

A_       : Optional[Any]         =			256
# Modulus to hash a string
A_       : List[Any]         =			100_0003

def 						UpperCamelCase					(lowercase_: Optional[Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	A__							:    int        =		len(SCREAMING_SNAKE_CASE__					)
	A__							:    Union[str, Any]        =		len(SCREAMING_SNAKE_CASE__					)
	if p_len > t_len:
		return False

	A__							:    Tuple        =		0
	A__							:    Union[str, Any]        =		0
	A__							:    Union[str, Any]        =		1

	# Calculating the hash of pattern and substring of text
	for i in range(SCREAMING_SNAKE_CASE__					):
		A__							:    Union[str, Any]        =		(ord(pattern[i]					) + p_hash * alphabet_size) % modulus
		A__							:    str        =		(ord(text[i]					) + text_hash * alphabet_size) % modulus
		if i == p_len - 1:
			continue
		A__							:    List[str]        =		(modulus_power * alphabet_size) % modulus

	for i in range(0    ,      t_len - p_len + 1					):
		if text_hash == p_hash and text[i : i + p_len] == pattern:
			return True
		if i == t_len - p_len:
			continue
		# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
		A__							:    str        =		(
		    (text_hash - ord(text[i]					) * modulus_power) * alphabet_size
		    + ord(text[i + p_len]					)
		) % modulus
	return False

def 						UpperCamelCase					()     ->      List[Any]:
	A__							:    Tuple        =		"""abc1abc12"""
	A__							:    Any        =		"""alskfjaldsabc1abc1abc12k23adsfabcabc"""
	A__							:    Union[str, Any]        =		"""alskfjaldsk23adsfabcabc"""
	assert rabin_karp(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__					) and not rabin_karp(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__					)

	# Test 2)
	A__							:    Dict        =		"""ABABX"""
	A__							:    Optional[int]        =		"""ABABZABABYABABX"""
	assert rabin_karp(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__					)

	# Test 3)
	A__							:    Optional[int]        =		"""AAAB"""
	A__							:    Union[str, Any]        =		"""ABAAAAAB"""
	assert rabin_karp(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__					)

	# Test 4)
	A__							:    Optional[int]        =		"""abcdabcy"""
	A__							:    Any        =		"""abcxabcdabxabcdabcdabcy"""
	assert rabin_karp(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__					)

	# Test 5)
	A__							:    List[str]        =		"""Lü"""
	A__							:    Optional[int]        =		"""Lüsai"""
	assert rabin_karp(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__					)
	A__							:    Any        =		"""Lue"""
	assert not rabin_karp(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__					)
	print("""Success."""					)


if __name__ == "__main__":
	test_rabin_karp()







'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




