



"""simple docstring"""





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


lowerCamelCase          =						logging.get_logger(__name__)

lowerCamelCase          =						{
    """google/pegasus-x-base""": """https://huggingface.co/google/pegasus-x-base/resolve/main/config.json""",
    """google/pegasus-x-large""": """https://huggingface.co/google/pegasus-x-large/resolve/main/config.json""",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}
class       lowercase__   (    UpperCamelCase__      ):







       '''simple docstring'''


       UpperCamelCase						=     '''pegasus_x'''
       UpperCamelCase						=     ['''past_key_values''']
       UpperCamelCase						=     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



       def __init__(							self			:  Optional[Any]		,				_UpperCAmelCase			:  Tuple=96103		,				_UpperCAmelCase			:  str=16384		,				_UpperCAmelCase			:  Optional[Any]=16		,				_UpperCAmelCase			:  Dict=4096		,				_UpperCAmelCase			:  Dict=16		,				_UpperCAmelCase			:  str=16		,				_UpperCAmelCase			:  List[str]=4096		,				_UpperCAmelCase			:  Optional[int]=16		,				_UpperCAmelCase			:  str=0.0		,				_UpperCAmelCase			:  Optional[int]=0.0		,				_UpperCAmelCase			:  Union[str, Any]=True		,				_UpperCAmelCase			:  int=True		,				_UpperCAmelCase			:  List[Any]="gelu"		,				_UpperCAmelCase			:  Union[str, Any]=1024		,				_UpperCAmelCase			:  List[str]=0.1		,				_UpperCAmelCase			:  int=0.0		,				_UpperCAmelCase			:  Dict=0.0		,				_UpperCAmelCase			:  List[str]=0.02		,				_UpperCAmelCase			:  Optional[int]=0		,				_UpperCAmelCase			:  List[str]=True		,				_UpperCAmelCase			:  str=0		,				_UpperCAmelCase			:  Tuple=1		,				_UpperCAmelCase			:  Any=1		,				_UpperCAmelCase			:  int=32		,				_UpperCAmelCase			:  Tuple=512		,				_UpperCAmelCase			:  Tuple=True		,				**_UpperCAmelCase			:  str		,				) ->						Any:


             '''simple docstring'''




             UpperCAmelCase_			 =							vocab_size
             UpperCAmelCase_			 =							max_position_embeddings
             UpperCAmelCase_			 =							d_model
             UpperCAmelCase_			 =							encoder_ffn_dim
             UpperCAmelCase_			 =							encoder_layers
             UpperCAmelCase_			 =							encoder_attention_heads
             UpperCAmelCase_			 =							decoder_ffn_dim
             UpperCAmelCase_			 =							decoder_layers
             UpperCAmelCase_			 =							decoder_attention_heads
             UpperCAmelCase_			 =							dropout
             UpperCAmelCase_			 =							attention_dropout
             UpperCAmelCase_			 =							activation_dropout
             UpperCAmelCase_			 =							activation_function
             UpperCAmelCase_			 =							init_std
             UpperCAmelCase_			 =							encoder_layerdrop
             UpperCAmelCase_			 =							decoder_layerdrop
             UpperCAmelCase_			 =							use_cache
             UpperCAmelCase_			 =							encoder_layers
             UpperCAmelCase_			 =							scale_embedding  # scale factor will be sqrt(d_model) if True

             UpperCAmelCase_			 =							num_global_tokens
             UpperCAmelCase_			 =							block_size
             UpperCAmelCase_			 =							stagger_local_blocks

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,				eos_token_id=SCREAMING_SNAKE_CASE__		,				is_encoder_decoder=SCREAMING_SNAKE_CASE__		,				decoder_start_token_id=SCREAMING_SNAKE_CASE__		,				forced_eos_token_id=SCREAMING_SNAKE_CASE__		,				**SCREAMING_SNAKE_CASE__		,				)



       @property
       def      lowercase__				(							self			:  List[str]    ) ->						int:


             '''simple docstring'''




             return self.encoder_attention_heads







       @property
       def      lowercase__				(							self			:  Union[str, Any]    ) ->						int:


             '''simple docstring'''




             return self.d_model










# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

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

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).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
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

         lowerCAmelCase__		= (image / 2 + 0.5).clamp(0   ,	1   )
         lowerCAmelCase__		= image.cpu().permute(0   ,	2   ,	3   ,	1   ).numpy()
         if output_type == "pil":
              lowerCAmelCase__		= self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

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

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   ), "This is a local test"
