
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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



@dataclass
class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							torch.FloatTensor
   _SCREAMING_SNAKE_CASE      :							torch.FloatTensor
   _SCREAMING_SNAKE_CASE      :							Optional[torch.FloatTensor]	       =      None





class 		UpperCamelCase__  (       __lowercase    ,__lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      2
   @register_to_config
   def __init__(self :					Optional[int]					,				snake_case_ :					float = 0.02					,				snake_case_ :					float = 1_0_0					,				snake_case_ :					float = 1.007					,				snake_case_ :					float = 8_0					,				snake_case_ :					float = 0.05					,				snake_case_ :					float = 5_0					,				):
    # standard deviation of the initial noise distribution
    __a :  List[str]						=  sigma_max

    # setable values
    __a :  int						=  None
    __a :  np.IntTensor						=  None
    __a :  torch.FloatTensor						=  None  # sigma(t_i)
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					Optional[int] = None   ):
    return sample
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					int					,				snake_case_ :					Union[str, torch.device] = None   ):
    __a :  Tuple						=  num_inference_steps
    __a :  Optional[int]						=  np.arange(0					,				self.num_inference_steps   )[::-1].copy()
    __a :  str						=  torch.from_numpy(snake_case_   ).to(snake_case_   )
    __a :  List[Any]						=  [
        (
            self.config.sigma_max**2
            * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
        )
        for i in self.timesteps
    ]
    __a :  Optional[Any]						=  torch.tensor(snake_case_					,				dtype=torch.floataa					,				device=snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					float					,				snake_case_ :					Optional[torch.Generator] = None   ):
    if self.config.s_min <= sigma <= self.config.s_max:
     __a :  List[Any]						=  min(self.config.s_churn / self.num_inference_steps					,				2**0.5 - 1   )
    else:
     __a :  Optional[int]						=  0

    # sample eps ~ N(0, S_noise^2 * I)
    __a :  str						=  self.config.s_noise * randn_tensor(sample.shape					,				generator=snake_case_   ).to(sample.device   )
    __a :  int						=  sigma + gamma * sigma
    __a :  Dict						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

    return sample_hat, sigma_hat
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					float					,				snake_case_ :					float					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					bool = True					,				):
    __a :  Dict						=  sample_hat + sigma_hat * model_output
    __a :  Optional[int]						=  (sample_hat - pred_original_sample) / sigma_hat
    __a :  Optional[Any]						=  sample_hat + (sigma_prev - sigma_hat) * derivative

    if not return_dict:
     return (sample_prev, derivative)

    return KarrasVeOutput(
        prev_sample=snake_case_					,				derivative=snake_case_					,				pred_original_sample=snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					float					,				snake_case_ :					float					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					torch.FloatTensor					,				snake_case_ :					bool = True					,				):
    __a :  Optional[Any]						=  sample_prev + sigma_prev * model_output
    __a :  Union[str, Any]						=  (sample_prev - pred_original_sample) / sigma_prev
    __a :  Optional[int]						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

    if not return_dict:
     return (sample_prev, derivative)

    return KarrasVeOutput(
        prev_sample=snake_case_					,				derivative=snake_case_					,				pred_original_sample=snake_case_   )







   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Tuple					,				snake_case_ :					str					,				snake_case_ :					Tuple   ):
    raise NotImplementedError()


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 if n_term == "":
  return []
 __a :  list						=  []
 for temp in range(int(lowerCAmelCase__  )  ):
  series.append(f"1/{temp + 1}" if series else '''1'''  )
 return series


if __name__ == "__main__":
      lowercase__       =input('Enter the last number (nth term) of the Harmonic Series')
      print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
      print(harmonic_series(nth_term))
