

"""simple docstring"""
from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class 			lowerCamelCase  :


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     ('_front', '_back', '_len')

  @dataclass
  class 			lowerCamelCase  :


    '''simple docstring'''







    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None




  class 			lowerCamelCase  :


    '''simple docstring'''







    SCREAMING_SNAKE_CASE							=     ('_cur',)



    def __init__(self       ,					_lowerCamelCase  ):


          """simple docstring"""

          UpperCAmelCase__ :       Union[str, Any]						=						cur



    def __iter__(self  ):


          """simple docstring"""

          return self




    def    _a			(self  ):


          """simple docstring"""

          if self._cur is None:
                # finished iterating
                raise StopIteration
          UpperCAmelCase__ :       str						=						self._cur.val
          UpperCAmelCase__ :       str						=						self._cur.next_node

          return val




  def __init__(self       ,					_lowerCamelCase = None  ):


        """simple docstring"""

        UpperCAmelCase__ :       Any						=						None
        UpperCAmelCase__ :       Any						=						None
        UpperCAmelCase__ :       int						=						0

        if iterable is not None:
              # append every value to the deque
              for val in iterable:
                    self.append(_lowerCamelCase  )



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						self._Node(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
        if self.is_empty():
              # front = back
              UpperCAmelCase__ :       List[str]						=						node
              UpperCAmelCase__ :       Dict						=						1
        else:
              # connect nodes
              UpperCAmelCase__ :       str						=						node
              UpperCAmelCase__ :       List[Any]						=						self._back
              UpperCAmelCase__ :       List[Any]						=						node  # assign new back to the new node

              self._len += 1

              # make sure there were no errors
              assert not self.is_empty(), "Error on appending value."



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						self._Node(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
        if self.is_empty():
              # front = back
              UpperCAmelCase__ :       Optional[int]						=						node
              UpperCAmelCase__ :       int						=						1
        else:
              # connect nodes
              UpperCAmelCase__ :       Union[str, Any]						=						self._front
              UpperCAmelCase__ :       Optional[int]						=						node
              UpperCAmelCase__ :       List[Any]						=						node  # assign new front to the new node

              self._len += 1

              # make sure there were no errors
              assert not self.is_empty(), "Error on appending value."



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        for val in iterable:
              self.append(_lowerCamelCase  )



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        for val in iterable:
              self.appendleft(_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        assert not self.is_empty(), "Deque is empty."

        UpperCAmelCase__ :       Optional[Any]						=						self._back
        UpperCAmelCase__ :       Any						=						self._back.prev_node  # set new back
        # drop the last node - python will deallocate memory automatically
        UpperCAmelCase__ :       Optional[Any]						=						None

        self._len -= 1

        return topop.val



  def    _a			(self  ):


        """simple docstring"""

        assert not self.is_empty(), "Deque is empty."

        UpperCAmelCase__ :       Optional[Any]						=						self._front
        UpperCAmelCase__ :       str						=						self._front.next_node  # set new front and drop the first node
        UpperCAmelCase__ :       Optional[int]						=						None

        self._len -= 1

        return topop.val



  def    _a			(self  ):


        """simple docstring"""

        return self._front is None



  def __len__(self  ):


        """simple docstring"""

        return self._len



  def __eq__(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        if not isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
              return NotImplemented

        UpperCAmelCase__ :       str						=						self._front
        UpperCAmelCase__ :       str						=						other._front

        # if the length of the dequeues are not the same, they are not equal
        if len(self  ) != len(_lowerCamelCase  ):
              return False

        while me is not None and oth is not None:
              # compare every value
              if me.val != oth.val:
                    return False
              UpperCAmelCase__ :       List[Any]						=						me.next_node
              UpperCAmelCase__ :       int						=						oth.next_node

        return True



  def __iter__(self  ):


        """simple docstring"""

        return Deque._Iterator(self._front  )




  def __repr__(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						[]
        UpperCAmelCase__ :       Union[str, Any]						=						self._front
        while aux is not None:
              # append the values in a list to display
              values_list.append(aux.val  )
              UpperCAmelCase__ :       str						=						aux.next_node

        return F"""[{', '.join(repr(_lowerCamelCase  ) for val in values_list  )}]"""


if __name__ == "__main__":
    import doctest

    doctest.testmod()




"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
  SCREAMING_SNAKE_CASE							=     ()



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						4
        UpperCAmelCase__ :       Tuple						=						3
        UpperCAmelCase__ :       Optional[Any]						=						8
        UpperCAmelCase__ :       Dict						=						8

        UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )

        return sample



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						4
        UpperCAmelCase__ :       Optional[Any]						=						3
        UpperCAmelCase__ :       List[str]						=						8
        UpperCAmelCase__ :       Dict						=						8

        UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
        UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
        UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
        UpperCAmelCase__ :       Dict						=						sample / num_elems
        UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )

        return sample



  def    _a			(self  ):


        """simple docstring"""

        def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
              return sample * t / (t + 1)

        return model



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						{
            """num_train_timesteps""": 2000,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1348,
            """sampling_eps""": 1e-5,
        }

        config.update(**_lowerCamelCase  )
        return config



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       int						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

              UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
              UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

              UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
        kwargs.update(_lowerCamelCase  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       List[str]						=						self.dummy_sample
              UpperCAmelCase__ :       List[str]						=						0.1 * sample

              UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
              UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

              UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"



  def    _a			(self  ):


        """simple docstring"""

        for timesteps in [10, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
              self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        for t in [0.1, 0.5, 0.75]:
              self.check_over_forward(time_step=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )

        UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
        UpperCAmelCase__ :       int						=						self.get_scheduler_config()
        UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

        UpperCAmelCase__ :       List[str]						=						3

        UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
        UpperCAmelCase__ :       int						=						self.dummy_sample_deter

        scheduler.set_sigmas(_lowerCamelCase  )
        scheduler.set_timesteps(_lowerCamelCase  )
        UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]

              for _ in range(scheduler.config.correct_steps  ):
                    with torch.no_grad():
                          UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                    UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample

              with torch.no_grad():
                    UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )

              UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
              UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean

        UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
        UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )

        assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
        assert np.isclose(result_mean.item()       ,					18_714_530.0  )




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )

        UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
              UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )

              UpperCAmelCase__ :       str						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

              if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                    scheduler.set_timesteps(_lowerCamelCase  )
              elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                    UpperCAmelCase__ :       Dict						=						num_inference_steps

              UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              self.assertEqual(output_a.shape       ,					sample.shape  )
              self.assertEqual(output_a.shape       ,					output_a.shape  )

