



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 a						=		[
     "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(UpperCAmelCase__ ,					UpperCAmelCase__					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int]					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		torch.load(UpperCAmelCase__ ,					map_location="cpu"					)
 a						=		mam_aaa["args"] or mam_aaa["cfg"]["model"]
 a						=		mam_aaa["model"]
 remove_ignore_keys_(UpperCAmelCase__					)
 a						=		state_dict["encoder.embed_tokens.weight"].shape[0]

 a						=		MaMaaaConfig(
     vocab_size=UpperCAmelCase__ ,					max_position_embeddings=10_24 ,					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" ,					)

 a						=		state_dict["decoder.embed_tokens.weight"]
 a						=		MaMaaaForConditionalGeneration(UpperCAmelCase__					)
 model.model.load_state_dict(UpperCAmelCase__ ,					strict=UpperCAmelCase__					)
 a						=		make_linear_from_emb(model.model.shared					)

 return model


if __name__ == "__main__":
     A_		:     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.''')
     A_		:     Dict        =     parser.parse_args()
     A_		:     Optional[int]        =     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






from __future__ import annotations

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

A_		:     List[str]        =     (3, 9, -11, 0, 7, 5, 1, -1)
A_		:     Optional[int]        =     (4, 6, 2, 0, 8, 10, 3, -2)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42



class 		_lowercase		:

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Iterable[int]							)       ->				None:


    """simple docstring"""






    a						=		None
    for i in sorted(__lowerCAmelCase							,   reverse=__lowerCAmelCase							):
     a						=		Node(__lowerCAmelCase							,   self.head							)

   def __iter__(      self			:       Union[str, Any]							)       ->				Iterator[int]:


    """simple docstring"""






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

   def __len__(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    return sum(1 for _ in self							)




   def __str__(      self			:       Union[str, Any]							)       ->				str:


    """simple docstring"""






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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :SortedLinkedList ,					UpperCAmelCase__ :SortedLinkedList					):






 '''simple docstring'''

 return SortedLinkedList(list(UpperCAmelCase__					) + list(UpperCAmelCase__					)					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     A_		:     Optional[Any]        =     SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

