





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class      lowerCAmelCase							(      __UpperCamelCase    ):
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   int			)    ->			str:
     super().__init__()
     self.register_modules(unet=UpperCAmelCase    ,  scheduler=UpperCAmelCase			)







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

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

     for t in self.progress_bar(self.scheduler.timesteps			):
         # 1. predict noise model_output
         lowerCamelCase__					:   Any      				=	self.unet(UpperCAmelCase    ,  UpperCAmelCase			).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__					:   Tuple      				=	self.scheduler.step(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			).prev_sample

     lowerCamelCase__					:   List[str]      				=	(image / 2 + 0.5).clamp(0    ,  1			)
     lowerCamelCase__					:   List[Any]      				=	image.cpu().permute(0    ,  2    ,  3    ,  1			).numpy()
     if output_type == "pil":
         lowerCamelCase__					:   Optional[int]      				=	self.numpy_to_pil(UpperCAmelCase			)

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

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












import enum
import shutil
import sys


_UpperCAmelCase     ,_UpperCAmelCase       : List[Any]			     =       shutil.get_terminal_size()

_UpperCAmelCase       : Union[str, Any]			     =       {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}

class      lowerCAmelCase							(      enum.Enum    ):
 UpperCAmelCase__									=	0
 UpperCAmelCase__									=	1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  int:
    sys.stdout.write(str(_UpperCAmelCase				) + end				)
    sys.stdout.flush()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  Union[str, Any]:
    forceWrite(F"""\u001b[{color}m{content}\u001b[0m"""		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    forceWrite('\r'				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""				)


def  SCREAMING_SNAKE_CASE   ( )    ->  int:
    forceWrite(' ' * TERMINAL_WIDTH				)
    reset_cursor()


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    reset_cursor()
    forceWrite('-' * TERMINAL_WIDTH				)





