

"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]				)							->				Tuple:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any				)							->				Optional[int]:





    '''simple docstring'''






    A__   				=       emb.weight.shape
    A__   				=       nn.Linear(UpperCAmelCase__   ,      UpperCAmelCase__   ,      bias=UpperCAmelCase__				)
    A__   				=       emb.weight.data
    return lin_layer
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]				)							->				Union[str, Any]:





    '''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=1024   ,      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__":
     lowercase_			=  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.")
     lowercase_			=  parser.parse_args()
     lowercase_			=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)




from __future__ import annotations

from typing import Any
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list				)							->				int:





    '''simple docstring'''






    if not postfix_notation:
        return 0

    A__   				=       {'+', '-', '*', '/'}
    A__   				=       []

    for token in postfix_notation:
        if token in operations:
            A__				,						A__   				=       stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(SCREAMING_SNAKE_CASE__				)				)

    return stack.pop()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

