


"""simple docstring"""



from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class        __magic_name__		(      a_  ):






       def __init__( self	:     Union[str, Any]	,  snake_case__	:     str	,  snake_case__	:     Optional[int]	,  snake_case__	:     Union[str, Any]	,  snake_case__	:     str = None	,  ):

              '''simple docstring'''






              super().__init__()
              self.register_modules(transformer=__UpperCamelCase	,  vae=__UpperCamelCase	,  scheduler=__UpperCamelCase					)

              # create a imagenet -> id dictionary for easier use
              lowercase		:Union[str, Any]											=					{}
              if idalabel is not None:
                     for key, value in idalabel.items():
                            for label in value.split(''','''					):
                                   lowercase		:List[Any]											=					int(__UpperCamelCase					)
                     lowercase		:Optional[Any]											=					dict(sorted(self.labels.items()					)					)






       def 			__snake_case    ( self	:     Dict	,  snake_case__	:     str					):

              '''simple docstring'''






              if not isinstance(__UpperCamelCase	,  __UpperCamelCase					):
                     lowercase		:Optional[Any]											=					list(__UpperCamelCase					)

              for l in label:
                     if l not in self.labels:
                            raise ValueError(
                                f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""					)

              return [self.labels[l] for l in label]







       @torch.no_grad()
       def __call__( self	:     int	,  snake_case__	:     Optional[Any]	,  snake_case__	:     Optional[Any] = 4.0	,  snake_case__	:     Optional[int] = None	,  snake_case__	:     Dict = 5_0	,  snake_case__	:     int = "pil"	,  snake_case__	:     Union[str, Any] = True	,  ):

              '''simple docstring'''






              lowercase		:Any											=					len(__UpperCamelCase					)
              lowercase		:Any											=					self.transformer.config.sample_size
              lowercase		:str											=					self.transformer.config.in_channels

              lowercase		:int											=					randn_tensor(
                  shape=(batch_size, latent_channels, latent_size, latent_size)	,  generator=__UpperCamelCase	,  device=self.device	,  dtype=self.transformer.dtype	,  )
              lowercase		:Optional[Any]											=					torch.cat([latents] * 2					) if guidance_scale > 1 else latents

              lowercase		:Union[str, Any]											=					torch.tensor(__UpperCamelCase	,  device=self.device					).reshape(-1					)
              lowercase		:Optional[int]											=					torch.tensor([1_0_0_0] * batch_size	,  device=self.device					)
              lowercase		:str											=					torch.cat([class_labels, class_null]	,  0					) if guidance_scale > 1 else class_labels

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

              for t in self.progress_bar(self.scheduler.timesteps					):
                     if guidance_scale > 1:
                            lowercase		:Tuple											=					latent_model_input[: len(__UpperCamelCase					) // 2]
                            lowercase		:Any											=					torch.cat([half, half]	,  dim=0					)
                     lowercase		:Dict											=					self.scheduler.scale_model_input(__UpperCamelCase	,  __UpperCamelCase					)

                     lowercase		:List[Any]											=					t
                     if not torch.is_tensor(__UpperCamelCase					):
                            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                            # This would be a good case for the `match` statement (Python 3.10+)
                            lowercase		:Dict											=					latent_model_input.device.type == '''mps'''
                            if isinstance(__UpperCamelCase	,  __UpperCamelCase					):
                                   lowercase		:Dict											=					torch.floataa if is_mps else torch.floataa
                            else:
                                   lowercase		:Tuple											=					torch.intaa if is_mps else torch.intaa
                            lowercase		:List[Any]											=					torch.tensor([timesteps]	,  dtype=__UpperCamelCase	,  device=latent_model_input.device					)
                     elif len(timesteps.shape					) == 0:
                            lowercase		:Union[str, Any]											=					timesteps[None].to(latent_model_input.device					)
                     # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                     lowercase		:Union[str, Any]											=					timesteps.expand(latent_model_input.shape[0]					)
                     # predict noise model_output
                     lowercase		:Optional[int]											=					self.transformer(
                         __UpperCamelCase	,  timestep=__UpperCamelCase	,  class_labels=__UpperCamelCase					).sample

