



'''simple docstring'''
import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin






class 				lowercase__      (				lowercase		,   lowercase  ):
	lowercase__    							=     1





	@register_to_config
	def __init__(					self   :							int					,lowerCamelCase__   :							Dict=2000					,lowerCamelCase__   :							int=0.1					,lowerCamelCase__   :							Union[str, Any]=20					,lowerCamelCase__   :							int=1E-3					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  None
							_UpperCamelCase   :						Tuple												=  None
							_UpperCamelCase   :						List[str]												=  None





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Optional[Any] = None					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.linspace(1					,self.config.sampling_eps					,UpperCAmelCase_					,device=UpperCAmelCase_					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							int					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							Tuple=None					):







							'''simple docstring'''
							if self.timesteps is None:
													raise ValueError(
													    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							_UpperCamelCase   :						int												=  (
							    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							_UpperCamelCase   :						List[Any]												=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
							_UpperCamelCase   :						Optional[int]												=  std.flatten()
							while len(std.shape					) < len(score.shape					):
													_UpperCamelCase   :						Optional[int]												=  std.unsqueeze(-1					)
							_UpperCamelCase   :						Tuple												=  -score / std

							# compute
							_UpperCamelCase   :						str												=  -1.0 / len(self.timesteps					)

							_UpperCamelCase   :						Union[str, Any]												=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							_UpperCamelCase   :						Union[str, Any]												=  beta_t.flatten()
							while len(beta_t.shape					) < len(x.shape					):
													_UpperCamelCase   :						str												=  beta_t.unsqueeze(-1					)
							_UpperCamelCase   :						List[str]												=  -0.5 * beta_t * x

							_UpperCamelCase   :						Tuple												=  torch.sqrt(UpperCAmelCase_					)
							_UpperCamelCase   :						Optional[int]												=  drift - diffusion**2 * score
							_UpperCamelCase   :						Union[str, Any]												=  x + drift * dt

							# add noise
							_UpperCamelCase   :						Optional[int]												=  randn_tensor(x.shape					,layout=x.layout					,generator=UpperCAmelCase_					,device=x.device					,dtype=x.dtype					)
							_UpperCamelCase   :						str												=  x_mean + diffusion * math.sqrt(-dt					) * noise

							return x, x_mean





	def __len__(					self   :							Tuple					):







							'''simple docstring'''
							return self.config.num_train_timesteps







'''simple docstring'''





SCREAMING_SNAKE_CASE__          =  256
# Modulus to hash a string
SCREAMING_SNAKE_CASE__          =  100_0003




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    len(_snake_case							)
    lowercase__									=    len(_snake_case							)
    if p_len > t_len:
        return False

    lowercase__									=    0
    lowercase__									=    0
    lowercase__									=    1

    # Calculating the hash of pattern and substring of text
    for i in range(_snake_case							):
        lowercase__									=    (ord(pattern[i]							) + p_hash * alphabet_size) % modulus
        lowercase__									=    (ord(text[i]							) + text_hash * alphabet_size) % modulus
        if i == p_len - 1:
            continue
        lowercase__									=    (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
        lowercase__									=    (
            (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
            + ord(text[i + p_len]							)
        ) % modulus
    return False




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    "abc1abc12"
    lowercase__									=    "alskfjaldsabc1abc1abc12k23adsfabcabc"
    lowercase__									=    "alskfjaldsk23adsfabcabc"
    assert rabin_karp(_snake_case  ,_snake_case							) and not rabin_karp(_snake_case  ,_snake_case							)

    # Test 2)
    lowercase__									=    "ABABX"
    lowercase__									=    "ABABZABABYABABX"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 3)
    lowercase__									=    "AAAB"
    lowercase__									=    "ABAAAAAB"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 4)
    lowercase__									=    "abcdabcy"
    lowercase__									=    "abcxabcdabxabcdabcdabcy"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 5)
    lowercase__									=    "Lü"
    lowercase__									=    "Lüsai"
    assert rabin_karp(_snake_case  ,_snake_case							)
    lowercase__									=    "Lue"
    assert not rabin_karp(_snake_case  ,_snake_case							)
    print("Success."							)


if __name__ == "__main__":
 test_rabin_karp()




