




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


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







@dataclass
class __snake_case     (     a						):
   UpperCAmelCase__    :       torch.FloatTensor







class __snake_case     (     a					,    a						):
   UpperCAmelCase__    :       List[str]		=    1







   @register_to_config
   def __init__( self						:		Dict   ,		_snake_case						:		int = 40   ,		_snake_case						:		float = 0.0_0_2   ,		_snake_case						:		float = 8_0.0   ,		_snake_case						:		float = 0.5   ,		_snake_case						:		float = 1.0   ,		_snake_case						:		float = 7.0   ,		_snake_case						:		bool = True   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				sigma_max

        UpperCAmelCase_		=				np.linspace(0   ,		1   ,		_snake_case)
        UpperCAmelCase_		=				self._convert_to_karras(_snake_case)
        UpperCAmelCase_		=				self.sigma_to_t(_snake_case)

        # setable values
        UpperCAmelCase_		=				None
        UpperCAmelCase_		=				torch.from_numpy(_snake_case)
        UpperCAmelCase_		=				torch.from_numpy(_snake_case)
        UpperCAmelCase_		=				False
        UpperCAmelCase_		=				False







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		List[Any]   ,		_snake_case						:		Tuple=None):

        """simple docstring"""


        if schedule_timesteps is None:
             UpperCAmelCase_		=				self.timesteps

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







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		Union[float, torch.FloatTensor]):

        """simple docstring"""


        if isinstance(_snake_case   ,		torch.Tensor):
             UpperCAmelCase_		=				timestep.to(self.timesteps.device)
        UpperCAmelCase_		=				self.index_for_timestep(_snake_case)
        UpperCAmelCase_		=				self.sigmas[step_idx]

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

        UpperCAmelCase_		=				True
        return sample







   def lowerCamelCase		( self						:		Any   ,		_snake_case						:		Union[float, np.ndarray]):

        """simple docstring"""


        if not isinstance(_snake_case   ,		np.ndarray):
             UpperCAmelCase_		=				np.array(_snake_case   ,		dtype=np.floataa)

        UpperCAmelCase_		=				1000 * 0.2_5 * np.log(sigmas + 1e-44)

        return timesteps







   def lowerCamelCase		( self						:		List[str]   ,		_snake_case						:		Optional[int] = None   ,		_snake_case						:		Union[str, torch.device] = None   ,		_snake_case						:		Optional[List[int]] = None   ,		):

        """simple docstring"""


        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(_snake_case)):
                  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}.""")

             UpperCAmelCase_		=				np.array(_snake_case   ,		dtype=np.intaa)
             UpperCAmelCase_		=				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.""")

             UpperCAmelCase_		=				num_inference_steps

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

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

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

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







   def lowerCamelCase		( self						:		Optional[int]   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				self.config.sigma_min
        UpperCAmelCase_		=				self.config.sigma_max

        UpperCAmelCase_		=				self.config.rho
        UpperCAmelCase_		=				sigma_min ** (1 / rho)
        UpperCAmelCase_		=				sigma_max ** (1 / rho)
        UpperCAmelCase_		=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas







   def lowerCamelCase		( self						:		List[str]   ,		_snake_case						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.config.sigma_data

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







   def lowerCamelCase		( self						:		Optional[int]   ,		_snake_case						:		str):

        """simple docstring"""


        UpperCAmelCase_		=				self.config.sigma_min
        UpperCAmelCase_		=				self.config.sigma_data

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







   def lowerCamelCase		( self						:		Union[str, Any]   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		Union[float, torch.FloatTensor]   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		Optional[torch.Generator] = None   ,		_snake_case						:		bool = True   ,		):

        """simple docstring"""


        if (
            isinstance(_snake_case   ,		_snake_case)
            or isinstance(_snake_case   ,		torch.IntTensor)
            or isinstance(_snake_case   ,		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(_snake_case   ,		torch.Tensor):
             UpperCAmelCase_		=				timestep.to(self.timesteps.device)

        UpperCAmelCase_		=				self.config.sigma_min
        UpperCAmelCase_		=				self.config.sigma_max

        UpperCAmelCase_		=				self.index_for_timestep(_snake_case)

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

        # Get scalings for boundary conditions
        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_scalings_for_boundary_condition(_snake_case)

        # 1. Denoise model output using boundary conditions
        UpperCAmelCase_		=				c_out * model_output + c_skip * sample
        if self.config.clip_denoised:
             UpperCAmelCase_		=				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:
             UpperCAmelCase_		=				randn_tensor(
                 model_output.shape   ,		dtype=model_output.dtype   ,		device=model_output.device   ,		generator=_snake_case)
        else:
             UpperCAmelCase_		=				torch.zeros_like(_snake_case)
        UpperCAmelCase_		=				noise * self.config.s_noise

        UpperCAmelCase_		=				sigma_next.clamp(min=_snake_case   ,		max=_snake_case)

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

        if not return_dict:
             return (prev_sample,)

        return CMStochasticIterativeSchedulerOutput(prev_sample=_snake_case)







   def lowerCamelCase		( self						:		List[str]   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		torch.FloatTensor   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				self.sigmas.to(device=original_samples.device   ,		dtype=original_samples.dtype)
        if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case):
             # mps does not support float64
             UpperCAmelCase_		=				self.timesteps.to(original_samples.device   ,		dtype=torch.floataa)
             UpperCAmelCase_		=				timesteps.to(original_samples.device   ,		dtype=torch.floataa)
        else:
             UpperCAmelCase_		=				self.timesteps.to(original_samples.device)
             UpperCAmelCase_		=				timesteps.to(original_samples.device)

        UpperCAmelCase_		=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        UpperCAmelCase_		=				original_samples + noise * sigma
        return noisy_samples







   def __len__( self						:		Optional[Any]):

        """simple docstring"""


        return self.config.num_train_timesteps












import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


snake_case_					:					Dict       						=			logging.get_logger(__name__)







class __snake_case     (     a						):







   def __init__( self						:		Union[str, Any]   ,		*_snake_case						:		int   ,		**_snake_case						:		List[str]):

        """simple docstring"""


        warnings.warn(
            '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use LevitImageProcessor instead.'''   ,		_snake_case   ,		)
        super().__init__(*_snake_case   ,		**_snake_case)






