





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    (      UpperCAmelCase_       ):






					"""simple docstring"""



					snake_case_						  =			42
					snake_case_						  =			42






class  lowercase    (      UpperCAmelCase_ ,					UpperCAmelCase_       ):






					"""simple docstring"""



					snake_case_						  =			1





					@register_to_config
					def __init__(    self						:     Dict ,					a_						:     Tuple = 20_00 ,					a_						:     List[str] = 0.1_5 ,					a_						:     int = 0.0_1 ,					a_						:     Union[str, Any] = 13_48.0 ,					a_						:     Tuple = 1e-5 ,					a_						:     Dict = 1 ,					):

						"""simple docstring"""



						lowerCamelCase__										=			sigma_max

						# setable values
						lowerCamelCase__										=			None

						self.set_sigmas(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase							)





					def       _UpperCamelCase       (    self						:     Union[str, Any] ,					a_						:     str ,					a_						:     List[str] = None							):

						"""simple docstring"""



						return sample





					def       _UpperCamelCase       (    self						:     Optional[int] ,					a_						:     Optional[int] ,					a_						:     Optional[Any] = None ,					a_						:     str = None							):

						"""simple docstring"""



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

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





					def       _UpperCamelCase       (    self						:     Tuple ,					a_						:     str ,					a_						:     str = None ,					a_						:     Dict = None ,					a_						:     Tuple = None							):

						"""simple docstring"""



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

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





					def       _UpperCamelCase       (    self						:     List[Any] ,					a_						:     Optional[Any] ,					a_						:     Union[str, Any]							):

						"""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						:     Union[str, Any] ,					a_						:     str ,					a_						:     Tuple ,					a_						:     Tuple ,					a_						:     str = None ,					a_						:     Dict = 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"""							)

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

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

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





					def       _UpperCamelCase       (    self						:     Optional[int] ,					a_						:     Optional[int] ,					a_						:     Tuple ,					a_						:     Optional[Any] = None ,					a_						:     Tuple = 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
						lowerCamelCase__										=			randn_tensor(sample.shape ,					layout=sample.layout ,					generator=__UpperCAmelCase							).to(sample.device							)

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

						if not return_dict:
							return (prev_sample,)

						return SchedulerOutput(prev_sample=__UpperCAmelCase							)





					def       _UpperCamelCase       (    self						:     int ,					a_						:     List[Any] ,					a_						:     int ,					a_						:     Any ,					):

						"""simple docstring"""



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





					def __len__(    self						:     Optional[Any]							):

						"""simple docstring"""



						return self.config.num_train_timesteps







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XCLIPModel""",
      """XCLIPPreTrainedModel""",
      """XCLIPTextModel""",
      """XCLIPVisionModel""",
  ]

if TYPE_CHECKING:
  from .configuration_x_clip import (
      XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      XCLIPConfig,
      XCLIPTextConfig,
      XCLIPVisionConfig,
  )
  from .processing_x_clip import XCLIPProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_x_clip import (
        XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        XCLIPModel,
        XCLIPPreTrainedModel,
        XCLIPTextModel,
        XCLIPVisionModel,
    )

else:
  import sys

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