"""simple docstring"""







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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	42
				_snake_case  =	None



def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase=0.9_9_9  ,__UpperCAmelCase="cosine"  ,):






	"""simple docstring"""





	if alpha_transform_type == "cosine":

		def alpha_bar_fn(__UpperCAmelCase      ):
			return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2      ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(__UpperCAmelCase      ):
			return math.exp(t * -1_2.0      )

	else:
		raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''      )

	_lowercase			:     Optional[int]      					=				[]
	for i in range(__UpperCAmelCase      ):
		_lowercase			:     List[Any]      					=				i / num_diffusion_timesteps
		_lowercase			:     List[Any]      					=				(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase      ) / alpha_bar_fn(__UpperCAmelCase      )  ,__UpperCAmelCase      )      )
	return torch.tensor(__UpperCAmelCase  ,dtype=torch.floataa      )







class 			_lowerCamelCase      (__lowerCamelCase    ,				__lowerCamelCase  ):
				_snake_case  =	[e.name for e in KarrasDiffusionSchedulers]
				_snake_case  =	1







				@register_to_config
				def __init__(			self     :							Optional[int]      ,       lowerCamelCase_     :							int = 1_0_0_0      ,       lowerCamelCase_     :							float = 0.0001      ,       lowerCamelCase_     :							float = 0.02      ,       lowerCamelCase_     :							str = "linear"      ,       lowerCamelCase_     :							Optional[Union[np.ndarray, List[float]]] = None      ,       lowerCamelCase_     :							Optional[bool] = False      ,       lowerCamelCase_     :							str = "epsilon"      ,       lowerCamelCase_     :							str = "linspace"      ,       lowerCamelCase_     :							int = 0      ,       ):

					"""simple docstring"""






					if trained_betas is not None:
						_lowercase			:     int      					=				torch.tensor(lowerCamelCase_      ,       dtype=torch.floataa    )
					elif beta_schedule == "linear":
						_lowercase			:     int      					=				torch.linspace(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       dtype=torch.floataa    )
					elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						_lowercase			:     Tuple      					=				(
						    torch.linspace(beta_start**0.5      ,       beta_end**0.5      ,       lowerCamelCase_      ,       dtype=torch.floataa    ) ** 2
						)
					elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						_lowercase			:     int      					=				betas_for_alpha_bar(lowerCamelCase_    )
					else:
						raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

					_lowercase			:     List[str]      					=				1.0 - self.betas
					_lowercase			:     Optional[int]      					=				torch.cumprod(self.alphas      ,       dim=0    )

					_lowercase			:     Dict      					=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
					_lowercase			:     str      					=				np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
					_lowercase			:     List[str]      					=				torch.from_numpy(lowerCamelCase_    )

					# setable values
					_lowercase			:     List[str]      					=				None
					_lowercase			:     int      					=				use_karras_sigmas
					self.set_timesteps(lowerCamelCase_      ,       lowerCamelCase_    )
					_lowercase			:     str      					=				[]
					_lowercase			:     str      					=				False







				@property
				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					if self.config.timestep_spacing in ["linspace", "trailing"]:
						return self.sigmas.max()

					return (self.sigmas.max() ** 2 + 1) ** 0.5







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							torch.FloatTensor      ,       lowerCamelCase_     :							Union[float, torch.FloatTensor]    ):

					"""simple docstring"""






					if isinstance(lowerCamelCase_      ,       torch.Tensor    ):
						_lowercase			:     Optional[int]      					=				timestep.to(self.timesteps.device    )
					_lowercase			:     List[str]      					=				(self.timesteps == timestep).nonzero().item()
					_lowercase			:     List[str]      					=				self.sigmas[step_index]
					_lowercase			:     Union[str, Any]      					=				sample / ((sigma**2 + 1) ** 0.5)
					_lowercase			:     List[Any]      					=				True
					return sample







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							List[Any]      ,       lowerCamelCase_     :							Dict      ,       lowerCamelCase_     :							List[str]    ):

					"""simple docstring"""






					def lms_derivative(lowerCamelCase_     :							Optional[int]    ):
						_lowercase			:     List[Any]      					=				1.0
						for k in range(lowerCamelCase_    ):
							if current_order == k:
								continue
							prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
						return prod

