

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					42
    UpperCAmelCase						 =					42

    def __init__(     self: Optional[int]    ,						UpperCamelCase: UNetaDModel    ,						UpperCamelCase: KarrasVeScheduler       ):






         """simple docstring"""


         super().__init__()
         self.register_modules(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )





    @torch.no_grad()
    def __call__(     self: Dict    ,						UpperCamelCase: int = 1    ,						UpperCamelCase: int = 50    ,						UpperCamelCase: Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						UpperCamelCase: Optional[str] = "pil"    ,						UpperCamelCase: bool = True    ,						**UpperCamelCase: Any    ,						):






         """simple docstring"""


         A__										=  self.unet.config.sample_size
         A__										=  (batch_size, 3, img_size, img_size)

         A__										=  self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         A__										=  randn_tensor(UpperCamelCase    ,						generator=UpperCamelCase    ,						device=self.device       ) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(UpperCamelCase       )

         for t in self.progress_bar(self.scheduler.timesteps       ):
              # here sigma_t == t_i from the paper
              A__										=  self.scheduler.schedule[t]
              A__										=  self.scheduler.schedule[t - 1] if t > 0 else 0

              # 1. Select temporarily increased noise level sigma_hat
              # 2. Add new noise to move from sample_i to sample_hat
              A__			,				A__										=  self.scheduler.add_noise_to_input(UpperCamelCase    ,						UpperCamelCase    ,						generator=UpperCamelCase       )

              # 3. Predict the noise residual given the noise magnitude `sigma_hat`
              # The model inputs and output are adjusted by following eq. (213) in [1].
              A__										=  (sigma_hat / 2) * model((sample_hat + 1) / 2    ,						sigma_hat / 2       ).sample

              # 4. Evaluate dx/dt at sigma_hat
              # 5. Take Euler step from sigma to sigma_prev
              A__										=  self.scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

              if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   A__										=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,						sigma_prev / 2       ).sample
                   A__										=  self.scheduler.step_correct(
                       UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						step_output.prev_sample    ,						step_output["""derivative"""]    ,						)
              A__										=  step_output.prev_sample

         A__										=  (sample / 2 + 0.5).clamp(0    ,						1       )
         A__										=  sample.cpu().permute(0    ,						2    ,						3    ,						1       ).numpy()
         if output_type == "pil":
              A__										=  self.numpy_to_pil(UpperCamelCase       )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=UpperCamelCase       )









"""simple docstring"""



from __future__ import annotations



class 					a						:



    """simple docstring"""



    def __init__(     self: Any    ,						UpperCamelCase: str    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__			,				A__										=  text, pattern
         A__			,				A__										=  len(UpperCamelCase       ), len(UpperCamelCase       )

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         for i in range(self.patLen - 1    ,						-1    ,						-1       ):
              if char == self.pattern[i]:
                   return i
         return -1

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: int       ):






         """simple docstring"""


         for i in range(self.patLen - 1    ,						-1    ,						-1       ):
              if self.pattern[i] != self.text[current_pos + i]:
                   return current_pos + i
         return -1





    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  []
         for i in range(self.textLen - self.patLen + 1       ):
              A__										=  self.mismatch_in_text(UpperCamelCase       )
              if mismatch_index == -1:
                   positions.append(UpperCamelCase       )
              else:
                   A__										=  self.match_in_pattern(self.text[mismatch_index]       )
                   A__										=  (
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
         return positions

SCREAMING_SNAKE_CASE_					:	List[Any]							  =						'ABAABA'
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						'AB'
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE_					:	int							  =						bms.bad_character_heuristic()

if len(positions) == 0:
 print('No match found')
else:
 print('Pattern found in following positions: ')
 print(positions)






