






"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowerCAmelCase_						:     Dict							  =   (3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCAmelCase_						:     Optional[int]							  =   (4, 6, 2, 0, 8, 1_0, 3, -2)





@dataclass
class 	UpperCamelCase_			:
  _A							:       List[Any]					=						42
  _A							:       str					=						42





class 	UpperCamelCase_			:



  def __init__(   self			,		snake_case__					)      ->      Optional[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      None
         for i in sorted(lowerCAmelCase__			,		reverse=lowerCAmelCase__					):
                UpperCAmelCase						     =      Node(lowerCAmelCase__			,		self.head					)



  def __iter__(   self					)      ->      str:



         """simple docstring"""





         UpperCAmelCase						     =      self.head
         while node:
                yield node.data
                UpperCAmelCase						     =      node.next_node



  def __len__(   self					)      ->      Any:



         """simple docstring"""





         return sum(1 for _ in self					)



  def __str__(   self					)      ->      Optional[int]:



         """simple docstring"""





         return " -> ".join([str(lowerCAmelCase__					) for node in self]					)






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       return SortedLinkedList(list(A__		) + list(A__		)		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCAmelCase_						:     Dict							  =   SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					[
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'encoder.embed_positions._float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(A__,      A__				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					emb.weight.shape
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(A__,      A__,      bias=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					emb.weight.data
			return lin_layer






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.load(A__,      map_location='cpu'				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					mam_aaa['args'] or mam_aaa['cfg']['model']
			SCREAMING_SNAKE_CASE_		:     Any					=					mam_aaa['model']
			remove_ignore_keys_(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict['encoder.embed_tokens.weight'].shape[0]

			SCREAMING_SNAKE_CASE_		:     Dict					=					MaMaaaConfig(
			    vocab_size=A__,      max_position_embeddings=1_0_2_4,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      encoder_layerdrop=args.encoder_layerdrop,      decoder_layerdrop=args.decoder_layerdrop,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='relu',      )

			SCREAMING_SNAKE_CASE_		:     int					=					state_dict['decoder.embed_tokens.weight']
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MaMaaaForConditionalGeneration(A__				)
			model.model.load_state_dict(A__,      strict=A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[int]								=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				lowerCAmelCase__			:      Optional[Any]								=parser.parse_args()
				lowerCAmelCase__			:      int								=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)





