






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

 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(lowercase_					,       lowercase_	)





def 		A_			(		lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						emb.weight.shape
 SCREAMING_SNAKE_CASE							  =						nn.Linear(lowercase_					,       lowercase_					,       bias=lowercase_	)
 SCREAMING_SNAKE_CASE							  =						emb.weight.data
 return lin_layer





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







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE							  =						PLBartConfig.from_pretrained(lowercase_					,       vocab_size=lowercase_	)

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

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

 return model


if __name__ == "__main__":
      __UpperCAmelCase      				=							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"
      )
      __UpperCAmelCase      				=							parser.parse_args()
      __UpperCAmelCase      				=							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)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      				=							{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      				=							[
          "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "VisualBertForMultipleChoice",
          "VisualBertForPreTraining",
          "VisualBertForQuestionAnswering",
          "VisualBertForRegionToPhraseAlignment",
          "VisualBertForVisualReasoning",
          "VisualBertLayer",
          "VisualBertModel",
          "VisualBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

      __UpperCAmelCase      				=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




