code: 
  
  
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import PNDMScheduler
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  class 		_snake_case      (			_SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  	lowerCamelCase_          =			42
  	lowerCamelCase_          =			42
  	def __init__(						self	,    a	,    a					)	->							str:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			super().__init__()
  
  			_A           =    PNDMScheduler.from_config(scheduler.config					)
  
  			self.register_modules(unet=UpperCamelCase__	,    scheduler=UpperCamelCase__					)
  
  
  
  	@torch.no_grad()
  	def __call__(						self	,    a = 1	,    a = 5_0	,    a = None	,    a = "pil"	,    a = True	,    **a	,    )	->							Dict:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			_A           =    randn_tensor(
  			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,    generator=UpperCamelCase__	,    device=self.device	,    )
  
  			self.scheduler.set_timesteps(UpperCamelCase__					)
  			for t in self.progress_bar(self.scheduler.timesteps					):
  					_A           =    self.unet(UpperCamelCase__	,    UpperCamelCase__					).sample
  
  					_A           =    self.scheduler.step(UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__					).prev_sample
  
  			_A           =    (image / 2 + 0.5).clamp(0	,    1					)
  			_A           =    image.cpu().permute(0	,    2	,    3	,    1					).numpy()
  			if output_type == "pil":
  					_A           =    self.numpy_to_pil(UpperCamelCase__					)
  
  			if not return_dict:
  					return (image,)
  
  			return ImagePipelineOutput(images=UpperCamelCase__					)
style_context: 
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{}
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt_decoder'''
   def __init__(						self	,    a=0	,    a=0					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
     _A           =    vocab_size
     _A           =    bos_token_id
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt'''
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    langs
     _A           =    src_vocab_size
     _A           =    tgt_vocab_size
     _A           =    d_model  # encoder_embed_dim and decoder_embed_dim
  
     _A           =    encoder_ffn_dim
     _A           =    _A           =    encoder_layers
     _A           =    encoder_attention_heads
     _A           =    encoder_layerdrop
     _A           =    decoder_layerdrop
     _A           =    decoder_ffn_dim
     _A           =    decoder_layers
     _A           =    decoder_attention_heads
     _A           =    max_position_embeddings
     _A           =    init_std  # Normal(0, this parameter)
     _A           =    activation_function
  
     _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
     if "decoder" in common_kwargs:
       del common_kwargs["decoder"]
  
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
     # 3 Types of Dropout
     _A           =    attention_dropout
     _A           =    activation_dropout
     _A           =    dropout
  
     _A           =    use_cache
     super().__init__(
         pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    copy.deepcopy(self.__dict__					)
     _A           =    self.decoder.to_dict()
     _A           =    self.__class__.model_type
     return output