


"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[str]    =		42
     A_       :	Dict    =		42


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[int]:
          super().__init__()
          self.register_modules(unet=_A    ,				scheduler=_A							)





     @torch.no_grad()
     def __call__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 50    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = "pil"    ,				__UpperCAmelCase = True    ,				**__UpperCAmelCase    ,				)       ->							int:
          _a    	=				self.unet.config.sample_size
          _a    	=				(batch_size, 3, img_size, img_size)

          _a    	=				self.unet

          # sample x_0 ~ N(0, sigma_0^2 * I)
          _a    	=				randn_tensor(_A    ,				generator=_A    ,				device=self.device							) * self.scheduler.init_noise_sigma

          self.scheduler.set_timesteps(_A							)

          for t in self.progress_bar(self.scheduler.timesteps							):
               # here sigma_t == t_i from the paper
               _a    	=				self.scheduler.schedule[t]
               _a    	=				self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               _a    	=				self.scheduler.add_noise_to_input(_A    ,				_A    ,				generator=_A							)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               _a    	=				(sigma_hat / 2) * model((sample_hat + 1) / 2    ,				sigma_hat / 2							).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               _a    	=				self.scheduler.step(_A    ,				_A    ,				_A    ,				_A							)

               if sigma_prev != 0:
                    # 6. Apply 2nd order correction
                    # The model inputs and output are adjusted by following eq. (213) in [1].
                    _a    	=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,				sigma_prev / 2							).sample
                    _a    	=				self.scheduler.step_correct(
                        _A    ,				_A    ,				_A    ,				_A    ,				step_output.prev_sample    ,				step_output['''derivative''']    ,				)
               _a    	=				step_output.prev_sample

          _a    	=				(sample / 2 + 0.5).clamp(0    ,				1							)
          _a    	=				sample.cpu().permute(0    ,				2    ,				3    ,				1							).numpy()
          if output_type == "pil":
               _a    	=				self.numpy_to_pil(_A							)

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=_A							)



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
   parser.add_argument(
       '''--hf_config''',
       default='''facebook/mbart-large-cc25''',
       type=str,
       help='''Which huggingface architecture to use: mbart-large''',
   )
   parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)