





# 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 			lowercase				(							a_     ):



      """simple docstring"""

      _UpperCamelCase  :	torch.FloatTensor
      _UpperCamelCase  :	torch.FloatTensor



class 			lowercase				(							a_		,			a_     ):



      """simple docstring"""

      _UpperCamelCase  :	Union[str, Any]								=	1







      @register_to_config
      def __init__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		int = 20_00 ,				lowerCamelCase_			:		float = 0.15 ,				lowerCamelCase_			:		float = 0.01 ,				lowerCamelCase_			:		float = 1348.0 ,				lowerCamelCase_			:		float = 1e-5 ,				lowerCamelCase_			:		int = 1 ,				):





          '''simple docstring'''







          _snake_case :			Dict  						=   sigma_max

          # setable values
          _snake_case :			Union[str, Any]  						=   None

          self.set_sigmas(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_					)







      def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		Optional[int] = None					):





          '''simple docstring'''







          return sample







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





          '''simple docstring'''







          _snake_case :			Optional[Any]  						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

          _snake_case :			Optional[int]  						=   torch.linspace(1 ,				lowerCamelCase_ ,				lowerCamelCase_ ,				device=lowerCamelCase_					)







      def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float = None ,				lowerCamelCase_			:		float = None ,				lowerCamelCase_			:		float = None					):





          '''simple docstring'''







          _snake_case :			Dict  						=   sigma_min if sigma_min is not None else self.config.sigma_min
          _snake_case :			Tuple  						=   sigma_max if sigma_max is not None else self.config.sigma_max
          _snake_case :			Optional[Any]  						=   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 :			List[Any]  						=   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
          _snake_case :			Tuple  						=   torch.exp(torch.linspace(math.log(lowerCamelCase_					) ,				math.log(lowerCamelCase_					) ,				lowerCamelCase_					)					)
          _snake_case :			List[Any]  						=   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]					)







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





          '''simple docstring'''







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







      def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		Optional[torch.Generator] = None ,				lowerCamelCase_			:		bool = True ,				):





          '''simple docstring'''







          if self.timesteps is None:
              raise ValueError(
                  '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

          _snake_case :			Tuple  						=   timestep * torch.ones(
              sample.shape[0] ,				device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
          _snake_case :			Dict  						=   (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 :			Optional[int]  						=   timesteps.to(self.discrete_sigmas.device					)

          _snake_case :			Tuple  						=   self.discrete_sigmas[timesteps].to(sample.device					)
          _snake_case :			Optional[Any]  						=   self.get_adjacent_sigma(lowerCamelCase_ ,				lowerCamelCase_					).to(sample.device					)
          _snake_case :			List[str]  						=   torch.zeros_like(lowerCamelCase_					)
          _snake_case :			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
          _snake_case :			Optional[int]  						=   diffusion.flatten()
          while len(diffusion.shape					) < len(sample.shape					):
              _snake_case :			List[Any]  						=   diffusion.unsqueeze(-1					)
          _snake_case :			Tuple  						=   drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          _snake_case :			Optional[int]  						=   randn_tensor(
              sample.shape ,				layout=sample.layout ,				generator=lowerCamelCase_ ,				device=sample.device ,				dtype=sample.dtype					)
          _snake_case :			Tuple  						=   sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          _snake_case :			Union[str, Any]  						=   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_			:		torch.FloatTensor ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		Optional[torch.Generator] = None ,				lowerCamelCase_			:		bool = True ,				):





          '''simple docstring'''







          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 :			Optional[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
          _snake_case :			Optional[int]  						=   torch.norm(model_output.reshape(model_output.shape[0] ,				-1					) ,				dim=-1					).mean()
          _snake_case :			Any  						=   torch.norm(noise.reshape(noise.shape[0] ,				-1					) ,				dim=-1					).mean()
          _snake_case :			str  						=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          _snake_case :			int  						=   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 :			Tuple  						=   step_size.flatten()
          while len(step_size.shape					) < len(sample.shape					):
              _snake_case :			Dict  						=   step_size.unsqueeze(-1					)
          _snake_case :			Dict  						=   sample + step_size * model_output
          _snake_case :			Optional[Any]  						=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
              return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCamelCase_					)







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





          '''simple docstring'''







          _snake_case :			Union[str, Any]  						=   timesteps.to(original_samples.device					)
          _snake_case :			Optional[Any]  						=   self.discrete_sigmas.to(original_samples.device					)[timesteps]
          _snake_case :			Dict  						=   (
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(lowerCamelCase_					) * sigmas[:, None, None, None]
          )
          _snake_case :			int  						=   noise + original_samples
          return noisy_samples







      def __len__(    self			:		Dict					):





          '''simple docstring'''







          return self.config.num_train_timesteps













from typing import TYPE_CHECKING

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


lowercase_		:				Optional[int]				=						{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Any				=						[
		    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MegaForCausalLM''',
		    '''MegaForMaskedLM''',
		    '''MegaForMultipleChoice''',
		    '''MegaForQuestionAnswering''',
		    '''MegaForSequenceClassification''',
		    '''MegaForTokenClassification''',
		    '''MegaModel''',
		    '''MegaPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

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






