





"""simple docstring"""


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class      __SCREAMING_SNAKE_CASE    (						__UpperCAmelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:int	     =					(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					(('''eta''', 0.0), ('''num_inference_steps''', 50))

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   **__a   :       Optional[Any]	)		->				Any:
        _UpperCamelCase      :	Tuple						=       {
            "num_train_timesteps": 1000,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "clip_sample": True,
        }

        config.update(**__SCREAMING_SNAKE_CASE	)
        return config

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   **__a   :       Dict	)		->				List[str]:
        _UpperCamelCase      :	Union[str, Any]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	List[str]						=       self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
        _UpperCamelCase      :	int						=       scheduler_class(**__SCREAMING_SNAKE_CASE	)

        _UpperCamelCase,  _UpperCamelCase      :	Optional[Any]						=       10, 0.0

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

        scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

        for t in scheduler.timesteps:
              _UpperCamelCase      :	Any						=       model(__SCREAMING_SNAKE_CASE					,   __SCREAMING_SNAKE_CASE	)
              _UpperCamelCase      :	List[Any]						=       scheduler.step(__SCREAMING_SNAKE_CASE					,   __SCREAMING_SNAKE_CASE					,   __SCREAMING_SNAKE_CASE					,   __SCREAMING_SNAKE_CASE	).prev_sample

        return sample

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Any:

        for timesteps in [100, 500, 1000]:
              self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:

        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=__SCREAMING_SNAKE_CASE	)

        _UpperCamelCase      :	Optional[Any]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	Union[str, Any]						=       self.get_scheduler_config(steps_offset=1	)
        _UpperCamelCase      :	Any						=       scheduler_class(**__SCREAMING_SNAKE_CASE	)
        scheduler.set_timesteps(5	)
        assert torch.equal(scheduler.timesteps					,   torch.LongTensor([801, 601, 401, 201, 1]	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Optional[Any]:

        for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]					,   [0.0_02, 0.02, 0.2, 2]	):
              self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE					,   beta_end=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Dict:

        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Union[str, Any]:

        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Union[str, Any]:

        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				int:

        for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Optional[Any]:

        for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Tuple:
        self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE	)
        for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__SCREAMING_SNAKE_CASE					,   prediction_type=__SCREAMING_SNAKE_CASE					,   sample_max_value=__SCREAMING_SNAKE_CASE					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Optional[int]:

        for t in [1, 10, 49]:
              self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Tuple:

        for t, num_inference_steps in zip([1, 10, 50]					,   [10, 50, 500]	):
              self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE					,   num_inference_steps=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[Any]:

        for t, eta in zip([1, 10, 49]					,   [0.0, 0.5, 1.0]	):
              self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE					,   eta=__SCREAMING_SNAKE_CASE	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Dict:
        _UpperCamelCase      :	List[Any]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	Tuple						=       self.get_scheduler_config()
        _UpperCamelCase      :	Tuple						=       scheduler_class(**__SCREAMING_SNAKE_CASE	)

        assert torch.sum(torch.abs(scheduler._get_variance(0					,   0	) - 0.0	)	) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420					,   400	) - 0.1_47_71	)	) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980					,   960	) - 0.3_24_60	)	) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0					,   0	) - 0.0	)	) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487					,   486	) - 0.0_09_79	)	) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999					,   998	) - 0.02	)	) < 1e-5

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Optional[Any]:
        _UpperCamelCase      :	Optional[Any]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	int						=       self.get_scheduler_config()
        _UpperCamelCase      :	Optional[Any]						=       scheduler_class(**__SCREAMING_SNAKE_CASE	)

        _UpperCamelCase,  _UpperCamelCase      :	Dict						=       10, 0.0
        scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

        _UpperCamelCase      :	Any						=       self.dummy_model()
        _UpperCamelCase      :	Dict						=       self.dummy_sample_deter
        _UpperCamelCase      :	Optional[Any]						=       self.dummy_sample_deter + 0.1
        _UpperCamelCase      :	Dict						=       self.dummy_sample_deter - 0.1

        _UpperCamelCase      :	Optional[int]						=       samplea.shape[0]
        _UpperCamelCase      :	Tuple						=       torch.stack([samplea, samplea, samplea]					,   dim=0	)
        _UpperCamelCase      :	str						=       torch.arange(__SCREAMING_SNAKE_CASE	)[0:3, None].repeat(1					,   __SCREAMING_SNAKE_CASE	)

        _UpperCamelCase      :	List[Any]						=       model(samples.flatten(0					,   1	)					,   timesteps.flatten(0					,   1	)	)
        _UpperCamelCase      :	List[str]						=       scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE					,   timesteps.flatten(0					,   1	)					,   samples.flatten(0					,   1	)					,   __SCREAMING_SNAKE_CASE	)

        _UpperCamelCase      :	List[Any]						=       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE	)	)
        _UpperCamelCase      :	str						=       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

        assert abs(result_sum.item() - 1147.7904	) < 1e-2
        assert abs(result_mean.item() - 0.49_82	) < 1e-3

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				str:
        _UpperCamelCase      :	Optional[Any]						=       self.full_loop()

