
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   UpperCAmelCase		(						A_ ):
   A__					:		torch.FloatTensor
   A__					:		Optional[torch.FloatTensor]						     =     None



def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Any=0.999				,					__lowerCamelCase     : Optional[Any]="cosine"				,					):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(__lowerCamelCase     : List[str]    ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(__lowerCamelCase     : List[str]    ):
                     return math.exp(t * -12.0    )

       else:
              raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

       snake_case       :   Optional[int]							=      []
       for i in range(__lowerCamelCase    ):
              snake_case       :   Optional[int]							=      i / num_diffusion_timesteps
              snake_case       :   Tuple							=      (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(__lowerCamelCase    ) / alpha_bar_fn(__lowerCamelCase    )				,					__lowerCamelCase    )    )
       return torch.tensor(__lowerCamelCase				,					dtype=torch.floataa    )






class   UpperCAmelCase		(						A_       ,A_ ):
   A__					:		Union[str, Any]						     =     1



   @register_to_config
   def __init__(self		:					Optional[int]    ,						snake_case__		:					int = 10_00    ,						snake_case__		:					float = 0.0001    ,						snake_case__		:					float = 0.02    ,						snake_case__		:					str = "linear"    ,						snake_case__		:					Optional[Union[np.ndarray, List[float]]] = None    ,						snake_case__		:					bool = True    ,						snake_case__		:					bool = True    ,						snake_case__		:					int = 0    ,						snake_case__		:					str = "epsilon"    ,						snake_case__		:					float = 1.0    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Optional[Any]:


          '''simple docstring'''


          if kwargs.get("set_alpha_to_one"    ,						snake_case__ ) is not None:
                 snake_case       :   Dict							=      (
                     "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
                 )
                 deprecate("set_alpha_to_one"    ,						"1.0.0"    ,						snake_case__    ,						standard_warn=snake_case__ )
                 snake_case       :   Optional[Any]							=      kwargs["set_alpha_to_one"]
          if trained_betas is not None:
                 snake_case       :   Optional[Any]							=      torch.tensor(snake_case__    ,						dtype=torch.floataa )
          elif beta_schedule == "linear":
                 snake_case       :   List[str]							=      torch.linspace(snake_case__    ,						snake_case__    ,						snake_case__    ,						dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 snake_case       :   Tuple							=      (
                     torch.linspace(beta_start**0.5    ,						beta_end**0.5    ,						snake_case__    ,						dtype=torch.floataa ) ** 2
                 )
          elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 snake_case       :   Tuple							=      betas_for_alpha_bar(snake_case__ )
          else:
                 raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )

          snake_case       :   Tuple							=      1.0 - self.betas
          snake_case       :   int							=      torch.cumprod(self.alphas    ,						dim=0 )

          # At every step in inverted ddim, we are looking into the next alphas_cumprod
          # For the final step, there is no next alphas_cumprod, and the index is out of bounds
          # `set_alpha_to_zero` decides whether we set this parameter simply to zero
          # in this case, self.step() just output the predicted noise
          # or whether we use the final alpha of the "non-previous" one.
          snake_case       :   Any							=      torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]

          # standard deviation of the initial noise distribution
          snake_case       :   List[Any]							=      1.0

          # setable values
          snake_case       :   Optional[int]							=      None
          snake_case       :   List[Any]							=      torch.from_numpy(np.arange(0    ,						snake_case__ ).copy().astype(np.intaa ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					Optional[int] = None )		->	torch.FloatTensor:


          '''simple docstring'''


          return sample



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					int    ,						snake_case__		:					Union[str, torch.device] = None )		->	Any:


          '''simple docstring'''


          if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
                     f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
                     f""" maximal {self.config.num_train_timesteps} timesteps.""" )

          snake_case       :   Union[str, Any]							=      num_inference_steps
          snake_case       :   Union[str, Any]							=      self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          snake_case       :   Optional[Any]							=      (np.arange(0    ,						snake_case__ ) * step_ratio).round().copy().astype(np.intaa )
          snake_case       :   Optional[Any]							=      torch.from_numpy(snake_case__ ).to(snake_case__ )
          self.timesteps += self.config.steps_offset



