





"""simple docstring"""


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					(UnCLIPScheduler,)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   **__a   :       Tuple	)		->				Tuple:
        _UpperCamelCase      :	Union[str, Any]						=       {
            "num_train_timesteps": 1000,
            "variance_type": "fixed_small_log",
            "clip_sample": True,
            "clip_sample_range": 1.0,
            "prediction_type": "epsilon",
        }

        config.update(**__a	)
        return config

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Optional[int]:
        for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Dict:
        for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Union[str, Any]:
        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Any:
        for clip_sample_range in [1, 5, 10, 20]:
              self.check_over_configs(clip_sample_range=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Optional[int]:
        for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				List[Any]:
        for time_step in [0, 500, 999]:
              for prev_timestep in [None, 5, 100, 250, 500, 750]:
                    if prev_timestep is not None and prev_timestep >= time_step:
                          continue

                    self.check_over_forward(time_step=__a					,   prev_timestep=__a	)

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

        assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.0_0_0_0e-1_0	)	) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0_54_96_25	)	) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.9_99_49_87	)	) < 1e-5

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Dict:
        _UpperCamelCase      :	Optional[Any]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	List[str]						=       self.get_scheduler_config(variance_type="learned_range"	)
        _UpperCamelCase      :	Dict						=       scheduler_class(**__a	)

        _UpperCamelCase      :	Any						=       0.5

        assert scheduler._get_variance(1					,   predicted_variance=__a	) - -10.1_71_27_90 < 1e-5
        assert scheduler._get_variance(487					,   predicted_variance=__a	) - -5.7_99_80_52 < 1e-5
        assert scheduler._get_variance(999					,   predicted_variance=__a	) - -0.0_01_00_11 < 1e-5

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

        _UpperCamelCase      :	List[Any]						=       scheduler.timesteps

        _UpperCamelCase      :	str						=       self.dummy_model()
        _UpperCamelCase      :	List[Any]						=       self.dummy_sample_deter
        _UpperCamelCase      :	List[str]						=       torch.manual_seed(0	)

        for i, t in enumerate(__a	):
              # 1. predict noise residual
              _UpperCamelCase      :	int						=       model(__a					,   __a	)

              # 2. predict previous mean of sample x_t-1
              _UpperCamelCase      :	Union[str, Any]						=       scheduler.step(__a					,   __a					,   __a					,   generator=__a	).prev_sample

              _UpperCamelCase      :	Dict						=       pred_prev_sample

        _UpperCamelCase      :	Optional[int]						=       torch.sum(torch.abs(__a	)	)
        _UpperCamelCase      :	Dict						=       torch.mean(torch.abs(__a	)	)

        assert abs(result_sum.item() - 2_52.2_68_24_95	) < 1e-2
        assert abs(result_mean.item() - 0.3_28_47_43	) < 1e-3

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

        scheduler.set_timesteps(25	)

        _UpperCamelCase      :	Tuple						=       scheduler.timesteps

        _UpperCamelCase      :	Optional[Any]						=       self.dummy_model()
        _UpperCamelCase      :	int						=       self.dummy_sample_deter
        _UpperCamelCase      :	Optional[Any]						=       torch.manual_seed(0	)

        for i, t in enumerate(__a	):
              # 1. predict noise residual
              _UpperCamelCase      :	List[str]						=       model(__a					,   __a	)

              if i + 1 == timesteps.shape[0]:
                    _UpperCamelCase      :	List[Any]						=       None
              else:
                    _UpperCamelCase      :	int						=       timesteps[i + 1]

              # 2. predict previous mean of sample x_t-1
              _UpperCamelCase      :	List[str]						=       scheduler.step(
                  __a					,   __a					,   __a					,   prev_timestep=__a					,   generator=__a	).prev_sample

              _UpperCamelCase      :	Union[str, Any]						=       pred_prev_sample

        _UpperCamelCase      :	List[Any]						=       torch.sum(torch.abs(__a	)	)
        _UpperCamelCase      :	Dict						=       torch.mean(torch.abs(__a	)	)

        assert abs(result_sum.item() - 2_58.2_04_49_83	) < 1e-2
        assert abs(result_mean.item() - 0.3_36_20_38	) < 1e-3

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


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict:
        pass







"""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})'''
