





'''simple docstring'''



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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=0.999					,	SCREAMING_SNAKE_CASE__="cosine"					,	):







  '''simple docstring'''



  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

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




class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[int]      =   [e.name for e in KarrasDiffusionSchedulers]
     UpperCAmelCase__       :						Dict      =   2

     @register_to_config
     def __init__(					self						,   lowerCamelCase = 1_000						,   lowerCamelCase = 0.00085						,   lowerCamelCase = 0.012						,   lowerCamelCase = "linear"						,   lowerCamelCase = None						,   lowerCamelCase = "epsilon"						,   lowerCamelCase = False						,   lowerCamelCase = False						,   lowerCamelCase = 1.0						,   lowerCamelCase = "linspace"						,   lowerCamelCase = 0						,   ):
       if trained_betas is not None:
         _snake_case     		=				torch.tensor(lowerCamelCase						,   dtype=torch.floataa	)
       elif beta_schedule == "linear":
         _snake_case     		=				torch.linspace(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   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						,   lowerCamelCase						,   dtype=torch.floataa	) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _snake_case     		=				betas_for_alpha_bar(lowerCamelCase						,   alpha_transform_type="cosine"	)
       elif beta_schedule == "exp":
         _snake_case     		=				betas_for_alpha_bar(lowerCamelCase						,   alpha_transform_type="exp"	)
       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(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       _snake_case     		=				use_karras_sigmas

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       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(lowerCamelCase	) > 1 else 0
       else:
         _snake_case     		=				timestep.cpu().item() if torch.is_tensor(lowerCamelCase	) else timestep
         _snake_case     		=				self._index_counter[timestep_int]

       return indices[pos].item()

     @property
     def 	UpperCamelCase(					self	):
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   ):
       _snake_case     		=				self.index_for_timestep(lowerCamelCase	)

       _snake_case     		=				self.sigmas[step_index]
       _snake_case     		=				sample / ((sigma**2 + 1) ** 0.5)
       return sample

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   ):
       _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						,   lowerCamelCase						,   dtype=lowerCamelCase	)[::-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						,   lowerCamelCase	) * step_ratio).round()[::-1].copy().astype(lowerCamelCase	)
         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(lowerCamelCase						,   0						,   -step_ratio	)).round().copy().astype(lowerCamelCase	)
         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     		=				np.log(lowerCamelCase	)
       _snake_case     		=				np.interp(lowerCamelCase						,   np.arange(0						,   len(lowerCamelCase	)	)						,   lowerCamelCase	)

       if self.config.use_karras_sigmas:
         _snake_case     		=				self._convert_to_karras(in_sigmas=lowerCamelCase						,   num_inference_steps=self.num_inference_steps	)
         _snake_case     		=				np.array([self._sigma_to_t(lowerCamelCase						,   lowerCamelCase	) for sigma in sigmas]	)

       _snake_case     		=				np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
       _snake_case     		=				torch.from_numpy(lowerCamelCase	).to(device=lowerCamelCase	)
       _snake_case     		=				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2	), sigmas[-1:]]	)

       _snake_case     		=				torch.from_numpy(lowerCamelCase	)
       _snake_case     		=				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2	)]	)

       if str(lowerCamelCase	).startswith("mps"	):
         # mps does not support float64
         _snake_case     		=				timesteps.to(lowerCamelCase						,   dtype=torch.floataa	)
       else:
         _snake_case     		=				timesteps.to(device=lowerCamelCase	)

       # empty dt and derivative
       _snake_case     		=				None
       _snake_case     		=				None

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       # get log sigma
       _snake_case     		=				np.log(lowerCamelCase	)

       # get distribution
       _snake_case     		=				log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       _snake_case     		=				np.cumsum((dists >= 0)						,   axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
       _snake_case     		=				low_idx + 1

       _snake_case     		=				log_sigmas[low_idx]
       _snake_case     		=				log_sigmas[high_idx]

       # interpolate sigmas
       _snake_case     		=				(low - log_sigma) / (low - high)
       _snake_case     		=				np.clip(lowerCamelCase						,   0						,   1	)

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				in_sigmas[-1].item()
       _snake_case     		=				in_sigmas[0].item()

       _snake_case     		=				7.0  # 7.0 is the value used in the paper
       _snake_case     		=				np.linspace(0						,   1						,   lowerCamelCase	)
       _snake_case     		=				sigma_min ** (1 / rho)
       _snake_case     		=				sigma_max ** (1 / rho)
       _snake_case     		=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas

     @property
     def 	UpperCamelCase(					self	):
       return self.dt is None

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = True						,   ):
       _snake_case     		=				self.index_for_timestep(lowerCamelCase	)

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

       if self.state_in_first_order:
         _snake_case     		=				self.sigmas[step_index]
         _snake_case     		=				self.sigmas[step_index + 1]
       else:
         # 2nd order / Heun's method
         _snake_case     		=				self.sigmas[step_index - 1]
         _snake_case     		=				self.sigmas[step_index]

       # 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

       # 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_next
         _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_next
         _snake_case     		=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
       elif self.config.prediction_type == "sample":
         _snake_case     		=				model_output
       else:
         raise ValueError(
             F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

       if self.config.clip_sample:
         _snake_case     		=				pred_original_sample.clamp(
             -self.config.clip_sample_range						,   self.config.clip_sample_range	)

       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_next - sigma_hat

         # store for 2nd order step
         _snake_case     		=				derivative
         _snake_case     		=				dt
         _snake_case     		=				sample
       else:
         # 2. 2nd order / Heun's method
         _snake_case     		=				(sample - pred_original_sample) / sigma_next
         _snake_case     		=				(self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         _snake_case     		=				self.dt
         _snake_case     		=				self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         _snake_case     		=				None
         _snake_case     		=				None
         _snake_case     		=				None

       _snake_case     		=				sample + derivative * dt

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       _snake_case     		=				self.sigmas.to(device=original_samples.device						,   dtype=original_samples.dtype	)
       if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase	):
         # 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(lowerCamelCase						,   lowerCamelCase	) 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	):
       return self.config.num_train_timesteps









'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


