


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

import numpy as np
import torch

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


UpperCAmelCase			=    logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 __snake_case												=  42







class lowerCAmelCase_						( lowerCamelCase__					,						lowerCamelCase__       ):

 '''simple docstring'''

 __snake_case												=  1



 @register_to_config
 def __init__(	self		,      _UpperCAmelCase = 40		,      _UpperCAmelCase = 0.002		,      _UpperCAmelCase = 80.0		,      _UpperCAmelCase = 0.5		,      _UpperCAmelCase = 1.0		,      _UpperCAmelCase = 7.0		,      _UpperCAmelCase = True		,      ):
       # standard deviation of the initial noise distribution
       snake_case_       =      sigma_max

       snake_case_       =      np.linspace(0		,      1		,      _UpperCAmelCase					)
       snake_case_       =      self._convert_to_karras(_UpperCAmelCase					)
       snake_case_       =      self.sigma_to_t(_UpperCAmelCase					)

       # setable values
       snake_case_       =      None
       snake_case_       =      torch.from_numpy(_UpperCAmelCase					)
       snake_case_       =      torch.from_numpy(_UpperCAmelCase					)
       snake_case_       =      False
       snake_case_       =      False



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase=None					):
       if schedule_timesteps is None:
             snake_case_       =      self.timesteps

       snake_case_       =      (schedule_timesteps == timestep).nonzero()
       return indices.item()



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       # Get sigma corresponding to timestep
       if isinstance(_UpperCAmelCase		,      torch.Tensor					):
             snake_case_       =      timestep.to(self.timesteps.device					)
       snake_case_       =      self.index_for_timestep(_UpperCAmelCase					)
       snake_case_       =      self.sigmas[step_idx]

       snake_case_       =      sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       snake_case_       =      True
       return sample



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase					):
       if not isinstance(_UpperCAmelCase		,      np.ndarray					):
             snake_case_       =      np.array(_UpperCAmelCase		,      dtype=np.floataa					)

       snake_case_       =      10_00 * 0.25 * np.log(sigmas + 1E-44					)

       return timesteps



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      ):
       if num_inference_steps is None and timesteps is None:
             raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''					)

       if num_inference_steps is not None and timesteps is not None:
             raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''					)

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
             for i in range(1		,      len(_UpperCAmelCase					)					):
                   if timesteps[i] >= timesteps[i - 1]:
                         raise ValueError('''`timesteps` must be in descending order.'''					)

             if timesteps[0] >= self.config.num_train_timesteps:
                   raise ValueError(
                       F'''`timesteps` must start before `self.config.train_timesteps`:'''
                       F''' {self.config.num_train_timesteps}.'''					)

             snake_case_       =      np.array(_UpperCAmelCase		,      dtype=np.intaa					)
             snake_case_       =      True
       else:
             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_       =      num_inference_steps

             snake_case_       =      self.config.num_train_timesteps // self.num_inference_steps
             snake_case_       =      (np.arange(0		,      _UpperCAmelCase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
             snake_case_       =      False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       snake_case_       =      self.config.num_train_timesteps
       snake_case_       =      timesteps[::-1].copy()
       snake_case_       =      ramp / (num_train_timesteps - 1)
       snake_case_       =      self._convert_to_karras(_UpperCAmelCase					)
       snake_case_       =      self.sigma_to_t(_UpperCAmelCase					)

       snake_case_       =      np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
       snake_case_       =      torch.from_numpy(_UpperCAmelCase					).to(device=_UpperCAmelCase					)

       if str(_UpperCAmelCase					).startswith('''mps'''					):
             # mps does not support float64
             snake_case_       =      torch.from_numpy(_UpperCAmelCase					).to(_UpperCAmelCase		,      dtype=torch.floataa					)
       else:
             snake_case_       =      torch.from_numpy(_UpperCAmelCase					).to(device=_UpperCAmelCase					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase					):
       snake_case_       =      self.config.sigma_min
       snake_case_       =      self.config.sigma_max

       snake_case_       =      self.config.rho
       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



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase					):
       snake_case_       =      self.config.sigma_data

       snake_case_       =      sigma_data**2 / (sigma**2 + sigma_data**2)
       snake_case_       =      sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase					):
       snake_case_       =      self.config.sigma_min
       snake_case_       =      self.config.sigma_data

       snake_case_       =      sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       snake_case_       =      (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = True		,      ):

       if (
           isinstance(_UpperCAmelCase		,      _UpperCAmelCase					)
           or isinstance(_UpperCAmelCase		,      torch.IntTensor					)
           or isinstance(_UpperCAmelCase		,      torch.LongTensor					)
       ):
             raise ValueError(
                 (
                     '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                     F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                     ''' one of the `scheduler.timesteps` as a timestep.'''
                 )		,      )

       if not self.is_scale_input_called:
             logger.warning(
                 '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                 '''See `StableDiffusionPipeline` for a usage example.'''					)

       if isinstance(_UpperCAmelCase		,      torch.Tensor					):
             snake_case_       =      timestep.to(self.timesteps.device					)

       snake_case_       =      self.config.sigma_min
       snake_case_       =      self.config.sigma_max

       snake_case_       =      self.index_for_timestep(_UpperCAmelCase					)

       # sigma_next corresponds to next_t in original implementation
       snake_case_       =      self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
             snake_case_       =      self.sigmas[step_index + 1]
       else:
             # Set sigma_next to sigma_min
             snake_case_       =      self.sigmas[-1]

       # Get scalings for boundary conditions
       snake_case_       ,    snake_case_       =      self.get_scalings_for_boundary_condition(_UpperCAmelCase					)

       # 1. Denoise model output using boundary conditions
       snake_case_       =      c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
             snake_case_       =      denoised.clamp(-1		,      1					)

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps					) > 1:
             snake_case_       =      randn_tensor(
                 model_output.shape		,      dtype=model_output.dtype		,      device=model_output.device		,      generator=_UpperCAmelCase					)
       else:
             snake_case_       =      torch.zeros_like(_UpperCAmelCase					)
       snake_case_       =      noise * self.config.s_noise

       snake_case_       =      sigma_next.clamp(min=_UpperCAmelCase		,      max=_UpperCAmelCase					)

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       snake_case_       =      denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
             return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=_UpperCAmelCase					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):
       # 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(_UpperCAmelCase					):
             # 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_       =      [(schedule_timesteps == t).nonzero().item() 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



def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if number != int(SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if number < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      snake_case_       =      1
      for i in range(1					,					number + 1					):
            value *= i
      return value






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if n < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      return 1 if n == 0 or n == 1 else n * factorial(n - 1					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase			=    int(input("""Enter a positive integer: """).strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')