


'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		int       =			[
     '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 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		List[Any]       =			emb.weight.shape
 __A  :		int       =			nn.Linear(UpperCamelCase__,				UpperCamelCase__,				bias=UpperCamelCase__			)
 __A  :		Dict       =			emb.weight.data
 return lin_layer


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			torch.load(UpperCamelCase__,				map_location='cpu'			)
 __A  :		Union[str, Any]       =			mam_aaa['args'] or mam_aaa['cfg']['model']
 __A  :		Any       =			mam_aaa['model']
 remove_ignore_keys_(UpperCamelCase__			)
 __A  :		List[str]       =			state_dict['encoder.embed_tokens.weight'].shape[0]

 __A  :		List[str]       =			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  :		Any       =			state_dict['decoder.embed_tokens.weight']
 __A  :		str       =			MaMaaaForConditionalGeneration(UpperCamelCase__			)
 model.model.load_state_dict(UpperCamelCase__,				strict=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			make_linear_from_emb(model.model.shared			)

 return model


if __name__ == "__main__":
 UpperCAmelCase_ :     Tuple								=      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_ :     Union[str, Any]								=      parser.parse_args()
 UpperCAmelCase_ :     Tuple								=      convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)










'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Optional[int]								=      {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Tuple    							=						'''vit_msn'''


 def __init__(      self      ,      __lowercase=768      ,      __lowercase=12      ,      __lowercase=12      ,      __lowercase=3_072      ,      __lowercase="gelu"      ,      __lowercase=0.0      ,      __lowercase=0.0      ,      __lowercase=0.0_2      ,      __lowercase=1E-06      ,      __lowercase=224      ,      __lowercase=16      ,      __lowercase=3      ,      __lowercase=True      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)

  __A  :		Tuple       =			hidden_size
  __A  :		Optional[int]       =			num_hidden_layers
  __A  :		str       =			num_attention_heads
  __A  :		Dict       =			intermediate_size
  __A  :		Optional[Any]       =			hidden_act
  __A  :		Union[str, Any]       =			hidden_dropout_prob
  __A  :		Optional[Any]       =			attention_probs_dropout_prob
  __A  :		List[str]       =			initializer_range
  __A  :		Dict       =			layer_norm_eps
  __A  :		List[Any]       =			image_size
  __A  :		Optional[int]       =			patch_size
  __A  :		List[Any]       =			num_channels
  __A  :		Optional[Any]       =			qkv_bias






