lowerCAmelCase__  :     Optional[int]       	=   PLBartForSequenceClassification(_a			)
				model.model.load_state_dict(_a			)
				model.classification_head.load_state_dict(_a			)

		return model


if __name__ == "__main__":
		lowerCamelCase		=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''fairseq_path''', type=str, help='''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='''uclanlp/plbart-base''',
		    type=str,
		    help='''Which huggingface architecture to use: plbart-base''',
		)
		parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
		parser.add_argument(
		    '''--classification''', action='''store_true''', help='''whether the model is a classification checkpoint'''
		)
		lowerCamelCase		=						parser.parse_args()
		lowerCamelCase		=						convert_fairseq_plbart_checkpoint_from_disk(
		    args.fairseq_path,
		    hf_config_path=args.hf_config,
		    finetuned=args.finetuned,
		    classification=args.classification,
		)
		model.save_pretrained(args.pytorch_dump_folder_path)








import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


lowerCamelCase		=						logging.get_logger(__name__)





class    _a				(							_lowercase):




	def __init__(   self		:						Any						,   *_SCREAMING_SNAKE_CASE		:						int						,   **_SCREAMING_SNAKE_CASE		:						Dict						)->			None:
			warnings.warn(
			    '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
			    ''' Please use MaskFormerImageProcessor instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			super().__init__(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




