"""simple docstring"""



# 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      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					torch.FloatTensor
    UpperCamelCase_	:					torch.FloatTensor






class      _A	(						_UpperCAmelCase							, _UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Any							=					1
    @register_to_config
    def __init__(		self							:     Tuple	,      A_							:     int = 2_000	,      A_							:     float = 0.15	,      A_							:     float = 0.01	,      A_							:     float = 13_48.0	,      A_							:     float = 1E-5	,      A_							:     int = 1	,      )    ->       str:
         # standard deviation of the initial noise distribution
         __snake_case				=							sigma_max

         # setable values
         __snake_case				=							None

         self.set_sigmas(A_	,      A_	,      A_	,      A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     torch.FloatTensor	,      A_							:     Optional[int] = None    )    ->       torch.FloatTensor:
         return sample
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     int	,      A_							:     float = None	,      A_							:     Union[str, torch.device] = None    )    ->       Optional[int]:
         __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  lowercase					(		self							:     int	,      A_							:     int	,      A_							:     float = None	,      A_							:     float = None	,      A_							:     float = None    )    ->       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  lowercase					(		self							:     List[str]	,      A_							:     str	,      A_							:     Optional[Any]    )    ->       Any:
         return torch.where(
             timesteps == 0	,      torch.zeros_like(t.to(timesteps.device    )    )	,      self.discrete_sigmas[timesteps - 1].to(timesteps.device    )	,      )
    def  lowercase					(		self							:     Any	,      A_							:     torch.FloatTensor	,      A_							:     int	,      A_							:     torch.FloatTensor	,      A_							:     Optional[torch.Generator] = None	,      A_							:     bool = 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  lowercase					(		self							:     Any	,      A_							:     torch.FloatTensor	,      A_							:     torch.FloatTensor	,      A_							:     Optional[torch.Generator] = None	,      A_							:     bool = 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  lowercase					(		self							:     Optional[Any]	,      A_							:     torch.FloatTensor	,      A_							:     torch.FloatTensor	,      A_							:     torch.FloatTensor	,      )    ->       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							:     Tuple    )    ->       int:
         return self.config.num_train_timesteps
"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Tuple	,      A_							:     Any    )    ->       Union[str, Any]:
         __snake_case				=							data
         __snake_case				=							None






class      _A	:




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							None
         __snake_case				=							None
    def __iter__(		self							:     Optional[Any]    )    ->       Iterator[Any]:
         __snake_case				=							self.head
         while self.head:
              yield node.data
              __snake_case				=							node.next
              if node == self.head:
                   break
    def __len__(		self							:     List[Any]    )    ->       int:
         return sum(1 for _ in self    )
    def __repr__(		self							:     Optional[int]    )    ->       Union[str, Any]:
         return "->".join(str(A_    ) for item in iter(self    )    )
    def  lowercase					(		self							:     int	,      A_							:     Any    )    ->       None:
         self.insert_nth(len(self    )	,      A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any    )    ->       None:
         self.insert_nth(0	,      A_    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     int	,      A_							:     Any    )    ->       None:
         if index < 0 or index > len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							Node(A_    )
         if self.head is None:
              __snake_case				=							new_node  # first node points itself
              __snake_case				=							__snake_case				=							new_node
         elif index == 0:  # insert at head
              __snake_case				=							self.head
              __snake_case				=							__snake_case				=							new_node
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							new_node
              if index == len(self    ) - 1:  # insert at tail
                   __snake_case				=							new_node
    def  lowercase					(		self							:     int    )    ->       str:
         return self.delete_nth(0    )
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         return self.delete_nth(len(self    ) - 1    )
    def  lowercase					(		self							:     List[str]	,      A_							:     int = 0    )    ->       Any:
         if not 0 <= index < len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							self.head
         if self.head == self.tail:  # just one node
              __snake_case				=							__snake_case				=							None
         elif index == 0:  # delete head node
              __snake_case				=							self.tail.next.next
              __snake_case				=							self.head.next
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							temp.next.next
              if index == len(self    ) - 1:  # delete at tail
                   __snake_case				=							temp
         return delete_node.data







    def  lowercase					(		self							:     Dict    )    ->       bool:
         return len(self    ) == 0






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							CircularLinkedList()
     assert len(snake_case) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5):
          assert len(snake_case) == i
          circular_linked_list.insert_nth(snake_case,      i + 1)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))

     circular_linked_list.insert_tail(6)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      7))
     circular_linked_list.insert_head(0)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(0,      7))

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))
     assert circular_linked_list.delete_nth(2) == 3

     circular_linked_list.insert_nth(2,      3)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
       import doctest

       doctest.testmod()