					_lowercase			:     str      					=				integrate.quad(lowerCamelCase_      ,       self.sigmas[t]      ,       self.sigmas[t + 1]      ,       epsrel=1E-4    )[0]

					return integrated_coeff







				def 				__UpperCAmelCase       (			self     :							int      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							Union[str, torch.device] = None    ):

					"""simple docstring"""






					_lowercase			:     str      					=				num_inference_steps

					# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
					if self.config.timestep_spacing == "linspace":
						_lowercase			:     Union[str, Any]      					=				np.linspace(0      ,       self.config.num_train_timesteps - 1      ,       lowerCamelCase_      ,       dtype=lowerCamelCase_    )[
						    ::-1
						].copy()
					elif self.config.timestep_spacing == "leading":
						_lowercase			:     int      					=				self.config.num_train_timesteps // self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						_lowercase			:     int      					=				(np.arange(0      ,       lowerCamelCase_    ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_    )
						timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
						_lowercase			:     Dict      					=				self.config.num_train_timesteps / self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						_lowercase			:     Union[str, Any]      					=				(np.arange(self.config.num_train_timesteps      ,       0      ,       -step_ratio    )).round().copy().astype(lowerCamelCase_    )
						timesteps -= 1
					else:
						raise ValueError(
						    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

					_lowercase			:     Any      					=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
					_lowercase			:     Union[str, Any]      					=				np.log(lowerCamelCase_    )
					_lowercase			:     int      					=				np.interp(lowerCamelCase_      ,       np.arange(0      ,       len(lowerCamelCase_    )    )      ,       lowerCamelCase_    )

					if self.use_karras_sigmas:
						_lowercase			:     int      					=				self._convert_to_karras(in_sigmas=lowerCamelCase_    )
						_lowercase			:     str      					=				np.array([self._sigma_to_t(lowerCamelCase_      ,       lowerCamelCase_    ) for sigma in sigmas]    )

					_lowercase			:     Dict      					=				np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )

					_lowercase			:     List[str]      					=				torch.from_numpy(lowerCamelCase_    ).to(device=lowerCamelCase_    )
					if str(lowerCamelCase_    ).startswith('mps'    ):
						# mps does not support float64
						_lowercase			:     Tuple      					=				torch.from_numpy(lowerCamelCase_    ).to(lowerCamelCase_      ,       dtype=torch.floataa    )
					else:
						_lowercase			:     Dict      					=				torch.from_numpy(lowerCamelCase_    ).to(device=lowerCamelCase_    )

					_lowercase			:     int      					=				[]







				def 				__UpperCAmelCase       (			self     :							Optional[int]      ,       lowerCamelCase_     :							List[str]      ,       lowerCamelCase_     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				np.log(lowerCamelCase_    )

					# get distribution
					_lowercase			:     int      					=				log_sigma - log_sigmas[:, np.newaxis]

					# get sigmas range
					_lowercase			:     str      					=				np.cumsum((dists >= 0)      ,       axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
					_lowercase			:     Union[str, Any]      					=				low_idx + 1

					_lowercase			:     Tuple      					=				log_sigmas[low_idx]
					_lowercase			:     int      					=				log_sigmas[high_idx]

					# interpolate sigmas
					_lowercase			:     Union[str, Any]      					=				(low - log_sigma) / (low - high)
					_lowercase			:     str      					=				np.clip(lowerCamelCase_      ,       0      ,       1    )

					# transform interpolation to time range
					_lowercase			:     Tuple      					=				(1 - w) * low_idx + w * high_idx
					_lowercase			:     Optional[int]      					=				t.reshape(sigma.shape    )
					return t







				def 				__UpperCAmelCase       (			self     :							str      ,       lowerCamelCase_     :							torch.FloatTensor    ):

					"""simple docstring"""






					_lowercase			:     float      					=				in_sigmas[-1].item()
					_lowercase			:     float      					=				in_sigmas[0].item()

