


"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def 						__a      (  a					):





       """simple docstring"""







       _a          =				[
           "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 						__a      (  a					):





       """simple docstring"""







       _a   , _a          =				emb.weight.shape
       _a          =				nn.Linear(a,		a,		bias=a					)
       _a          =				emb.weight.data
       return lin_layer



def 						__a      (  a,		a="uclanlp/plbart-base",		a=False,		a=False					):





       """simple docstring"""







       _a          =				torch.load(a,		map_location="cpu"					)["model"]
       remove_ignore_keys_(a					)
       _a          =				state_dict["encoder.embed_tokens.weight"].shape[0]

       _a          =				PLBartConfig.from_pretrained(a,		vocab_size=a					)

       _a          =				state_dict["decoder.embed_tokens.weight"]
       if not classification:
              _a          =				PLBartForConditionalGeneration(a					)
              model.model.load_state_dict(a					)
              if finetuned:
                     _a          =				make_linear_from_emb(model.model.shared					)

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

       return model


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

__SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class __snake_case		:







   """simple docstring"""




   def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
          _a          =				-1
          _a          =				-1
          _a          =				[-1] * length
          _a          =				None
          _a          =				None




   def __repr__(		self							:Optional[Any]       ):
          return f'Node(min_value={self.minn} max_value={self.maxx})'






def 						__a      (  a					):





       """simple docstring"""







       _a          =				Node(len(a					)					)
       _a   , _a          =				min(a					), max(a					)
       # Leaf node case where the node contains only one unique value
       if root.minn == root.maxx:
              return root
       _a          =				(root.minn + root.maxx) // 2

       _a          =				[]
       _a          =				[]

       for index, num in enumerate(a					):
              if num <= pivot:
                     left_arr.append(a					)
              else:
                     right_arr.append(a					)
              _a          =				len(a					)
       _a          =				build_tree(a					)
       _a          =				build_tree(a					)
       return root



def 						__a      (  a,		a,		a					):





       """simple docstring"""







       if index < 0 or node is None:
              return 0
       # Leaf node cases
       if node.minn == node.maxx:
              return index + 1 if node.minn == num else 0
       _a          =				(node.minn + node.maxx) // 2
       if num <= pivot:
              # go the left subtree and map index to the left subtree
              return rank_till_index(node.left,		a,		node.map_left[index] - 1					)
       else:
              # go to the right subtree and map index to the right subtree
              return rank_till_index(node.right,		a,		index - node.map_left[index]					)



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       if start > end:
              return 0
       _a          =				rank_till_index(a,		a,		a					)
       _a          =				rank_till_index(a,		a,		start - 1					)
       return rank_till_end - rank_before_start



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       if index > (end - start) or start > end or node is None:
              return -1
       # Leaf node case
       if node.minn == node.maxx:
              return node.minn
       # Number of elements in the left subtree in interval [start, end]
       _a          =				node.map_left[end] - (
           node.map_left[start - 1] if start else 0
       )
       if num_elements_in_left_tree > index:
              return quantile(
                  node.left,		a,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		)
       else:
              return quantile(
                  node.right,		index - num_elements_in_left_tree,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		)



def 						__a      (  a,		a,		a,		a,		a					):





       """simple docstring"""







       if (
           start > end
           or node is None
           or start_num > end_num
           or node.minn > end_num
           or node.maxx < start_num
       ):
              return 0
       if start_num <= node.minn and node.maxx <= end_num:
              return end - start + 1
       _a          =				range_counting(
           node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
       _a          =				range_counting(
           node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
       return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()
