





'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				(UniPCMultistepScheduler,)
  lowerCAmelCase_					=				(("""num_inference_steps""", 25),)


  def   UpperCamelCase__      (				self    :					int							,	**UpperCamelCase__    :					Optional[int]  )     ->						Optional[int]:
    _UpperCamelCase       			={
        '''num_train_timesteps''': 1000,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''solver_order''': 2,
        '''solver_type''': '''bh2''',
    }

    config.update(**UpperCamelCase__  )
    return config


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					str=0							,	**UpperCamelCase__    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=dict(self.forward_default_kwargs  )
    _UpperCamelCase       			=kwargs.pop('''num_inference_steps'''							,	UpperCamelCase__  )
    _UpperCamelCase       			=self.dummy_sample
    _UpperCamelCase       			=0.1 * sample
    _UpperCamelCase       			=[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )
      scheduler.set_timesteps(UpperCamelCase__  )
      # copy over dummy past residuals
      _UpperCamelCase       			=dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCamelCase__  )
        _UpperCamelCase       			=scheduler_class.from_pretrained(UpperCamelCase__  )
        new_scheduler.set_timesteps(UpperCamelCase__  )
        # copy over dummy past residuals
        _UpperCamelCase       			=dummy_past_residuals[: new_scheduler.config.solver_order]

      _UpperCamelCase			,     _UpperCamelCase       			=sample, sample
      for t in range(UpperCamelCase__							,	time_step + scheduler.config.solver_order + 1  ):
        _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample
        _UpperCamelCase       			=new_scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample

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


  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					Tuple=0							,	**UpperCamelCase__    :					Optional[Any]  )     ->						Optional[int]:
    _UpperCamelCase       			=dict(self.forward_default_kwargs  )
    _UpperCamelCase       			=kwargs.pop('''num_inference_steps'''							,	UpperCamelCase__  )
    _UpperCamelCase       			=self.dummy_sample
    _UpperCamelCase       			=0.1 * sample
    _UpperCamelCase       			=[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config()
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )
      scheduler.set_timesteps(UpperCamelCase__  )

      # copy over dummy past residuals (must be after setting timesteps)
      _UpperCamelCase       			=dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCamelCase__  )
        _UpperCamelCase       			=scheduler_class.from_pretrained(UpperCamelCase__  )
        # copy over dummy past residuals
        new_scheduler.set_timesteps(UpperCamelCase__  )

        # copy over dummy past residual (must be after setting timesteps)
        _UpperCamelCase       			=dummy_past_residuals[: new_scheduler.config.solver_order]

      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample
      _UpperCamelCase       			=new_scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample

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


  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					List[Any]=None							,	**UpperCamelCase__    :					Optional[int]  )     ->						Any:
    if scheduler is None:
      _UpperCamelCase       			=self.scheduler_classes[0]
      _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

    _UpperCamelCase       			=self.scheduler_classes[0]
    _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
    _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

    _UpperCamelCase       			=10
    _UpperCamelCase       			=self.dummy_model()
    _UpperCamelCase       			=self.dummy_sample_deter
    scheduler.set_timesteps(UpperCamelCase__  )

    for i, t in enumerate(scheduler.timesteps  ):
      _UpperCamelCase       			=model(UpperCamelCase__							,	UpperCamelCase__  )
      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  ).prev_sample

    return sample


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						str:
    _UpperCamelCase       			=dict(self.forward_default_kwargs  )

    _UpperCamelCase       			=kwargs.pop('''num_inference_steps'''							,	UpperCamelCase__  )

    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config()
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

      _UpperCamelCase       			=self.dummy_sample
      _UpperCamelCase       			=0.1 * sample

      if num_inference_steps is not None and hasattr(UpperCamelCase__							,	'''set_timesteps'''  ):
        scheduler.set_timesteps(UpperCamelCase__  )
      elif num_inference_steps is not None and not hasattr(UpperCamelCase__							,	'''set_timesteps'''  ):
        _UpperCamelCase       			=num_inference_steps

      # copy over dummy past residuals (must be done after set_timesteps)
      _UpperCamelCase       			=[residual + 0.2, residual + 0.15, residual + 0.10]
      _UpperCamelCase       			=dummy_past_residuals[: scheduler.config.solver_order]

      _UpperCamelCase       			=scheduler.timesteps[5]
      _UpperCamelCase       			=scheduler.timesteps[6]

      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample
      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample