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					int    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					float = 0.0    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[torch.FloatTensor] = None    ,						snake_case__		:					bool = True    ,						)		->	Union[DDIMSchedulerOutput, Tuple]:


          '''simple docstring'''


          snake_case       :   Tuple							=      timestep + self.config.num_train_timesteps // self.num_inference_steps

          # 2. compute alphas, betas
          # change original implementation to exactly match noise levels for analogous forward process
          snake_case       :   str							=      self.alphas_cumprod[timestep]
          snake_case       :   Union[str, Any]							=      (
              self.alphas_cumprod[prev_timestep]
              if prev_timestep < self.config.num_train_timesteps
              else self.final_alpha_cumprod
          )

          snake_case       :   Optional[int]							=      1 - alpha_prod_t

          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
                 snake_case       :   List[str]							=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 snake_case       :   Optional[int]							=      model_output
          elif self.config.prediction_type == "sample":
                 snake_case       :   Optional[Any]							=      model_output
                 snake_case       :   Optional[Any]							=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
                 snake_case       :   Dict							=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 snake_case       :   str							=      (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
                 raise ValueError(
                     f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                     " `v_prediction`" )

          # 4. Clip or threshold "predicted x_0"
          if self.config.clip_sample:
                 snake_case       :   Tuple							=      pred_original_sample.clamp(
                     -self.config.clip_sample_range    ,						self.config.clip_sample_range )

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          snake_case       :   Any							=      (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          snake_case       :   str							=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

          if not return_dict:
                 return (prev_sample, pred_original_sample)
          return DDIMSchedulerOutput(prev_sample=snake_case__    ,						pred_original_sample=snake_case__ )



   def __len__(self		:					Dict )		->	Optional[Any]:


          '''simple docstring'''


          return self.config.num_train_timesteps







def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int    ):
       snake_case       :   list[list[str]]							=      [[] for _ in range(__lowerCamelCase    )]
       snake_case       :   int							=      key - 1

       if key <= 0:
              raise ValueError("Height of grid can't be 0 or negative"    )
       if key == 1 or len(__lowerCamelCase    ) <= key:
              return input_string

       for position, character in enumerate(__lowerCamelCase    ):
              snake_case       :   Any							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Optional[int]							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              temp_grid[num].append(__lowerCamelCase    )
       snake_case       :   List[str]							=      ["".join(__lowerCamelCase    ) for row in temp_grid]
       snake_case       :   Tuple							=      "".join(__lowerCamelCase    )

       return output_string


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int    ):
       snake_case       :   Dict							=      []
       snake_case       :   Union[str, Any]							=      key - 1

       if key <= 0:
              raise ValueError("Height of grid can't be 0 or negative"    )
       if key == 1:
              return input_string

       snake_case       :   list[list[str]]							=      [[] for _ in range(__lowerCamelCase    )]  # generates template
       for position in range(len(__lowerCamelCase    )    ):
              snake_case       :   List[str]							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Optional[int]							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              temp_grid[num].append("*"    )

       snake_case       :   Tuple							=      0
       for row in temp_grid:  # fills in the characters
              snake_case       :   Union[str, Any]							=      input_string[counter : counter + len(__lowerCamelCase    )]
              grid.append(list(__lowerCamelCase    )    )
              counter += len(__lowerCamelCase    )

       snake_case       :   str							=      ""  # reads as zigzag
       for position in range(len(__lowerCamelCase    )    ):
              snake_case       :   Optional[int]							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Tuple							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              output_string += grid[num][0]
              grid[num].pop(0    )
       return output_string


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   Tuple							=      {}
       for key_guess in range(1				,					len(__lowerCamelCase    )    ):  # tries every key
              snake_case       :   Any							=      decrypt(__lowerCamelCase				,					__lowerCamelCase    )
       return results


if __name__ == "__main__":
   import doctest

   doctest.testmod()





