




'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def 		a_( lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       lowercase  :		Optional[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(lowerCamelCase   ,				lowerCamelCase      )




def 		a_( lowerCamelCase      )				->Tuple:






       '''simple docstring'''




       lowercase,     lowercase  :		List[str]						=      emb.weight.shape
       lowercase  :		Optional[Any]						=      nn.Linear(lowerCamelCase   ,				lowerCamelCase   ,				bias=lowerCamelCase      )
       lowercase  :		Dict						=      emb.weight.data
       return lin_layer




def 		a_( lowerCamelCase   ,				lowerCamelCase="uclanlp/plbart-base"   ,				lowerCamelCase=False   ,				lowerCamelCase=False      )				->Union[str, Any]:






       '''simple docstring'''




       lowercase  :		int						=      torch.load(lowerCamelCase   ,				map_location='''cpu'''      )['''model''']
       remove_ignore_keys_(lowerCamelCase      )
       lowercase  :		List[Any]						=      state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase  :		Union[str, Any]						=      PLBartConfig.from_pretrained(lowerCamelCase   ,				vocab_size=lowerCamelCase      )

       lowercase  :		Union[str, Any]						=      state_dict['''decoder.embed_tokens.weight''']
       if not classification:
              lowercase  :		List[Any]						=      PLBartForConditionalGeneration(lowerCamelCase      )
              model.model.load_state_dict(lowerCamelCase      )
              if finetuned:
                     lowercase  :		Optional[int]						=      make_linear_from_emb(model.model.shared      )

       else:
              lowercase  :		List[Any]						=      {}
              for key, value in state_dict.copy().items():
                     if key.startswith('''classification_heads.sentence_classification_head'''      ):
                            lowercase  :		Tuple						=      value
                            state_dict.pop(lowerCamelCase      )
              lowercase  :		Optional[int]						=      PLBartForSequenceClassification(lowerCamelCase      )
              model.model.load_state_dict(lowerCamelCase      )
              model.classification_head.load_state_dict(lowerCamelCase      )

       return model


if __name__ == "__main__":
      lowercase	:      Optional[int]				=       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'''
      )
      lowercase	:      Tuple				=       parser.parse_args()
      lowercase	:      Dict				=       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)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase	:      int				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