					_lowercase			:     Any      					=				7.0  # 7.0 is the value used in the paper
					_lowercase			:     List[Any]      					=				np.linspace(0      ,       1      ,       self.num_inference_steps    )
					_lowercase			:     Any      					=				sigma_min ** (1 / rho)
					_lowercase			:     Dict      					=				sigma_max ** (1 / rho)
					_lowercase			:     List[Any]      					=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
					return sigmas







				def 				__UpperCAmelCase       (			self     :							int      ,       lowerCamelCase_     :							torch.FloatTensor      ,       lowerCamelCase_     :							Union[float, torch.FloatTensor]      ,       lowerCamelCase_     :							torch.FloatTensor      ,       lowerCamelCase_     :							int = 4      ,       lowerCamelCase_     :							bool = True      ,       ):

					"""simple docstring"""






					if not self.is_scale_input_called:
						warnings.warn(
						    '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    ):
						_lowercase			:     Any      					=				timestep.to(self.timesteps.device    )
					_lowercase			:     Any      					=				(self.timesteps == timestep).nonzero().item()
					_lowercase			:     Tuple      					=				self.sigmas[step_index]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
						_lowercase			:     int      					=				sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
						# * c_out + input * c_skip
						_lowercase			:     Any      					=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
					elif self.config.prediction_type == "sample":
						_lowercase			:     Dict      					=				model_output
					else:
						raise ValueError(
						    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

					# 2. Convert to an ODE derivative
					_lowercase			:     int      					=				(sample - pred_original_sample) / sigma
					self.derivatives.append(lowerCamelCase_    )
					if len(self.derivatives    ) > order:
						self.derivatives.pop(0    )

					# 3. Compute linear multistep coefficients
					_lowercase			:     List[Any]      					=				min(step_index + 1      ,       lowerCamelCase_    )
					_lowercase			:     Union[str, Any]      					=				[self.get_lms_coefficient(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    ) for curr_order in range(lowerCamelCase_    )]

					# 4. Compute previous sample based on the derivatives path
					_lowercase			:     Optional[Any]      					=				sample + sum(
					    coeff * derivative for coeff, derivative in zip(lowerCamelCase_      ,       reversed(self.derivatives    )    )    )

					if not return_dict:
						return (prev_sample,)

					return LMSDiscreteSchedulerOutput(prev_sample=lowerCamelCase_      ,       pred_original_sample=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[Any]      ,       lowerCamelCase_     :							torch.FloatTensor      ,       lowerCamelCase_     :							torch.FloatTensor      ,       lowerCamelCase_     :							torch.FloatTensor      ,       ):

					"""simple docstring"""






					_lowercase			:     Optional[Any]      					=				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
						_lowercase			:     Optional[Any]      					=				self.timesteps.to(original_samples.device      ,       dtype=torch.floataa    )
						_lowercase			:     int      					=				timesteps.to(original_samples.device      ,       dtype=torch.floataa    )
					else:
						_lowercase			:     Union[str, Any]      					=				self.timesteps.to(original_samples.device    )
						_lowercase			:     Optional[Any]      					=				timesteps.to(original_samples.device    )

					_lowercase			:     Optional[Any]      					=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

					_lowercase			:     List[Any]      					=				sigmas[step_indices].flatten()
					while len(sigma.shape    ) < len(original_samples.shape    ):
						_lowercase			:     int      					=				sigma.unsqueeze(-1    )

					_lowercase			:     Any      					=				original_samples + noise * sigma
					return noisy_samples







				def __len__(			self     :							str    ):

					"""simple docstring"""






					return self.config.num_train_timesteps




"""simple docstring"""







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


SCREAMING_SNAKE_CASE						=    logging.get_logger(__name__)

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



class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	"regnet"
				_snake_case  =	["x", "y"]







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

					"""simple docstring"""






					super().__init__(**lowerCamelCase_    )
					if layer_type not in self.layer_types:
						raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types    )}'''    )
					_lowercase			:     str      					=				num_channels
					_lowercase			:     Optional[Any]      					=				embedding_size
					_lowercase			:     Union[str, Any]      					=				hidden_sizes
					_lowercase			:     Optional[int]      					=				depths
					_lowercase			:     Optional[int]      					=				groups_width
					_lowercase			:     Tuple      					=				layer_type
					_lowercase			:     Union[str, Any]      					=				hidden_act
					# always downsample in the first stage
					_lowercase			:     Optional[int]      					=				True



