






"""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


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






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






class 					a						( __snake_case  ,				__snake_case		):
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			1






    @register_to_config
    def __init__(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							int = 40	,						__SCREAMING_SNAKE_CASE					:							float = 0.002	,						__SCREAMING_SNAKE_CASE					:							float = 80.0	,						__SCREAMING_SNAKE_CASE					:							float = 0.5	,						__SCREAMING_SNAKE_CASE					:							float = 1.0	,						__SCREAMING_SNAKE_CASE					:							float = 7.0	,						__SCREAMING_SNAKE_CASE					:							bool = True	,						)					->		Union[str, Any]:
      # standard deviation of the initial noise distribution
      lowerCamelCase_   					=						sigma_max

      lowerCamelCase_   					=						np.linspace(0	,						1	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						self._convert_to_karras(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						self.sigma_to_t(__SCREAMING_SNAKE_CASE )

      # setable values
      lowerCamelCase_   					=						None
      lowerCamelCase_   					=						torch.from_numpy(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						torch.from_numpy(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						False
      lowerCamelCase_   					=						False






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=None )					->		int:
      if schedule_timesteps is None:
        lowerCamelCase_   					=						self.timesteps

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






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							Union[float, torch.FloatTensor] )					->		torch.FloatTensor:
      # Get sigma corresponding to timestep
      if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor ):
        lowerCamelCase_   					=						timestep.to(self.timesteps.device )
      lowerCamelCase_   					=						self.index_for_timestep(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						self.sigmas[step_idx]

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

      lowerCamelCase_   					=						True
      return sample






    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[float, np.ndarray] )					->		List[Any]:
      if not isinstance(__SCREAMING_SNAKE_CASE	,						np.ndarray ):
        lowerCamelCase_   					=						np.array(__SCREAMING_SNAKE_CASE	,						dtype=np.floataa )

      lowerCamelCase_   					=						1000 * 0.25 * np.log(sigmas + 1e-4_4 )

      return timesteps






    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Optional[int] = None	,						__SCREAMING_SNAKE_CASE					:							Union[str, torch.device] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[List[int]] = None	,						)					->		Optional[int]:
      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(__SCREAMING_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}.''' )

        lowerCamelCase_   					=						np.array(__SCREAMING_SNAKE_CASE	,						dtype=np.intaa )
        lowerCamelCase_   					=						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.''' )

        lowerCamelCase_   					=						num_inference_steps

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

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

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

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






    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Optional[Any] )					->		Dict:
      lowerCamelCase_   					=						self.config.sigma_min
      lowerCamelCase_   					=						self.config.sigma_max

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






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							str )					->		Union[str, Any]:
      lowerCamelCase_   					=						self.config.sigma_data

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






    def        UpperCamelCase	(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							str )					->		Optional[Any]:
      lowerCamelCase_   					=						self.config.sigma_min
      lowerCamelCase_   					=						self.config.sigma_data

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






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							Union[float, torch.FloatTensor]	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							Optional[torch.Generator] = None	,						__SCREAMING_SNAKE_CASE					:							bool = True	,						)					->		Union[CMStochasticIterativeSchedulerOutput, Tuple]:

      if (
          isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
          or isinstance(__SCREAMING_SNAKE_CASE	,						torch.IntTensor )
          or isinstance(__SCREAMING_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(__SCREAMING_SNAKE_CASE	,						torch.Tensor ):
        lowerCamelCase_   					=						timestep.to(self.timesteps.device )

      lowerCamelCase_   					=						self.config.sigma_min
      lowerCamelCase_   					=						self.config.sigma_max

      lowerCamelCase_   					=						self.index_for_timestep(__SCREAMING_SNAKE_CASE )

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

      # Get scalings for boundary conditions
      lowerCamelCase_					,     lowerCamelCase_   					=						self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE )

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

      lowerCamelCase_   					=						sigma_next.clamp(min=__SCREAMING_SNAKE_CASE	,						max=__SCREAMING_SNAKE_CASE )

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

      if not return_dict:
        return (prev_sample,)

      return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						)					->		torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      lowerCamelCase_   					=						self.sigmas.to(device=original_samples.device	,						dtype=original_samples.dtype )
      if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE ):
        # mps does not support float64
        lowerCamelCase_   					=						self.timesteps.to(original_samples.device	,						dtype=torch.floataa )
        lowerCamelCase_   					=						timesteps.to(original_samples.device	,						dtype=torch.floataa )
      else:
        lowerCamelCase_   					=						self.timesteps.to(original_samples.device )
        lowerCamelCase_   					=						timesteps.to(original_samples.device )

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

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

      lowerCamelCase_   					=						original_samples + noise * sigma
      return noisy_samples






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












"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE :    List[Any]    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :    str    =						{
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					str						=			"""regnet"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			["""x""", """y"""]






    def __init__(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							Any=3	,						__SCREAMING_SNAKE_CASE					:							Any=32	,						__SCREAMING_SNAKE_CASE					:							List[str]=[128, 192, 512, 1088]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=[2, 6, 12, 2]	,						__SCREAMING_SNAKE_CASE					:							str=64	,						__SCREAMING_SNAKE_CASE					:							List[Any]="y"	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="relu"	,						**__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						)					->		Union[str, Any]:
      super().__init__(**__SCREAMING_SNAKE_CASE )
      if layer_type not in self.layer_types:
        raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
      lowerCamelCase_   					=						num_channels
      lowerCamelCase_   					=						embedding_size
      lowerCamelCase_   					=						hidden_sizes
      lowerCamelCase_   					=						depths
      lowerCamelCase_   					=						groups_width
      lowerCamelCase_   					=						layer_type
      lowerCamelCase_   					=						hidden_act
      # always downsample in the first stage
      lowerCamelCase_   					=						True




