


"""simple docstring"""






import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     torch.FloatTensor
							__magic_name__					:     Optional[torch.FloatTensor]				  =  None



def 			A__		(					_UpperCAmelCase    :      int				,    _UpperCAmelCase    :      Optional[int]=0.9_9_9				,    _UpperCAmelCase    :      str="cosine"				,    )							->	List[str]:

	'''simple docstring'''


	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_UpperCAmelCase    :      Tuple	):
			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    :      Union[str, Any]	):
			return math.exp(t * -1_2.0	)

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

	snake_case__  :						List[str]		=      []
	for i in range(_UpperCAmelCase	):
		snake_case__  :						str		=      i / num_diffusion_timesteps
		snake_case__  :						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 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  [e.name for e in KarrasDiffusionSchedulers]
							__magic_name__					:     Dict				  =  1




							@register_to_config
							def __init__(						self	,					lowerCamelCase__ = 1_000	,					lowerCamelCase__ = 0.00_01	,					lowerCamelCase__ = 0.02	,					lowerCamelCase__ = "linear"	,					lowerCamelCase__ = None	,					lowerCamelCase__ = "epsilon"	,					lowerCamelCase__ = "linspace"	,					lowerCamelCase__ = 0	,					)   ->	Optional[Any]:







								'''simple docstring'''




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

								snake_case__  :						Dict		=      1.0 - self.betas
								snake_case__  :						Tuple		=      torch.cumprod(self.alphas	,					dim=0)

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

								# setable values
								snake_case__  :						Union[str, Any]		=      None
								snake_case__  :						Union[str, Any]		=      np.linspace(0	,					num_train_timesteps - 1	,					lowerCamelCase__	,					dtype=lowerCamelCase__)[::-1].copy()
								snake_case__  :						Optional[int]		=      torch.from_numpy(lowerCamelCase__)
								snake_case__  :						Tuple		=      False




							@property
							def        UpperCAmelCase (						self)   ->	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	,					lowerCamelCase__	,					lowerCamelCase__)   ->	torch.FloatTensor:







								'''simple docstring'''




								if isinstance(lowerCamelCase__	,					torch.Tensor):
									snake_case__  :						Dict		=      timestep.to(self.timesteps.device)
								snake_case__  :						Dict		=      (self.timesteps == timestep).nonzero().item()
								snake_case__  :						int		=      self.sigmas[step_index]
								snake_case__  :						str		=      sample / ((sigma**2 + 1) ** 0.5)
								snake_case__  :						Tuple		=      True
								return sample




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	int:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      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":
									snake_case__  :						Optional[int]		=      np.linspace(0	,					self.config.num_train_timesteps - 1	,					lowerCamelCase__	,					dtype=lowerCamelCase__)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									snake_case__  :						Optional[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
									snake_case__  :						Optional[Any]		=      (np.arange(0	,					lowerCamelCase__) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									snake_case__  :						Optional[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
									snake_case__  :						Tuple		=      (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'.""")

								snake_case__  :						List[Any]		=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								snake_case__  :						List[str]		=      np.interp(lowerCamelCase__	,					np.arange(0	,					len(lowerCamelCase__))	,					lowerCamelCase__)
								snake_case__  :						List[Any]		=      np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								snake_case__  :						Optional[Any]		=      torch.from_numpy(lowerCamelCase__).to(device=lowerCamelCase__)
								if str(lowerCamelCase__).startswith("mps"):
									# mps does not support float64
									snake_case__  :						int		=      torch.from_numpy(lowerCamelCase__).to(lowerCamelCase__	,					dtype=torch.floataa)
								else:
									snake_case__  :						Optional[int]		=      torch.from_numpy(lowerCamelCase__).to(device=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = None	,					lowerCamelCase__ = True	,					)   ->	Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:







								'''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"
									        " `EulerDiscreteScheduler.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):
									snake_case__  :						Tuple		=      timestep.to(self.timesteps.device)

								snake_case__  :						List[str]		=      (self.timesteps == timestep).nonzero().item()
								snake_case__  :						Dict		=      self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									snake_case__  :						Optional[Any]		=      sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									snake_case__  :						Optional[int]		=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
									raise NotImplementedError("prediction_type not implemented yet: sample")
								else:
									raise ValueError(
									    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

								snake_case__  :						List[Any]		=      self.sigmas[step_index]
								snake_case__  :						List[str]		=      self.sigmas[step_index + 1]
								snake_case__  :						Any		=      (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								snake_case__  :						Union[str, Any]		=      (sigma_to**2 - sigma_up**2) ** 0.5

								# 2. Convert to an ODE derivative
								snake_case__  :						Union[str, Any]		=      (sample - pred_original_sample) / sigma

								snake_case__  :						Union[str, Any]		=      sigma_down - sigma

								snake_case__  :						List[Any]		=      sample + derivative * dt

								snake_case__  :						List[str]		=      model_output.device
								snake_case__  :						Optional[Any]		=      randn_tensor(model_output.shape	,					dtype=model_output.dtype	,					device=lowerCamelCase__	,					generator=lowerCamelCase__)

								snake_case__  :						List[Any]		=      prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=lowerCamelCase__	,					pred_original_sample=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	torch.FloatTensor:







								'''simple docstring'''




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

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

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

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







							def __len__(						self)   ->	Optional[int]:







								'''simple docstring'''




								return self.config.num_train_timesteps









"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase  		=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """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

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