        _UpperCamelCase      :	Tuple						=       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE	)	)
        _UpperCamelCase      :	str						=       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

        assert abs(result_sum.item() - 172.0067	) < 1e-2
        assert abs(result_mean.item() - 0.22_39_67	) < 1e-3

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				int:
        _UpperCamelCase      :	Optional[int]						=       self.full_loop(prediction_type="v_prediction"	)

        _UpperCamelCase      :	str						=       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE	)	)
        _UpperCamelCase      :	List[Any]						=       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

        assert abs(result_sum.item() - 52.53_02	) < 1e-2
        assert abs(result_mean.item() - 0.06_84	) < 1e-3

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Any:
        _UpperCamelCase      :	Dict						=       self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE					,   beta_start=0.01	)
        _UpperCamelCase      :	Optional[int]						=       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE	)	)
        _UpperCamelCase      :	Any						=       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

        assert abs(result_sum.item() - 149.8295	) < 1e-2
        assert abs(result_mean.item() - 0.19_51	) < 1e-3


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Any:
        _UpperCamelCase      :	List[str]						=       self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE					,   beta_start=0.01	)
        _UpperCamelCase      :	Optional[Any]						=       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE	)	)
        _UpperCamelCase      :	str						=       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

        assert abs(result_sum.item() - 149.0784	) < 1e-2
        assert abs(result_mean.item() - 0.19_41	) < 1e-3







"""simple docstring"""


from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCamelCase__				  =   TypeVar("KEY")
lowerCamelCase__				  =   TypeVar("VAL")



@dataclass(frozen=_UpperCamelCase		,  slots=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						Generic[KEY, VAL]   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:KEY
  SCREAMING_SNAKE_CASE__							:VAL





class      __SCREAMING_SNAKE_CASE    (						_Item   ):







  '''simple docstring'''


  def __init__(		self   :       List[str]	)		->				None:
        super().__init__(__a					,   __a	)


  def __bool__(		self   :       Dict	)		->				bool:
        return False





lowerCamelCase__				  =   _DeletedItem()



class      __SCREAMING_SNAKE_CASE    (						MutableMapping[KEY, VAL]   ):







  '''simple docstring'''


  def __init__(		self   :       int					,   __a   :       int = 8					,   __a   :       float = 0.75	)		->				None:
        _UpperCamelCase      :	str						=       initial_block_size
        _UpperCamelCase      :	list[_Item | None]						=       [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        _UpperCamelCase      :	List[str]						=       capacity_factor
        _UpperCamelCase      :	Dict						=       0

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       KEY	)		->				int:
        return hash(__a	) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       int	)		->				int:
        return (ind + 1) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				bool:
        _UpperCamelCase      :	List[Any]						=       self._buckets[ind]
        if not stored:
              _UpperCamelCase      :	Tuple						=       _Item(__a					,   __a	)
              self._len += 1
              return True
        elif stored.key == key:
              _UpperCamelCase      :	Union[str, Any]						=       _Item(__a					,   __a	)
              return True
        else:
              return False

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				bool:
        _UpperCamelCase      :	Any						=       len(self._buckets	) * self._capacity_factor
        return len(self	) >= int(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				bool:
        if len(self._buckets	) <= self._initial_block_size:
              return False
        _UpperCamelCase      :	List[str]						=       len(self._buckets	) * self._capacity_factor / 2
        return len(self	) < limit

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       int	)		->				None:
        _UpperCamelCase      :	Any						=       self._buckets
        _UpperCamelCase      :	List[Any]						=       [None] * new_size
        _UpperCamelCase      :	List[str]						=       0
        for item in old_buckets:
              if item:
                    self._add_item(item.key					,   item.val	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				None:
        self._resize(len(self._buckets	) * 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				None:
        self._resize(len(self._buckets	) // 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       KEY	)		->				Iterator[int]:
        _UpperCamelCase      :	str						=       self._get_bucket_index(__a	)
        for _ in range(len(self._buckets	)	):
              yield ind
              _UpperCamelCase      :	Tuple						=       self._get_next_ind(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        for ind in self._iterate_buckets(__a	):
              if self._try_set(__a					,   __a					,   __a	):
                    break

  def __setitem__(		self   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        if self._is_full():
              self._size_up()

        self._add_item(__a					,   __a	)

  def __delitem__(		self   :       str					,   __a   :       KEY	)		->				None:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    raise KeyError(__a	)
              if item is _deleted:
                    continue
              if item.key == key:
                    _UpperCamelCase      :	List[Any]						=       _deleted
                    self._len -= 1
                    break
        if self._is_sparse():
              self._size_down()

  def __getitem__(		self   :       str					,   __a   :       KEY	)		->				VAL:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    break
              if item is _deleted:
                    continue
              if item.key == key:
                    return item.val
        raise KeyError(__a	)

  def __len__(		self   :       List[Any]	)		->				int:
        return self._len

  def __iter__(		self   :       List[str]	)		->				Iterator[KEY]:
        yield from (item.key for item in self._buckets if item)


  def __repr__(		self   :       List[str]	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       " ,".join(
            F'''{item.key}: {item.val}''' for item in self._buckets if item	)
        return F'''HashMap({val_string})'''
