




'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def  SCREAMING_SNAKE_CASE_ (							__A				: List[str]  ) -> Dict:
   _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  SCREAMING_SNAKE_CASE_ (							__A				: Any  ) -> Union[str, Any]:
   _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  SCREAMING_SNAKE_CASE_ (							__A				: Any       ,     __A				: List[str]="uclanlp/plbart-base"       ,     __A				: Union[str, Any]=False       ,     __A				: Optional[int]=False  ) -> Optional[int]:
   _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__":
 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)







'''simple docstring'''







from functools import lru_cache






@lru_cache
def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if not isinstance(lowercase_						,							lowercase_  ):
				raise TypeError("k must be an integer."  )
		if k < 0:
				raise ValueError("k integer must be greater or equal to zero."  )
		if k == 0:
				return 0
		elif k == 1:
				return 1
		else:
				return fibonacci(k - 1  ) + fibonacci(k - 2  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       len(lowercase_  )
		# Find m such that F_m >= n where F_i is the i_th fibonacci number.
		_snake_case	       =       0
		while True:
				if fibonacci(lowercase_  ) >= len_list:
						_snake_case	       =       i
						break
				i += 1
		_snake_case	       =       0
		while fibb_k > 0:
				_snake_case	       =       min(
				    offset + fibonacci(fibb_k - 1  )						,							len_list - 1  )  # Prevent out of range
				_snake_case	       =       arr[index_k]
				if item_k_1 == val:
						return index_k
				elif val < item_k_1:
						fibb_k -= 1
				elif val > item_k_1:
						offset += fibonacci(fibb_k - 1  )
						fibb_k -= 2
		else:
				return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()





