





# 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'''

					lowerCamelCase     :     torch.FloatTensor
					lowerCamelCase     :     torch.FloatTensor







class   _UpperCamelCase  (       A_						,       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Dict			    =       1





					@register_to_config
					def __init__(						self    :  str ,					__lowercase    :  int = 20_00 ,					__lowercase    :  float = 0.15 ,					__lowercase    :  float = 0.01 ,					__lowercase    :  float = 1348.0 ,					__lowercase    :  float = 1e-5 ,					__lowercase    :  int = 1 ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     sigma_max

						# setable values
						UpperCAmelCase_								=     None

						self.set_sigmas(__lowercase ,					__lowercase ,					__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  Optional[int] = None		):






						'''simple docstring'''







						return sample





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  int ,					__lowercase    :  float = None ,					__lowercase    :  Union[str, torch.device] = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						UpperCAmelCase_								=     torch.linspace(1 ,					__lowercase ,					__lowercase ,					device=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  int ,					__lowercase    :  float = None ,					__lowercase    :  float = None ,					__lowercase    :  float = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     sigma_min if sigma_min is not None else self.config.sigma_min
						UpperCAmelCase_								=     sigma_max if sigma_max is not None else self.config.sigma_max
						UpperCAmelCase_								=     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if self.timesteps is None:
							self.set_timesteps(__lowercase ,					__lowercase		)

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





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  str ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  int ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  Optional[torch.Generator] = None ,					__lowercase    :  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"""		)

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

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

						#  equation 6: sample noise for the diffusion term of
						UpperCAmelCase_								=     randn_tensor(
						    sample.shape ,					layout=sample.layout ,					generator=__lowercase ,					device=sample.device ,					dtype=sample.dtype		)
						UpperCAmelCase_								=     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						UpperCAmelCase_								=     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=__lowercase ,					prev_sample_mean=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  Optional[torch.Generator] = None ,					__lowercase    :  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
						UpperCAmelCase_								=     randn_tensor(sample.shape ,					layout=sample.layout ,					generator=__lowercase		).to(sample.device		)

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

						if not return_dict:
							return (prev_sample,)

						return SchedulerOutput(prev_sample=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  torch.FloatTensor ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     timesteps.to(original_samples.device		)
						UpperCAmelCase_								=     self.discrete_sigmas.to(original_samples.device		)[timesteps]
						UpperCAmelCase_								=     (
						    noise * sigmas[:, None, None, None]
						    if noise is not None
						    else torch.randn_like(__lowercase		) * sigmas[:, None, None, None]
						)
						UpperCAmelCase_								=     noise + original_samples
						return noisy_samples





					def __len__(						self    :  Any		):






						'''simple docstring'''







						return self.config.num_train_timesteps









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__   : List[Any]					  = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : List[str]					  = [
				    """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SEWForCTC""",
				    """SEWForSequenceClassification""",
				    """SEWModel""",
				    """SEWPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_sew import (
								    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SEWForCTC,
								    SEWForSequenceClassification,
								    SEWModel,
								    SEWPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__   : str					  = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


