
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					[
        "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(__lowerCAmelCase     ,		__lowerCAmelCase				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    ,       snake_case__    =					emb.weight.shape
    snake_case__    =					nn.Linear(__lowerCAmelCase     ,		__lowerCAmelCase     ,		bias=__lowerCAmelCase				)
    snake_case__    =					emb.weight.data
    return lin_layer





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					torch.load(__lowerCAmelCase     ,		map_location="cpu"				)
    snake_case__    =					mam_aaa["args"] or mam_aaa["cfg"]["model"]
    snake_case__    =					mam_aaa["model"]
    remove_ignore_keys_(__lowerCAmelCase				)
    snake_case__    =					state_dict["encoder.embed_tokens.weight"].shape[0]

    snake_case__    =					MaMaaaConfig(
        vocab_size=__lowerCAmelCase     ,		max_position_embeddings=1_024     ,		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"     ,		)

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

    return model


if __name__ == "__main__":
     __magic_name__  							=	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.''')
     __magic_name__  							=	parser.parse_args()
     __magic_name__  							=	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 Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



