
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 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							42
							UpperCamelCase_					   =							None



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=0.999				,    __SCREAMING_SNAKE_CASE="cosine"				,    )	->     Optional[int]:
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__SCREAMING_SNAKE_CASE ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(__SCREAMING_SNAKE_CASE ):
									return math.exp(t * -12.0 )

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

			UpperCamelCase__    :		List[Any]     =				[]
			for i in range(__SCREAMING_SNAKE_CASE ):
						UpperCamelCase__    :		str     =				i / num_diffusion_timesteps
						UpperCamelCase__    :		Union[str, Any]     =				(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(__SCREAMING_SNAKE_CASE ) / alpha_bar_fn(__SCREAMING_SNAKE_CASE )				,    __SCREAMING_SNAKE_CASE ) )
			return torch.tensor(__SCREAMING_SNAKE_CASE				,    dtype=torch.floataa )



class 			UpperCamelCase_			(						__UpperCamelCase	,     __UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							[e.name for e in KarrasDiffusionSchedulers]
							UpperCamelCase_					   =							1
							@register_to_config
							def __init__(				self     ,						UpperCamelCase = 10_00     ,						UpperCamelCase = 0.0001     ,						UpperCamelCase = 0.02     ,						UpperCamelCase = "linear"     ,						UpperCamelCase = None     ,						UpperCamelCase = False     ,						UpperCamelCase = "epsilon"     ,						UpperCamelCase = "linspace"     ,						UpperCamelCase = 0     ,						) ->	Dict:
										if trained_betas is not None:
													UpperCamelCase__    :		List[str]     =				torch.tensor(UpperCamelCase     ,						dtype=torch.floataa)
										elif beta_schedule == "linear":
													UpperCamelCase__    :		Any     =				torch.linspace(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						dtype=torch.floataa)
										elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													UpperCamelCase__    :		Tuple     =				(
													    torch.linspace(beta_start**0.5     ,						beta_end**0.5     ,						UpperCamelCase     ,						dtype=torch.floataa) ** 2
													)
										elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													UpperCamelCase__    :		List[Any]     =				betas_for_alpha_bar(UpperCamelCase)
										else:
													raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

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

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

										# setable values
										UpperCamelCase__    :		Optional[int]     =				None
										UpperCamelCase__    :		Union[str, Any]     =				use_karras_sigmas
										self.set_timesteps(UpperCamelCase     ,						UpperCamelCase)
										UpperCamelCase__    :		str     =				[]
										UpperCamelCase__    :		Tuple     =				False
							@property
							def        lowerCAmelCase__ (				self) ->	Dict:
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase) ->	torch.FloatTensor:
										if isinstance(UpperCamelCase     ,						torch.Tensor):
													UpperCamelCase__    :		Tuple     =				timestep.to(self.timesteps.device)
										UpperCamelCase__    :		Any     =				(self.timesteps == timestep).nonzero().item()
										UpperCamelCase__    :		Optional[Any]     =				self.sigmas[step_index]
										UpperCamelCase__    :		Any     =				sample / ((sigma**2 + 1) ** 0.5)
										UpperCamelCase__    :		Tuple     =				True
										return sample
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	List[str]:

										def lms_derivative(UpperCamelCase):
													UpperCamelCase__    :		Any     =				1.0
													for k in range(UpperCamelCase):
																if current_order == k:
																			continue
																prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
													return prod

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

										return integrated_coeff
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None) ->	Optional[Any]:
										UpperCamelCase__    :		int     =				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":
													UpperCamelCase__    :		Tuple     =				np.linspace(0     ,						self.config.num_train_timesteps - 1     ,						UpperCamelCase     ,						dtype=UpperCamelCase)[
													    ::-1
													].copy()
										elif self.config.timestep_spacing == "leading":
													UpperCamelCase__    :		List[Any]     =				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
													UpperCamelCase__    :		List[str]     =				(np.arange(0     ,						UpperCamelCase) * step_ratio).round()[::-1].copy().astype(UpperCamelCase)
													timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
													UpperCamelCase__    :		Any     =				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
													UpperCamelCase__    :		Tuple     =				(np.arange(self.config.num_train_timesteps     ,						0     ,						-step_ratio)).round().copy().astype(UpperCamelCase)
													timesteps -= 1
										else:
													raise ValueError(
													    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

										UpperCamelCase__    :		Tuple     =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
										UpperCamelCase__    :		List[str]     =				np.log(UpperCamelCase)
										UpperCamelCase__    :		Dict     =				np.interp(UpperCamelCase     ,						np.arange(0     ,						len(UpperCamelCase))     ,						UpperCamelCase)

										if self.use_karras_sigmas:
													UpperCamelCase__    :		List[str]     =				self._convert_to_karras(in_sigmas=UpperCamelCase)
													UpperCamelCase__    :		List[Any]     =				np.array([self._sigma_to_t(UpperCamelCase     ,						UpperCamelCase) for sigma in sigmas])

