

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42
					__magic_name__        =   42

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					__magic_name__        =   1





					@register_to_config
					def __init__(				self				,      lowerCamelCase__ = 2_0_0_0				,      lowerCamelCase__ = 0.15				,      lowerCamelCase__ = 0.01				,      lowerCamelCase__ = 1_348.0				,      lowerCamelCase__ = 1e-5				,      lowerCamelCase__ = 1				,      ):
						# standard deviation of the initial noise distribution
						UpperCAmelCase__:	Tuple									= sigma_max

						# setable values
						UpperCAmelCase__:	int									= None

						self.set_sigmas(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						return sample





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None						):
						UpperCAmelCase__:	int									= sampling_eps if sampling_eps is not None else self.config.sampling_eps

						UpperCAmelCase__:	Any									= torch.linspace(1				,      lowerCamelCase__				,      lowerCamelCase__				,      device=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None						):
						UpperCAmelCase__:	Union[str, Any]									= sigma_min if sigma_min is not None else self.config.sigma_min
						UpperCAmelCase__:	Optional[Any]									= sigma_max if sigma_max is not None else self.config.sigma_max
						UpperCAmelCase__:	List[str]									= sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if self.timesteps is None:
							self.set_timesteps(lowerCamelCase__				,      lowerCamelCase__						)

						UpperCAmelCase__:	Optional[int]									= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
						UpperCAmelCase__:	List[Any]									= torch.exp(torch.linspace(math.log(lowerCamelCase__						)				,      math.log(lowerCamelCase__						)				,      lowerCamelCase__						)						)
						UpperCAmelCase__:	str									= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__						):
						return torch.where(
						    timesteps == 0				,      torch.zeros_like(t.to(timesteps.device						)						)				,      self.discrete_sigmas[timesteps - 1].to(timesteps.device						)				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = True				,      ):
						if self.timesteps is None:
							raise ValueError(
							    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)

						UpperCAmelCase__:	Optional[int]									= timestep * torch.ones(
						    sample.shape[0]				,      device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
						UpperCAmelCase__:	Optional[Any]									= (timestep * (len(self.timesteps						) - 1)).long()

						# mps requires indices to be in the same device, so we use cpu as is the default with cuda
						UpperCAmelCase__:	Any									= timesteps.to(self.discrete_sigmas.device						)

						UpperCAmelCase__:	Tuple									= self.discrete_sigmas[timesteps].to(sample.device						)
						UpperCAmelCase__:	int									= self.get_adjacent_sigma(lowerCamelCase__				,      lowerCamelCase__						).to(sample.device						)
						UpperCAmelCase__:	str									= torch.zeros_like(lowerCamelCase__						)
						UpperCAmelCase__:	str									= (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						UpperCAmelCase__:	Any									= diffusion.flatten()
						while len(diffusion.shape						) < len(sample.shape						):
							UpperCAmelCase__:	Tuple									= diffusion.unsqueeze(-1						)
						UpperCAmelCase__:	Tuple									= drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						UpperCAmelCase__:	Union[str, Any]									= randn_tensor(
						    sample.shape				,      layout=sample.layout				,      generator=lowerCamelCase__				,      device=sample.device				,      dtype=sample.dtype						)
						UpperCAmelCase__:	Tuple									= sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						UpperCAmelCase__:	List[str]									= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
							return (prev_sample, prev_sample_mean)

						return SdeVeOutput(prev_sample=lowerCamelCase__				,      prev_sample_mean=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = True				,      ):
						if self.timesteps is None:
							raise ValueError(
							    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						UpperCAmelCase__:	List[Any]									= randn_tensor(sample.shape				,      layout=sample.layout				,      generator=lowerCamelCase__						).to(sample.device						)

						# compute step size from the model_output, the noise, and the snr
						UpperCAmelCase__:	List[Any]									= torch.norm(model_output.reshape(model_output.shape[0]				,      -1						)				,      dim=-1						).mean()
						UpperCAmelCase__:	Optional[Any]									= torch.norm(noise.reshape(noise.shape[0]				,      -1						)				,      dim=-1						).mean()
						UpperCAmelCase__:	str									= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						UpperCAmelCase__:	Optional[Any]									= step_size * torch.ones(sample.shape[0]						).to(sample.device						)
						# self.repeat_scalar(step_size, sample.shape[0])

						# compute corrected sample: model_output term and noise term
						UpperCAmelCase__:	Tuple									= step_size.flatten()
						while len(step_size.shape						) < len(sample.shape						):
							UpperCAmelCase__:	Union[str, Any]									= step_size.unsqueeze(-1						)
						UpperCAmelCase__:	int									= sample + step_size * model_output
						UpperCAmelCase__:	Tuple									= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
							return (prev_sample,)

						return SchedulerOutput(prev_sample=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						UpperCAmelCase__:	Optional[Any]									= timesteps.to(original_samples.device						)
						UpperCAmelCase__:	Any									= self.discrete_sigmas.to(original_samples.device						)[timesteps]
						UpperCAmelCase__:	Dict									= (
						    noise * sigmas[:, None, None, None]
						    if noise is not None
						    else torch.randn_like(lowerCamelCase__						) * sigmas[:, None, None, None]
						)
						UpperCAmelCase__:	Optional[Any]									= noise + original_samples
						return noisy_samples





					def __len__(				self						):
						return self.config.num_train_timesteps


from typing import TYPE_CHECKING

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


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)