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


  def   UpperCamelCase__      (				self    :					Any  )     ->						Dict:
    # make sure that iterating over schedulers with same config names gives same results
    # for defaults
    _UpperCamelCase       			=UniPCMultistepScheduler(**self.get_scheduler_config()  )
    _UpperCamelCase       			=self.full_loop(scheduler=UpperCamelCase__  )
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.2464  ) < 1E-3

    _UpperCamelCase       			=DPMSolverSinglestepScheduler.from_config(scheduler.config  )
    _UpperCamelCase       			=DEISMultistepScheduler.from_config(scheduler.config  )
    _UpperCamelCase       			=DPMSolverMultistepScheduler.from_config(scheduler.config  )
    _UpperCamelCase       			=UniPCMultistepScheduler.from_config(scheduler.config  )

    _UpperCamelCase       			=self.full_loop(scheduler=UpperCamelCase__  )
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.2464  ) < 1E-3


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Optional[Any]:
    for timesteps in [25, 50, 100, 999, 1000]:
      self.check_over_configs(num_train_timesteps=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Union[str, Any]:
    self.check_over_configs(thresholding=UpperCamelCase__  )
    for order in [1, 2, 3]:
      for solver_type in ["bh1", "bh2"]:
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(
                thresholding=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	sample_max_value=UpperCamelCase__							,	solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	)


  def   UpperCamelCase__      (				self    :					Dict  )     ->						List[str]:
    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Optional[Any]:
    for solver_type in ["bh1", "bh2"]:
      for order in [1, 2, 3]:
        for prediction_type in ["epsilon", "sample"]:
          self.check_over_configs(
              solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	)
          _UpperCamelCase       			=self.full_loop(
              solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	)
          assert not torch.isnan(UpperCamelCase__  ).any(), "Samples have nan numbers"


  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    self.check_over_configs(lower_order_final=UpperCamelCase__  )
    self.check_over_configs(lower_order_final=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[Any]:
    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
      self.check_over_forward(num_inference_steps=UpperCamelCase__							,	time_step=0  )


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=self.full_loop()
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.2464  ) < 1E-3


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Any:
    _UpperCamelCase       			=self.full_loop(prediction_type='''v_prediction'''  )
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.1014  ) < 1E-3


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[int]:
    _UpperCamelCase       			=self.scheduler_classes[0]
    _UpperCamelCase       			=self.get_scheduler_config(thresholding=UpperCamelCase__							,	dynamic_thresholding_ratio=0  )
    _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

    _UpperCamelCase       			=10
    _UpperCamelCase       			=self.dummy_model()
    _UpperCamelCase       			=self.dummy_sample_deter.half()
    scheduler.set_timesteps(UpperCamelCase__  )

    for i, t in enumerate(scheduler.timesteps  ):
      _UpperCamelCase       			=model(UpperCamelCase__							,	UpperCamelCase__  )
      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  ).prev_sample

    assert sample.dtype == torch.floataa




  def   UpperCamelCase__      (				self    :					Optional[int]							,	**UpperCamelCase__    :					Tuple  )     ->						List[Any]:
    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

      scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
      assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps













'''simple docstring'''






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__lowerCamelCase						: Optional[int]		=   TypeVar('T')







class 		UpperCAmelCase   (				Generic[T]):


  """simple docstring"""







  lowerCAmelCase_					=				42  # Cache store of keys
  lowerCAmelCase_					=				42  # References of the keys in cache
  lowerCAmelCase_					=				10  # Maximum capacity of cache


  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					int  )     ->						None:
    _UpperCamelCase       			=deque()
    _UpperCamelCase       			=set()
    if not n:
      _UpperCamelCase       			=sys.maxsize
    elif n < 0:
      raise ValueError('''n should be an integer greater than 0.'''  )
    else:
      _UpperCamelCase       			=n


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					T  )     ->						None:
    if x not in self.key_reference:
      if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
        _UpperCamelCase       			=self.dq_store.pop()
        self.key_reference.remove(UpperCamelCase__  )
    else:
      self.dq_store.remove(UpperCamelCase__  )

    self.dq_store.appendleft(UpperCamelCase__  )
    self.key_reference.add(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						None:
    for k in self.dq_store:
      print(UpperCamelCase__  )




  def __repr__(				self    :					str  )     ->						str:
    return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      __lowerCamelCase						: LRUCache[str | int]		=   LRUCache(4)
      lru_cache.refer('A')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('A')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"






