

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 	a (     A__      :  List[str]	)       -> Union[str, Any]:





      """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(A__ ,  A__	)
def 	a (     A__      :  int	)       -> List[Any]:





      """simple docstring"""



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





      """simple docstring"""



      _lowercase      			=torch.load(A__ ,  map_location='cpu'	)
      _lowercase      			=mam_aaa['args'] or mam_aaa['cfg']['model']
      _lowercase      			=mam_aaa['model']
      remove_ignore_keys_(A__	)
      _lowercase      			=state_dict['encoder.embed_tokens.weight'].shape[0]

      _lowercase      			=MaMaaaConfig(
          vocab_size=A__ ,  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(A__	)
      model.model.load_state_dict(A__ ,  strict=A__	)
      _lowercase      			=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)





import flax.linen as nn
import jax
import jax.numpy as jnp





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=hidden_states.shape
        _lowercase      			=jax.image.resize(
            lowerCAmelCase				,  shape=(batch, height * 2, width * 2, channels)				,  method='nearest'				,  )
        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(2, 2)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    None
  _a		     =    0.0
  _a		     =    None
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.in_channels if self.out_channels is None else self.out_channels

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=nn.Dense(lowerCAmelCase				,  dtype=self.dtype   )

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Dropout(self.dropout_prob   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        _lowercase      			=None
        if use_nin_shortcut:
              _lowercase      			=nn.Conv(
                  lowerCAmelCase				,  kernel_size=(1, 1)				,  strides=(1, 1)				,  padding='VALID'				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=True   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=hidden_states
        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        _lowercase      			=self.time_emb_proj(nn.swish(lowerCAmelCase   )   )
        _lowercase      			=jnp.expand_dims(jnp.expand_dims(lowerCAmelCase				,  1   )				,  1   )
        _lowercase      			=hidden_states + temb

        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.dropout(lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        if self.conv_shortcut is not None:
              _lowercase      			=self.conv_shortcut(lowerCAmelCase   )

        return hidden_states + residual


