


"""simple docstring"""

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[
     """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 lowerCAmelCase_      (				UpperCamelCase__	: Tuple ):






 """simple docstring"""







 __lowercase							,		__lowercase									=	emb.weight.shape
 __lowercase									=	nn.Linear(UpperCamelCase__				,   UpperCamelCase__				,   bias=UpperCamelCase__ )
 __lowercase									=	emb.weight.data
 return lin_layer


def lowerCAmelCase_      (				UpperCamelCase__	: Any ):






 """simple docstring"""







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

 __lowercase									=	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"""				,   )

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

 return model


if __name__ == "__main__":
       UpperCAmelCase__  							=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.")
       UpperCAmelCase__  							=parser.parse_args()
       UpperCAmelCase__  							=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
       model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""

from collections.abc import Callable

import numpy as np


def lowerCAmelCase_      (				UpperCamelCase__	: Callable				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float ):






 """simple docstring"""







 __lowercase									=	int(np.ceil((x_end - xa) / step_size ) )
 __lowercase									=	np.zeros((n + 1,) )
 __lowercase									=	ya
 __lowercase									=	xa

 for k in range(UpperCamelCase__ ):
  __lowercase									=	y[k] + step_size * ode_func(UpperCamelCase__				,   y[k] )
  x += step_size

 return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()
