
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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



def 		lowercase_     (  A__					,						A__=0.999					,						A__="cosine"					,						)				->				List[str]:
    """simple docstring"""

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(A__						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(A__						):
            return math.exp(t * -12.0						)

    else:
        raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'						)

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







class 	lowerCamelCase						(    _lowerCamelCase				,					_lowerCamelCase    ):
     UpperCAmelCase__							:				List[str] 	=						[e.name for e in KarrasDiffusionSchedulers]
     UpperCAmelCase__							:				Union[str, Any] 	=						2





     @register_to_config
     def __init__(self :       Optional[int]							,       _A :       int = 1_0_0_0							,       _A :       float = 0.0_00_85							,       _A :       float = 0.0_12							,       _A :       str = "linear"							,       _A :       Optional[Union[np.ndarray, List[float]]] = None							,       _A :       str = "epsilon"							,       _A :       str = "linspace"							,       _A :       int = 0							,       )		->		Tuple:


         if trained_betas is not None:
             snake_case						=   torch.tensor(_A							,       dtype=torch.floataa  )
         elif beta_schedule == "linear":
             snake_case						=   torch.linspace(_A							,       _A							,       _A							,       dtype=torch.floataa  )
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             snake_case						=   (
                 torch.linspace(beta_start**0.5							,       beta_end**0.5							,       _A							,       dtype=torch.floataa  ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             snake_case						=   betas_for_alpha_bar(_A  )
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'  )

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

         #  set all values
         self.set_timesteps(_A							,       _A							,       _A  )





     def 						UpperCAmelCase(self :       Optional[int]							,       _A :       Dict							,       _A :       Optional[Any]=None  )		->		int:


         if schedule_timesteps is None:
             snake_case						=   self.timesteps

         snake_case						=   (schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter  ) == 0:
             snake_case						=   1 if len(_A  ) > 1 else 0
         else:
             snake_case						=   timestep.cpu().item() if torch.is_tensor(_A  ) else timestep
             snake_case						=   self._index_counter[timestep_int]

         return indices[pos].item()





     @property
     def 						UpperCAmelCase(self :       Optional[Any]  )		->		List[Any]:


         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5





     def 						UpperCAmelCase(self :       int							,       _A :       torch.FloatTensor							,       _A :       Union[float, torch.FloatTensor]							,       )		->		Optional[int]:
         snake_case						=   self.index_for_timestep(_A  )

         if self.state_in_first_order:
             snake_case						=   self.sigmas[step_index]
         else:
             snake_case						=   self.sigmas_interpol[step_index - 1]

         snake_case						=   sample / ((sigma**2 + 1) ** 0.5)
         return sample





     def 						UpperCAmelCase(self :       Dict							,       _A :       int							,       _A :       Union[str, torch.device] = None							,       _A :       Optional[int] = None							,       )		->		Optional[int]:
         snake_case						=   num_inference_steps

         snake_case						=   num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             snake_case						=   np.linspace(0							,       num_train_timesteps - 1							,       _A							,       dtype=_A  )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             snake_case						=   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						=   (np.arange(0							,       _A  ) * step_ratio).round()[::-1].copy().astype(_A  )
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             snake_case						=   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						=   (np.arange(_A							,       0							,       -step_ratio  )).round().copy().astype(_A  )
             timesteps -= 1
         else:
             raise ValueError(
                 f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'  )

         snake_case						=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
         snake_case						=   torch.from_numpy(np.log(_A  )  ).to(_A  )

         snake_case						=   np.interp(_A							,       np.arange(0							,       len(_A  )  )							,       _A  )
         snake_case						=   np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
         snake_case						=   torch.from_numpy(_A  ).to(device=_A  )

         # compute up and down sigmas
         snake_case						=   sigmas.roll(-1  )
         snake_case						=   0.0
         snake_case						=   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
         snake_case						=   (sigmas_next**2 - sigmas_up**2) ** 0.5
         snake_case						=   0.0

         # compute interpolated sigmas
         snake_case						=   sigmas.log().lerp(sigmas_down.log()							,       0.5  ).exp()
         snake_case						=   0.0

         # set sigmas
         snake_case						=   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2  ), sigmas[-1:]]  )
         snake_case						=   torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2  ), sigmas_interpol[-1:]]  )
         snake_case						=   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2  ), sigmas_up[-1:]]  )
         snake_case						=   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2  ), sigmas_down[-1:]]  )

         if str(_A  ).startswith("mps"  ):
             # mps does not support float64
             snake_case						=   torch.from_numpy(_A  ).to(_A							,       dtype=torch.floataa  )
         else:
             snake_case						=   torch.from_numpy(_A  ).to(_A  )

         snake_case						=   self.sigma_to_t(_A  ).to(_A							,       dtype=timesteps.dtype  )
         snake_case						=   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])							,       dim=-1  ).flatten()

         snake_case						=   torch.cat([timesteps[:1], interleaved_timesteps]  )

         snake_case						=   None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         snake_case						=   defaultdict(_A  )





     def 						UpperCAmelCase(self :       Tuple							,       _A :       Optional[Any]  )		->		List[Any]:
         snake_case						=   sigma.log()

