




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class  __magic_name__  (		__A    ):





   """simple docstring"""




   def __init__(	self					:  Optional[int]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    super().__init__()
    self.register_modules(unet=_lowercase		,  scheduler=_lowercase							)



   @torch.no_grad()
   def __call__(	self					:  int		,  _lowercase					:  Optional[int] = 1		,  _lowercase					:  Tuple = None		,  _lowercase					:  Tuple = 50		,  _lowercase					:  List[Any] = "pil"		,  _lowercase					:  List[str] = True		,  **_lowercase					:  List[Any]		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,  generator=_lowercase		,  )
    _UpperCamelCase:     Tuple							= image.to(self.device							)

    # set step values
    self.scheduler.set_timesteps(_lowercase							)

    for t in self.progress_bar(self.scheduler.timesteps							):
     # 1. predict noise model_output
     _UpperCamelCase:     str							= self.unet(_lowercase		,  _lowercase							).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     _UpperCamelCase:     Dict							= self.scheduler.step(_lowercase		,  _lowercase		,  _lowercase							).prev_sample

    _UpperCamelCase:     Optional[int]							= (image / 2 + 0.5).clamp(0		,  1							)
    _UpperCamelCase:     List[Any]							= image.cpu().permute(0		,  2		,  3		,  1							).numpy()
    if output_type == "pil":
     _UpperCamelCase:     List[Any]							= self.numpy_to_pil(_lowercase							)

    if not return_dict:
     return (image,), "This is a local test"

    return ImagePipelineOutput(images=_lowercase							), "This is a local test"
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= [
     '''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(lowercase  ,					lowercase		)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= emb.weight.shape
 _UpperCamelCase:     int							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Any							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							Optional[Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     Any							= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 _UpperCamelCase:     List[Any]							= mam_aaa['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Tuple							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     List[Any]							= MaMaaaConfig(
     vocab_size=lowercase  ,					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'''  ,					)

 _UpperCamelCase:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MaMaaaForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 _UpperCamelCase:     Optional[Any]							= 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)