



'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 					snake_case			(UpperCamelCase		,   UpperCamelCase ):
       lowerCAmelCase__	:List[Any]     =       1



       @register_to_config
       def __init__(					self  ,UpperCAmelCase_=2_000  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=20  ,UpperCAmelCase_=1E-3   )	->				List[str]:
           lowercase__									=    None
           lowercase__									=    None
           lowercase__									=    None



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None   )	->				int:
           lowercase__									=    torch.linspace(1  ,self.config.sampling_eps  ,UpperCAmelCase_  ,device=UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None   )	->				List[Any]:
           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
           lowercase__									=    (
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           lowercase__									=    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
           lowercase__									=    std.flatten()
           while len(std.shape   ) < len(score.shape   ):
               lowercase__									=    std.unsqueeze(-1   )
           lowercase__									=    -score / std

           # compute
           lowercase__									=    -1.0 / len(self.timesteps   )

           lowercase__									=    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           lowercase__									=    beta_t.flatten()
           while len(beta_t.shape   ) < len(x.shape   ):
               lowercase__									=    beta_t.unsqueeze(-1   )
           lowercase__									=    -0.5 * beta_t * x

           lowercase__									=    torch.sqrt(UpperCAmelCase_   )
           lowercase__									=    drift - diffusion**2 * score
           lowercase__									=    x + drift * dt

           # add noise
           lowercase__									=    randn_tensor(x.shape  ,layout=x.layout  ,generator=UpperCAmelCase_  ,device=x.device  ,dtype=x.dtype   )
           lowercase__									=    x_mean + diffusion * math.sqrt(-dt   ) * noise

           return x, x_mean







       def __len__(					self   )	->				List[str]:
           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()




