

# 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_   (  __lowercase					):
      UpperCamelCase_      :		str										=    4_2
      UpperCamelCase_      :		Any										=    4_2



class   lowercase_   (  __lowercase       ,							__lowercase					):
      UpperCamelCase_      :		Tuple										=    1



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

        # setable values
        _snake_case						=							None

        self.set_sigmas(_a      , _a      , _a      , _a		)



      def 						UpperCamelCase_		(      self				:    str      , A__				:    Optional[Any]      , A__				:    Dict = None		)   ->					torch.FloatTensor:
        return sample



      def 						UpperCamelCase_		(      self				:    int      , A__				:    str      , A__				:    List[str] = None      , A__				:    Union[str, Any] = None		)   ->					Any:
        _snake_case						=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _snake_case						=							torch.linspace(1      , _a      , _a      , device=_a		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Optional[int]      , A__				:    int = None      , A__				:    str = None      , A__				:    Optional[int] = None		)   ->					Optional[int]:
        _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(_a      , _a		)

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



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Tuple      , A__				:    Union[str, Any]		)   ->					int:
        return torch.where(
            timesteps == 0      , torch.zeros_like(t.to(timesteps.device		)		)      , self.discrete_sigmas[timesteps - 1].to(timesteps.device		)      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    int      , A__				:    Union[str, Any]      , A__				:    Dict      , A__				:    Dict = None      , A__				:    Union[str, Any] = True      , )   ->					Union[SdeVeOutput, Tuple]:
        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(_a      , _a		).to(sample.device		)
        _snake_case						=							torch.zeros_like(_a		)
        _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=_a      , 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=_a      , prev_sample_mean=_a		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    List[str]      , A__				:    int      , A__				:    str = None      , A__				:    Optional[Any] = True      , )   ->					Union[SchedulerOutput, Tuple]:
        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=_a		).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=_a		)



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Optional[int]      , A__				:    Optional[int]      , A__				:    Optional[Any]      , )   ->					torch.FloatTensor:
        # 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(_a		) * sigmas[:, None, None, None]
        )
        _snake_case						=							noise + original_samples
        return noisy_samples







      def __len__(      self				:    Optional[Any]		)   ->					Union[str, Any]:
        return self.config.num_train_timesteps




__A      		=     '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''



def 				snake_case_(_UpperCamelCase					)							->     bytes:



  """simple docstring"""






  if not isinstance(_UpperCamelCase			,	_UpperCamelCase					):
    _snake_case						=							F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
    raise TypeError(_UpperCamelCase					)

  _snake_case						=							''''''.join(bin(_UpperCamelCase					)[2:].zfill(8					) for byte in data					)

  _snake_case						=							len(_UpperCamelCase					) % 6 != 0

  if padding_needed:
    # The padding that will be added later
    _snake_case						=							b'''=''' * ((6 - len(_UpperCamelCase					) % 6) // 2)

    # Append binary_stream with arbitrary binary digits (0's by default) to make its
    # length a multiple of 6.
    binary_stream += "0" * (6 - len(_UpperCamelCase					) % 6)
  else:
    _snake_case						=							b''''''

  # Encode every 6 binary digits to their corresponding Base64 character
  return (
      "".join(
          B64_CHARSET[int(binary_stream[index : index + 6]			,	2					)]
          for index in range(0			,	len(_UpperCamelCase					)			,	6					)					).encode()
      + padding
  )



def 				snake_case_(_UpperCamelCase					)							->     bytes:



  """simple docstring"""






  if not isinstance(_UpperCamelCase			,	_UpperCamelCase					) and not isinstance(_UpperCamelCase			,	_UpperCamelCase					):
    _snake_case						=							(
        '''argument should be a bytes-like object or ASCII string, '''
        F"""not '{encoded_data.__class__.__name__}'"""
    )
    raise TypeError(_UpperCamelCase					)

  # In case encoded_data is a bytes-like object, make sure it contains only
  # ASCII characters so we convert it to a string object
  if isinstance(_UpperCamelCase			,	_UpperCamelCase					):
    try:
      _snake_case						=							encoded_data.decode('''utf-8'''					)
    except UnicodeDecodeError:
      raise ValueError('''base64 encoded data should only contain ASCII characters'''					)

  _snake_case						=							encoded_data.count('''='''					)

  # Check if the encoded string contains non base64 characters
  if padding:
    assert all(
        char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
  else:
    assert all(
        char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

  # Check the padding
  assert len(_UpperCamelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

  if padding:
    # Remove padding if there is one
    _snake_case						=							encoded_data[:-padding]

    _snake_case						=							''''''.join(
        bin(B64_CHARSET.index(_UpperCamelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
  else:
    _snake_case						=							''''''.join(
        bin(B64_CHARSET.index(_UpperCamelCase					)					)[2:].zfill(6					) for char in encoded_data					)

  _snake_case						=							[
      int(binary_stream[index : index + 8]			,	2					)
      for index in range(0			,	len(_UpperCamelCase					)			,	8					)
  ]

  return bytes(_UpperCamelCase					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