         # get distribution
         snake_case						=   log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         snake_case						=   dists.ge(0  ).cumsum(dim=0  ).argmax(dim=0  ).clamp(max=self.log_sigmas.shape[0] - 2  )
         snake_case						=   low_idx + 1

         snake_case						=   self.log_sigmas[low_idx]
         snake_case						=   self.log_sigmas[high_idx]

         # interpolate sigmas
         snake_case						=   (low - log_sigma) / (low - high)
         snake_case						=   w.clamp(0							,       1  )

         # transform interpolation to time range
         snake_case						=   (1 - w) * low_idx + w * high_idx
         snake_case						=   t.view(sigma.shape  )
         return t





     @property
     def 						UpperCAmelCase(self :       Dict  )		->		Union[str, Any]:
         return self.sample is None





     def 						UpperCAmelCase(self :       List[Any]							,       _A :       Union[torch.FloatTensor, np.ndarray]							,       _A :       Union[float, torch.FloatTensor]							,       _A :       Union[torch.FloatTensor, np.ndarray]							,       _A :       Optional[torch.Generator] = None							,       _A :       bool = True							,       )		->		Optional[Any]:
         snake_case						=   self.index_for_timestep(_A  )

         # advance index counter by 1
         snake_case						=   timestep.cpu().item() if torch.is_tensor(_A  ) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
             snake_case						=   self.sigmas[step_index]
             snake_case						=   self.sigmas_interpol[step_index]
             snake_case						=   self.sigmas_up[step_index]
             snake_case						=   self.sigmas_down[step_index - 1]
         else:
             # 2nd order / KPDM2's method
             snake_case						=   self.sigmas[step_index - 1]
             snake_case						=   self.sigmas_interpol[step_index - 1]
             snake_case						=   self.sigmas_up[step_index - 1]
             snake_case						=   self.sigmas_down[step_index - 1]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         snake_case						=   0
         snake_case						=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         snake_case						=   model_output.device
         snake_case						=   randn_tensor(model_output.shape							,       dtype=model_output.dtype							,       device=_A							,       generator=_A  )

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             snake_case						=   sigma_hat if self.state_in_first_order else sigma_interpol
             snake_case						=   sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             snake_case						=   sigma_hat if self.state_in_first_order else sigma_interpol
             snake_case						=   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             raise NotImplementedError("prediction_type not implemented yet: sample"  )
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )

         if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             snake_case						=   (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             snake_case						=   sigma_interpol - sigma_hat

             # store for 2nd order step
             snake_case						=   sample
             snake_case						=   dt
             snake_case						=   sample + derivative * dt
         else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             snake_case						=   (sample - pred_original_sample) / sigma_interpol
             # 3. delta timestep
             snake_case						=   sigma_down - sigma_hat

             snake_case						=   self.sample
             snake_case						=   None

             snake_case						=   sample + derivative * dt
             snake_case						=   prev_sample + noise * sigma_up

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=_A  )





     def 						UpperCAmelCase(self :       str							,       _A :       torch.FloatTensor							,       _A :       torch.FloatTensor							,       _A :       torch.FloatTensor							,       )		->		List[Any]:
         snake_case						=   self.sigmas.to(device=original_samples.device							,       dtype=original_samples.dtype  )
         if original_samples.device.type == "mps" and torch.is_floating_point(_A  ):
             # mps does not support float64
             snake_case						=   self.timesteps.to(original_samples.device							,       dtype=torch.floataa  )
             snake_case						=   timesteps.to(original_samples.device							,       dtype=torch.floataa  )
         else:
             snake_case						=   self.timesteps.to(original_samples.device  )
             snake_case						=   timesteps.to(original_samples.device  )

         snake_case						=   [self.index_for_timestep(_A							,       _A  ) for t in timesteps]

         snake_case						=   sigmas[step_indices].flatten()
         while len(sigma.shape  ) < len(original_samples.shape  ):
             snake_case						=   sigma.unsqueeze(-1  )

         snake_case						=   original_samples + noise * sigma
         return noisy_samples





     def __len__(self :       List[Any]  )		->		Optional[int]:
         return self.config.num_train_timesteps




"""simple docstring"""



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]=3      ):
     A__				      =	int(mask / 2      )
     # copy image size
     A__				      =	zeros_like(UpperCAmelCase_      )
     for i in range(UpperCAmelCase_       , gray_img.shape[0] - bd      ):
          for j in range(UpperCAmelCase_       , gray_img.shape[1] - bd      ):
               # get mask according with mask
               A__				      =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
               # calculate mask median
               A__				      =	sort(UpperCAmelCase_      )[inta(divide((multiply(UpperCAmelCase_       , UpperCAmelCase_      ))       , 2      ) + 1      )]
               A__				      =	median
     return median_img


if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE_       :						int					=    imread('../image_data/lena.jpg')
 # turn image in gray scale value
 SCREAMING_SNAKE_CASE_       :						str					=    cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 SCREAMING_SNAKE_CASE_       :						List[Any]					=    median_filter(gray, 3)
 SCREAMING_SNAKE_CASE_       :						str					=    median_filter(gray, 5)

 # show result images
 imshow('median filter with 3x3 mask', medianaxa)
 imshow('median filter with 5x5 mask', medianaxa)
 waitKey(0)






