

# 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      SCREAMING_SNAKE_CASE__  (    _a		):





       '''simple docstring'''




       UpperCamelCase__			:  Any					     =							4_2
       UpperCamelCase__			:  Dict					     =							4_2



class      SCREAMING_SNAKE_CASE__  (    _a			,						_a		):





       '''simple docstring'''




       UpperCamelCase__			:  List[Any]					     =							1

       @register_to_config
       def __init__(    self							:    Union[str, Any]     ,		lowerCAmelCase__							:    Optional[Any] = 2000     ,		lowerCAmelCase__							:    Dict = 0.15     ,		lowerCAmelCase__							:    Tuple = 0.01     ,		lowerCAmelCase__							:    Any = 1348.0     ,		lowerCAmelCase__							:    int = 1E-5     ,		lowerCAmelCase__							:    Optional[Any] = 1     ,		)      ->     Any:
             # standard deviation of the initial noise distribution
             snake_case__											= sigma_max

             # setable values
             snake_case__											= None

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

       def UpperCAmelCase_    (    self							:    Any     ,		lowerCAmelCase__							:    Dict     ,		lowerCAmelCase__							:    int = None				)      ->     Any:
             return sample

       def UpperCAmelCase_    (    self							:    Optional[int]     ,		lowerCAmelCase__							:    List[str]     ,		lowerCAmelCase__							:    Optional[int] = None     ,		lowerCAmelCase__							:    str = None				)      ->     List[Any]:
             snake_case__											= sampling_eps if sampling_eps is not None else self.config.sampling_eps

             snake_case__											= torch.linspace(1     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		device=lowerCamelCase__				)

       def UpperCAmelCase_    (    self							:    List[Any]     ,		lowerCAmelCase__							:    List[Any]     ,		lowerCAmelCase__							:    Optional[Any] = None     ,		lowerCAmelCase__							:    Optional[int] = None     ,		lowerCAmelCase__							:    List[Any] = None				)      ->     Dict:
             snake_case__											= sigma_min if sigma_min is not None else self.config.sigma_min
             snake_case__											= sigma_max if sigma_max is not None else self.config.sigma_max
             snake_case__											= sampling_eps if sampling_eps is not None else self.config.sampling_eps
             if self.timesteps is None:
                   self.set_timesteps(lowerCamelCase__     ,		lowerCamelCase__				)

             snake_case__											= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
             snake_case__											= torch.exp(torch.linspace(math.log(lowerCamelCase__				)     ,		math.log(lowerCamelCase__				)     ,		lowerCamelCase__				)				)
             snake_case__											= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)

       def UpperCAmelCase_    (    self							:    str     ,		lowerCAmelCase__							:    Optional[Any]     ,		lowerCAmelCase__							:    str				)      ->     Tuple:
             return torch.where(
                 timesteps == 0     ,		torch.zeros_like(t.to(timesteps.device				)				)     ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device				)     ,		)

       def UpperCAmelCase_    (    self							:    Any     ,		lowerCAmelCase__							:    Tuple     ,		lowerCAmelCase__							:    Dict     ,		lowerCAmelCase__							:    Optional[int]     ,		lowerCAmelCase__							:    Optional[int] = None     ,		lowerCAmelCase__							:    Dict = True     ,		)      ->     int:
             if self.timesteps is None:
                   raise ValueError(
                       """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

             snake_case__											= timestep * torch.ones(
                 sample.shape[0]     ,		device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
             snake_case__											= (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
             snake_case__											= timesteps.to(self.discrete_sigmas.device				)

             snake_case__											= self.discrete_sigmas[timesteps].to(sample.device				)
             snake_case__											= self.get_adjacent_sigma(lowerCamelCase__     ,		lowerCamelCase__				).to(sample.device				)
             snake_case__											= torch.zeros_like(lowerCamelCase__				)
             snake_case__											= (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
             snake_case__											= diffusion.flatten()
             while len(diffusion.shape				) < len(sample.shape				):
                   snake_case__											= diffusion.unsqueeze(-1				)
             snake_case__											= drift - diffusion**2 * model_output

             #  equation 6: sample noise for the diffusion term of
             snake_case__											= randn_tensor(
                 sample.shape     ,		layout=sample.layout     ,		generator=lowerCamelCase__     ,		device=sample.device     ,		dtype=sample.dtype				)
             snake_case__											= sample - drift  # subtract because `dt` is a small negative timestep
             # TODO is the variable diffusion the correct scaling term for the noise?
             snake_case__											= 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							:    Any     ,		lowerCAmelCase__							:    Any     ,		lowerCAmelCase__							:    int     ,		lowerCAmelCase__							:    List[str] = None     ,		lowerCAmelCase__							:    Tuple = True     ,		)      ->     Dict:
             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
             snake_case__											= randn_tensor(sample.shape     ,		layout=sample.layout     ,		generator=lowerCamelCase__				).to(sample.device				)

             # compute step size from the model_output, the noise, and the snr
             snake_case__											= torch.norm(model_output.reshape(model_output.shape[0]     ,		-1				)     ,		dim=-1				).mean()
             snake_case__											= torch.norm(noise.reshape(noise.shape[0]     ,		-1				)     ,		dim=-1				).mean()
             snake_case__											= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
             snake_case__											= 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
             snake_case__											= step_size.flatten()
             while len(step_size.shape				) < len(sample.shape				):
                   snake_case__											= step_size.unsqueeze(-1				)
             snake_case__											= sample + step_size * model_output
             snake_case__											= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowerCamelCase__				)

       def UpperCAmelCase_    (    self							:    str     ,		lowerCAmelCase__							:    Union[str, Any]     ,		lowerCAmelCase__							:    str     ,		lowerCAmelCase__							:    List[str]     ,		)      ->     int:
             # Make sure sigmas and timesteps have the same device and dtype as original_samples
             snake_case__											= timesteps.to(original_samples.device				)
             snake_case__											= self.discrete_sigmas.to(original_samples.device				)[timesteps]
             snake_case__											= (
                 noise * sigmas[:, None, None, None]
                 if noise is not None
                 else torch.randn_like(lowerCamelCase__				) * sigmas[:, None, None, None]
             )
             snake_case__											= noise + original_samples
             return noisy_samples

       def __len__(    self							:    Union[str, Any]				)      ->     int:
             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__)