


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def 						_A						(						_lowercase				)			->				Dict:


     """simple docstring"""


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






def 						_A						(						_lowercase				)			->				Dict:


     """simple docstring"""


     __UpperCamelCase,  __UpperCamelCase				=	emb.weight.shape
     __UpperCamelCase				=	nn.Linear(_lowercase			,       _lowercase			,       bias=_lowercase				)
     __UpperCamelCase				=	emb.weight.data
     return lin_layer






def 						_A						(						_lowercase			,       _lowercase="uclanlp/plbart-base"			,       _lowercase=False			,       _lowercase=False				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	torch.load(_lowercase			,       map_location='cpu'				)['model']
     remove_ignore_keys_(_lowercase				)
     __UpperCamelCase				=	state_dict['encoder.embed_tokens.weight'].shape[0]

     __UpperCamelCase				=	PLBartConfig.from_pretrained(_lowercase			,       vocab_size=_lowercase				)

     __UpperCamelCase				=	state_dict['decoder.embed_tokens.weight']
     if not classification:
          __UpperCamelCase				=	PLBartForConditionalGeneration(_lowercase				)
          model.model.load_state_dict(_lowercase				)
          if finetuned:
               __UpperCamelCase				=	make_linear_from_emb(model.model.shared				)

     else:
          __UpperCamelCase				=	{}
          for key, value in state_dict.copy().items():
               if key.startswith('classification_heads.sentence_classification_head'				):
                    __UpperCamelCase				=	value
                    state_dict.pop(_lowercase				)
          __UpperCamelCase				=	PLBartForSequenceClassification(_lowercase				)
          model.model.load_state_dict(_lowercase				)
          model.classification_head.load_state_dict(_lowercase				)

     return model


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






def 						_A						(						_lowercase				)			->				list:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive integers have prime factors'				)
     __UpperCamelCase				=	[]
     while n % 2 == 0:
          pf.append(2				)
          __UpperCamelCase				=	int(n / 2				)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          while n % i == 0:
               pf.append(_lowercase				)
               __UpperCamelCase				=	int(n / i				)
     if n > 2:
          pf.append(_lowercase				)
     return pf






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     div *= temp
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          div *= temp
     if n > 1:
          div *= 2
     return div






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     if temp > 1:
          s *= (2**temp - 1) / (2 - 1)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          if temp > 1:
               s *= (i**temp - 1) / (i - 1)
     return int(_lowercase				)






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	n
     for x in set(prime_factors(_lowercase				)				):
          s *= (x - 1) / x
     return int(_lowercase				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