										UpperCamelCase__    :		List[str]     =				np.concatenate([sigmas, [0.0]]).astype(np.floataa)

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

										UpperCamelCase__    :		Tuple     =				[]
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
										# get log sigma
										UpperCamelCase__    :		Union[str, Any]     =				np.log(UpperCamelCase)

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

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

										UpperCamelCase__    :		List[str]     =				log_sigmas[low_idx]
										UpperCamelCase__    :		List[str]     =				log_sigmas[high_idx]

										# interpolate sigmas
										UpperCamelCase__    :		List[Any]     =				(low - log_sigma) / (low - high)
										UpperCamelCase__    :		Tuple     =				np.clip(UpperCamelCase     ,						0     ,						1)

										# transform interpolation to time range
										UpperCamelCase__    :		Optional[int]     =				(1 - w) * low_idx + w * high_idx
										UpperCamelCase__    :		Tuple     =				t.reshape(sigma.shape)
										return t
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	torch.FloatTensor:
										UpperCamelCase__    :		float     =				in_sigmas[-1].item()
										UpperCamelCase__    :		float     =				in_sigmas[0].item()

										UpperCamelCase__    :		str     =				7.0  # 7.0 is the value used in the paper
										UpperCamelCase__    :		List[str]     =				np.linspace(0     ,						1     ,						self.num_inference_steps)
										UpperCamelCase__    :		int     =				sigma_min ** (1 / rho)
										UpperCamelCase__    :		Union[str, Any]     =				sigma_max ** (1 / rho)
										UpperCamelCase__    :		Any     =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase = 4     ,						UpperCamelCase = True     ,						) ->	Union[LMSDiscreteSchedulerOutput, Tuple]:
										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(UpperCamelCase     ,						torch.Tensor):
													UpperCamelCase__    :		List[Any]     =				timestep.to(self.timesteps.device)
										UpperCamelCase__    :		Union[str, Any]     =				(self.timesteps == timestep).nonzero().item()
										UpperCamelCase__    :		Any     =				self.sigmas[step_index]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
													UpperCamelCase__    :		Any     =				sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													UpperCamelCase__    :		Optional[Any]     =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										elif self.config.prediction_type == "sample":
													UpperCamelCase__    :		List[Any]     =				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
										UpperCamelCase__    :		Tuple     =				(sample - pred_original_sample) / sigma
										self.derivatives.append(UpperCamelCase)
										if len(self.derivatives) > order:
													self.derivatives.pop(0)

										# 3. Compute linear multistep coefficients
										UpperCamelCase__    :		List[str]     =				min(step_index + 1     ,						UpperCamelCase)
										UpperCamelCase__    :		Tuple     =				[self.get_lms_coefficient(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) for curr_order in range(UpperCamelCase)]

										# 4. Compute previous sample based on the derivatives path
										UpperCamelCase__    :		List[str]     =				sample + sum(
										    coeff * derivative for coeff, derivative in zip(UpperCamelCase     ,						reversed(self.derivatives)))

										if not return_dict:
													return (prev_sample,)

										return LMSDiscreteSchedulerOutput(prev_sample=UpperCamelCase     ,						pred_original_sample=UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						) ->	torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										UpperCamelCase__    :		Tuple     =				self.sigmas.to(device=original_samples.device     ,						dtype=original_samples.dtype)
										if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase):
													# mps does not support float64
													UpperCamelCase__    :		Dict     =				self.timesteps.to(original_samples.device     ,						dtype=torch.floataa)
													UpperCamelCase__    :		List[Any]     =				timesteps.to(original_samples.device     ,						dtype=torch.floataa)
										else:
													UpperCamelCase__    :		List[Any]     =				self.timesteps.to(original_samples.device)
													UpperCamelCase__    :		List[Any]     =				timesteps.to(original_samples.device)

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

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

										UpperCamelCase__    :		Optional[Any]     =				original_samples + noise * sigma
										return noisy_samples






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





from typing import TYPE_CHECKING

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


UpperCAmelCase__				:  str       					=				{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__				:  List[Any]       					=				[
							    '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MobileViTV2ForImageClassification''',
							    '''MobileViTV2ForSemanticSegmentation''',
							    '''MobileViTV2Model''',
							    '''MobileViTV2PreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_mobilevitva import (
							    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileViTVaConfig,
							    MobileViTVaOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilevitva import (
														    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileViTVaForImageClassification,
														    MobileViTVaForSemanticSegmentation,
														    MobileViTVaModel,
														    MobileViTVaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__				:  List[str]       					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