                     # perform guidance
                     if guidance_scale > 1:
                            lowercase				,	lowercase		:List[str]											=					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                            lowercase				,	lowercase		:Any											=					torch.split(__UpperCamelCase	,  len(__UpperCamelCase					) // 2	,  dim=0					)

                            lowercase		:Union[str, Any]											=					uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                            lowercase		:Optional[int]											=					torch.cat([half_eps, half_eps]	,  dim=0					)

                            lowercase		:Any											=					torch.cat([eps, rest]	,  dim=1					)

                     # learned sigma
                     if self.transformer.config.out_channels // 2 == latent_channels:
                            lowercase				,	lowercase		:str											=					torch.split(__UpperCamelCase	,  __UpperCamelCase	,  dim=1					)
                     else:
                            lowercase		:int											=					noise_pred

                     # compute previous image: x_t -> x_t-1
                     lowercase		:int											=					self.scheduler.step(__UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase					).prev_sample

              if guidance_scale > 1:
                     lowercase				,	lowercase		:int											=					latent_model_input.chunk(2	,  dim=0					)
              else:
                     lowercase		:List[Any]											=					latent_model_input

              lowercase		:List[Any]											=					1 / self.vae.config.scaling_factor * latents
              lowercase		:Any											=					self.vae.decode(__UpperCamelCase					).sample

              lowercase		:Union[str, Any]											=					(samples / 2 + 0.5).clamp(0	,  1					)

              # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
              lowercase		:Dict											=					samples.cpu().permute(0	,  2	,  3	,  1					).float().numpy()

              if output_type == "pil":
                     lowercase		:Dict											=					self.numpy_to_pil(__UpperCamelCase					)

              if not return_dict:
                     return (samples,)

              return ImagePipelineOutput(images=__UpperCamelCase					)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase      :       Optional[int]    =						logging.get_logger(__name__)

lowerCamelCase      :       Optional[Any]    =						{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				"""pegasus"""
      snake_case_	=				["""past_key_values"""]
      snake_case_	=				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


      def __init__(						self       ,    __UpperCamelCase=5_0_2_6_5       ,    __UpperCamelCase=1_0_2_4       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=4_0_9_6       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=4_0_9_6       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=1_0_2_4       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase=0       ,    __UpperCamelCase=False       ,    __UpperCamelCase=0       ,    __UpperCamelCase=1       ,    __UpperCamelCase=1       ,    **__UpperCamelCase       ,    )->						Tuple:
           __lowerCAmelCase             =       vocab_size
           __lowerCAmelCase             =       max_position_embeddings
           __lowerCAmelCase             =       d_model
           __lowerCAmelCase             =       encoder_ffn_dim
           __lowerCAmelCase             =       encoder_layers
           __lowerCAmelCase             =       encoder_attention_heads
           __lowerCAmelCase             =       decoder_ffn_dim
           __lowerCAmelCase             =       decoder_layers
           __lowerCAmelCase             =       decoder_attention_heads
           __lowerCAmelCase             =       dropout
           __lowerCAmelCase             =       attention_dropout
           __lowerCAmelCase             =       activation_dropout
           __lowerCAmelCase             =       activation_function
           __lowerCAmelCase             =       init_std
           __lowerCAmelCase             =       encoder_layerdrop
           __lowerCAmelCase             =       decoder_layerdrop
           __lowerCAmelCase             =       use_cache
           __lowerCAmelCase             =       encoder_layers
           __lowerCAmelCase             =       scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=__UpperCamelCase       ,    eos_token_id=__UpperCamelCase       ,    is_encoder_decoder=__UpperCamelCase       ,    decoder_start_token_id=__UpperCamelCase       ,    forced_eos_token_id=__UpperCamelCase       ,    **__UpperCamelCase       ,    )


      @property
      def 			__UpperCAmelCase		(						self						)->						int:
           return self.encoder_attention_heads


      @property
      def 			__UpperCAmelCase		(						self						)->						int:
           return self.d_model






