



"""simple docstring"""

lowercase__			   =  "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__			   =  [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__			   =  {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

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





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

