




import numpy as np

__UpperCamelCase						:       Optional[Any]							=    [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]

class      UpperCAmelCase						:






   def __init__(	self   :       Tuple     )->			Tuple:
     _lowercase: Optional[Any]						     =    np.array(_lowerCAmelCase     )






   def  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       str     )->			str:
     _lowercase     ,					_lowercase: int						     =    np.where(letter == self.SQUARE     )
     _lowercase: Any						     =    np.concatenate([indexa + 1, indexa + 1]     )
     return indexes






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       int     )->			Dict:
     return self.SQUARE[indexa - 1, indexa - 1]






   def  _a						(	self   :       Dict						,					UpperCamelCase_   :       str     )->			Dict:
     _lowercase: Tuple						     =    message.lower()
     _lowercase: List[str]						     =    message.replace('j'						,					'i'     )

     _lowercase: List[str]						     =    ''
     for letter_index in range(len(_lowerCAmelCase     )     ):
       if message[letter_index] != " ":
         _lowercase: List[Any]						     =    self.letter_to_numbers(message[letter_index]     )
         _lowercase: Optional[int]						     =    encoded_message + str(numbers[0]     ) + str(numbers[1]     )
       elif message[letter_index] == " ":
         _lowercase: Union[str, Any]						     =    encoded_message + ' '

     return encoded_message






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       str     )->			int:
     _lowercase: List[str]						     =    message.replace(' '						,					'  '     )
     _lowercase: Tuple						     =    ''
     for numbers_index in range(int(len(_lowerCAmelCase     ) / 2     )     ):
       if message[numbers_index * 2] != " ":
         _lowercase: List[Any]						     =    message[numbers_index * 2]
         _lowercase: str						     =    message[numbers_index * 2 + 1]

         _lowercase: Union[str, Any]						     =    self.numbers_to_letter(int(_lowerCAmelCase     )						,					int(_lowerCAmelCase     )     )
         _lowercase: str						     =    decoded_message + letter
       elif message[numbers_index * 2] == " ":
         _lowercase: Tuple						     =    decoded_message + ' '

     return decoded_message






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    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
       _lowercase: int						     =    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
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    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"
