


"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def     lowercase__	(  lowerCamelCase     :  Optional[Any]				)					->     str:
  lowerCAmelCase__    :    List[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     lowercase__	(  lowerCamelCase     :  Union[str, Any]				)					->     Any:
  lowerCAmelCase__    :    List[str]								=					emb.weight.shape
  lowerCAmelCase__    :    str								=					nn.Linear(lowerCamelCase				,	lowerCamelCase				,	bias=lowerCamelCase				)
  lowerCAmelCase__    :    Optional[int]								=					emb.weight.data
  return lin_layer





def     lowercase__	(  lowerCamelCase     :  Union[str, Any]				,	lowerCamelCase     :  Dict="uclanlp/plbart-base"				,	lowerCamelCase     :  Tuple=False				,	lowerCamelCase     :  Optional[int]=False				)					->     int:
  lowerCAmelCase__    :    int								=					torch.load(lowerCamelCase				,	map_location="cpu"				)["model"]
  remove_ignore_keys_(lowerCamelCase				)
  lowerCAmelCase__    :    List[Any]								=					state_dict["encoder.embed_tokens.weight"].shape[0]

  lowerCAmelCase__    :    str								=					PLBartConfig.from_pretrained(lowerCamelCase				,	vocab_size=lowerCamelCase				)

  lowerCAmelCase__    :    List[Any]								=					state_dict["decoder.embed_tokens.weight"]
  if not classification:
    lowerCAmelCase__    :    Dict								=					PLBartForConditionalGeneration(lowerCamelCase				)
    model.model.load_state_dict(lowerCamelCase				)
    if finetuned:
      lowerCAmelCase__    :    Optional[Any]								=					make_linear_from_emb(model.model.shared				)

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

  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)




"""simple docstring"""





from __future__ import annotations





def     lowercase__	(  lowerCamelCase     :  list				,	lowerCamelCase     :  list				)					->     list:
  lowerCAmelCase__    :    Dict								=					[None] * (len(lowerCamelCase				) + len(lowerCamelCase				))

  lowerCAmelCase__    :    Optional[Any]								=					0  # pointer to current index for left Half
  lowerCAmelCase__    :    Any								=					0  # pointer to current index for the right Half
  lowerCAmelCase__    :    Dict								=					0  # pointer to current index for the sorted array Half

  while pointera < len(lowerCamelCase				) and pointera < len(lowerCamelCase				):
    if left_half[pointera] < right_half[pointera]:
      lowerCAmelCase__    :    Tuple								=					left_half[pointera]
      pointera += 1
      index += 1
    else:
      lowerCAmelCase__    :    int								=					right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Tuple								=					left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Optional[int]								=					right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array





def     lowercase__	(  lowerCamelCase     :  list				)					->     list:
  if len(lowerCamelCase				) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  lowerCAmelCase__    :    Optional[int]								=					0 + (len(lowerCamelCase				) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  lowerCAmelCase__    :    Union[str, Any]								=					array[:middle]
  lowerCAmelCase__    :    List[str]								=					array[middle:]

  return merge(merge_sort(lowerCamelCase				)				,	merge_sort(lowerCamelCase				)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
