

"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Tuple					):


					'''simple docstring'''






					UpperCamelCase__		:     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(A_   ,				A_					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=emb.weight.shape
					UpperCamelCase__		:     Tuple								=nn.Linear(A_   ,				A_   ,				bias=A_					)
					UpperCamelCase__		:     int								=emb.weight.data
					return lin_layer



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				List[Any]="uclanlp/plbart-base"   ,				UpperCAmelCase  :				Dict=False   ,				UpperCAmelCase  :				Any=False					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[int]								=torch.load(A_   ,				map_location='''cpu'''					)["model"]
					remove_ignore_keys_(A_					)
					UpperCamelCase__		:     Dict								=state_dict["encoder.embed_tokens.weight"].shape[0]

					UpperCamelCase__		:     List[Any]								=PLBartConfig.from_pretrained(A_   ,				vocab_size=A_					)

					UpperCamelCase__		:     int								=state_dict["decoder.embed_tokens.weight"]
					if not classification:
										UpperCamelCase__		:     int								=PLBartForConditionalGeneration(A_					)
										model.model.load_state_dict(A_					)
										if finetuned:
															UpperCamelCase__		:     List[Any]								=make_linear_from_emb(model.model.shared					)

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

					return model


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE							:	Tuple										=			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"""
				)
				_SCREAMING_SNAKE_CASE							:	Union[str, Any]										=			parser.parse_args()
				_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			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"""







class 		_A    :







    """simple docstring"""




    def __init__(   self							:       Any):
      a		:						Tuple 				=     [0]
      a		:						str 				=     0






    def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       int):
      a		:						Any 				=     self.__heap[i]
      while i // 2 > 0:
        if self.__heap[i] > self.__heap[i // 2]:
          a		:						Tuple 				=     self.__heap[i // 2]
          a		:						Any 				=     temporary
        i //= 2






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       int):
      self.__heap.append(__UpperCAmelCase)
      self.__size += 1
      self.__swap_up(self.__size)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       int):
      while self.__size >= 2 * i:
        if 2 * i + 1 > self.__size:
          a		:						List[Any] 				=     2 * i
        else:
          if self.__heap[2 * i] > self.__heap[2 * i + 1]:
            a		:						Tuple 				=     2 * i
          else:
            a		:						str 				=     2 * i + 1
        a		:						Optional[int] 				=     self.__heap[i]
        if self.__heap[i] < self.__heap[bigger_child]:
          a		:						Any 				=     self.__heap[bigger_child]
          a		:						Union[str, Any] 				=     temporary
        a		:						Dict 				=     bigger_child






    def       __snake_case				(   self							:       List[str]):
      a		:						int 				=     self.__heap[1]
      a		:						Tuple 				=     self.__heap[self.__size]
      self.__size -= 1
      self.__heap.pop()
      self.__swap_down(1)
      return max_value






    @property
    def       __snake_case				(   self							:       List[str]):
      return self.__heap[1:]






    def __len__(   self							:       Optional[int]):
      return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 __lowercase          = BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(10)
 binary_heap.insert(15)
 binary_heap.insert(12)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))






