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




@dataclass
class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						42






class    _snake_case  (						_lowercase ,				_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						1






							@register_to_config
							def __init__(					self						:			Union[str, Any]						, snake_case__						:			int = 4_0						, snake_case__						:			float = 0.002						, snake_case__						:			float = 80.0						, snake_case__						:			float = 0.5						, snake_case__						:			float = 1.0						, snake_case__						:			float = 7.0						, snake_case__						:			bool = True						, ):






												"""simple docstring"""



												__a:						str		    =					sigma_max

												__a:						Tuple		    =					np.linspace(0						, 1						, snake_case__    )
												__a:						Dict		    =					self._convert_to_karras(snake_case__    )
												__a:						Optional[Any]		    =					self.sigma_to_t(snake_case__    )

												# setable values
												__a:						Any		    =					None
												__a:						List[str]		    =					torch.from_numpy(snake_case__    )
												__a:						List[str]		    =					torch.from_numpy(snake_case__    )
												__a:						Union[str, Any]		    =					False
												__a:						Union[str, Any]		    =					False






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			int						, snake_case__						:			Union[str, Any]=None    ):






												"""simple docstring"""



												if schedule_timesteps is None:
																	__a:						Tuple		    =					self.timesteps

												__a:						List[Any]		    =					(schedule_timesteps == timestep).nonzero()
												return indices.item()






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			torch.FloatTensor						, snake_case__						:			Union[float, torch.FloatTensor]    ):






												"""simple docstring"""



												if isinstance(snake_case__						, torch.Tensor    ):
																	__a:						int		    =					timestep.to(self.timesteps.device    )
												__a:						List[Any]		    =					self.index_for_timestep(snake_case__    )
												__a:						List[str]		    =					self.sigmas[step_idx]

												__a:						int		    =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

												__a:						Union[str, Any]		    =					True
												return sample






							def 							__lowercase  (					self						:			int						, snake_case__						:			Union[float, np.ndarray]    ):






												"""simple docstring"""



												if not isinstance(snake_case__						, np.ndarray    ):
																	__a:						Dict		    =					np.array(snake_case__						, dtype=np.floataa    )

												__a:						Union[str, Any]		    =					1_0_0_0 * 0.25 * np.log(sigmas + 1e-44    )

												return timesteps






							def 							__lowercase  (					self						:			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}."""    )

																	__a:						Dict		    =					np.array(snake_case__						, dtype=np.intaa    )
																	__a:						List[str]		    =					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:						Tuple		    =					num_inference_steps

																	__a:						int		    =					self.config.num_train_timesteps // self.num_inference_steps
																	__a:						Tuple		    =					(np.arange(0						, snake_case__    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
																	__a:						Union[str, Any]		    =					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:						str		    =					self.config.num_train_timesteps
												__a:						List[Any]		    =					timesteps[::-1].copy()
												__a:						Optional[int]		    =					ramp / (num_train_timesteps - 1)
												__a:						Union[str, Any]		    =					self._convert_to_karras(snake_case__    )
												__a:						int		    =					self.sigma_to_t(snake_case__    )

												__a:						str		    =					np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
												__a:						Tuple		    =					torch.from_numpy(snake_case__    ).to(device=snake_case__    )

												if str(snake_case__    ).startswith("""mps"""    ):
																	# mps does not support float64
																	__a:						int		    =					torch.from_numpy(snake_case__    ).to(snake_case__						, dtype=torch.floataa    )
												else:
																	__a:						int		    =					torch.from_numpy(snake_case__    ).to(device=snake_case__    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			Any    ):






												"""simple docstring"""



												__a:						float		    =					self.config.sigma_min
												__a:						float		    =					self.config.sigma_max

												__a:						List[str]		    =					self.config.rho
												__a:						Any		    =					sigma_min ** (1 / rho)
												__a:						Optional[int]		    =					sigma_max ** (1 / rho)
												__a:						Optional[Any]		    =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			Dict    ):






												"""simple docstring"""



												__a:						Dict		    =					self.config.sigma_data

												__a:						List[str]		    =					sigma_data**2 / (sigma**2 + sigma_data**2)
												__a:						Any		    =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						Any		    =					self.config.sigma_min
												__a:						Optional[Any]		    =					self.config.sigma_data

												__a:						str		    =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
												__a:						Union[str, Any]		    =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out






							def 							__lowercase  (					self						:			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    ):
																	__a:						Union[str, Any]		    =					timestep.to(self.timesteps.device    )

												__a:						Union[str, Any]		    =					self.config.sigma_min
												__a:						Optional[Any]		    =					self.config.sigma_max

												__a:						List[str]		    =					self.index_for_timestep(snake_case__    )

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

												# Get scalings for boundary conditions
												__a    ,				__a:						Optional[int]		    =					self.get_scalings_for_boundary_condition(snake_case__    )

												# 1. Denoise model output using boundary conditions
												__a:						Tuple		    =					c_out * model_output + c_skip * sample
												if self.config.clip_denoised:
																	__a:						str		    =					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:						Union[str, Any]		    =					randn_tensor(
																	    model_output.shape						, dtype=model_output.dtype						, device=model_output.device						, generator=snake_case__    )
												else:
																	__a:						Optional[int]		    =					torch.zeros_like(snake_case__    )
												__a:						Dict		    =					noise * self.config.s_noise

												__a:						str		    =					sigma_next.clamp(min=snake_case__						, max=snake_case__    )

												# 3. Return noisy sample
												# tau = sigma_hat, eps = sigma_min
												__a:						Optional[int]		    =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

												if not return_dict:
																	return (prev_sample,)

												return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case__    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			torch.FloatTensor						, snake_case__						:			torch.FloatTensor						, snake_case__						:			torch.FloatTensor						, ):






												"""simple docstring"""



												__a:						Dict		    =					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
																	__a:						Dict		    =					self.timesteps.to(original_samples.device						, dtype=torch.floataa    )
																	__a:						int		    =					timesteps.to(original_samples.device						, dtype=torch.floataa    )
												else:
																	__a:						Optional[int]		    =					self.timesteps.to(original_samples.device    )
																	__a:						List[Any]		    =					timesteps.to(original_samples.device    )

												__a:						List[str]		    =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

												__a:						Tuple		    =					sigmas[step_indices].flatten()
												while len(sigma.shape    ) < len(original_samples.shape    ):
																	__a:						List[str]		    =					sigma.unsqueeze(-1    )

												__a:						List[Any]		    =					original_samples + noise * sigma
												return noisy_samples






							def __len__(					self						:			List[str]    ):






												"""simple docstring"""



												return self.config.num_train_timesteps


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

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




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**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    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
