






"""simple docstring"""


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


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


@dataclass
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      torch.FloatTensor





class    snake_case						(				_lowerCAmelCase  ,      _lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[Any]							=					1


   @register_to_config
   def __init__(      self					:	Tuple,			_lowerCamelCase					:	int = 40,			_lowerCamelCase					:	float = 0.0_02,			_lowerCamelCase					:	float = 80.0,			_lowerCamelCase					:	float = 0.5,			_lowerCamelCase					:	float = 1.0,			_lowerCamelCase					:	float = 7.0,			_lowerCamelCase					:	bool = True,			):

     '''simple docstring'''
     # standard deviation of the initial noise distribution
     __A		     =					sigma_max

     __A		     =					np.linspace(0,			1,			_lowerCamelCase    )
     __A		     =					self._convert_to_karras(_lowerCamelCase    )
     __A		     =					self.sigma_to_t(_lowerCamelCase    )

     # setable values
     __A		     =					None
     __A		     =					torch.from_numpy(_lowerCamelCase    )
     __A		     =					torch.from_numpy(_lowerCamelCase    )
     __A		     =					False
     __A		     =					False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Optional[Any]=None    ):

     '''simple docstring'''
     if schedule_timesteps is None:
       __A		     =					self.timesteps

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Union[float, torch.FloatTensor]    ):

     '''simple docstring'''
     # Get sigma corresponding to timestep
     if isinstance(_lowerCamelCase,			torch.Tensor    ):
       __A		     =					timestep.to(self.timesteps.device    )
     __A		     =					self.index_for_timestep(_lowerCamelCase    )
     __A		     =					self.sigmas[step_idx]

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

     __A		     =					True
     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Union[float, np.ndarray]    ):

     '''simple docstring'''
     if not isinstance(_lowerCamelCase,			np.ndarray    ):
       __A		     =					np.array(_lowerCamelCase,			dtype=np.floataa    )

     __A		     =					10_00 * 0.25 * np.log(sigmas + 1e-44    )

     return timesteps


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Optional[int] = None,			_lowerCamelCase					:	Union[str, torch.device] = None,			_lowerCamelCase					:	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(_lowerCamelCase    )    ):
         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}.'    )

       __A		     =					np.array(_lowerCamelCase,			dtype=np.intaa    )
       __A		     =					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.'    )

       __A		     =					num_inference_steps

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

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

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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_max

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_data

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_data

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Union[float, torch.FloatTensor],			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Optional[torch.Generator] = None,			_lowerCamelCase					:	bool = True,			):

     '''simple docstring'''

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

     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_max

     __A		     =					self.index_for_timestep(_lowerCamelCase    )

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

     # Get scalings for boundary conditions
     __A     ,       __A		     =					self.get_scalings_for_boundary_condition(_lowerCamelCase    )

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

     __A		     =					sigma_next.clamp(min=_lowerCamelCase,			max=_lowerCamelCase    )

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

     if not return_dict:
       return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	torch.FloatTensor,			):

     '''simple docstring'''
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     __A		     =					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
       __A		     =					self.timesteps.to(original_samples.device,			dtype=torch.floataa    )
       __A		     =					timesteps.to(original_samples.device,			dtype=torch.floataa    )
     else:
       __A		     =					self.timesteps.to(original_samples.device    )
       __A		     =					timesteps.to(original_samples.device    )

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

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

     __A		     =					original_samples + noise * sigma
     return noisy_samples






   def __len__(      self					:	Any    ):

     '''simple docstring'''
     return self.config.num_train_timesteps






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase   :					int    =			{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[Any]    =			["LevitFeatureExtractor"]
					_UpperCAmelCase   :					Optional[int]    =			["LevitImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[int]    =			[
					    "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "LevitForImageClassification",
					    "LevitForImageClassificationWithTeacher",
					    "LevitModel",
					    "LevitPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_levit import LevitFeatureExtractor
										from .image_processing_levit import LevitImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_levit import (
										    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LevitForImageClassification,
										    LevitForImageClassificationWithTeacher,
										    LevitModel,
										    LevitPreTrainedModel,
										)
else:
					import sys

					_UpperCAmelCase   :					Optional[Any]    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
