"""simple docstring"""






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

__lowerCAmelCase					: str        =TypeVar("""KEY""")
__lowerCAmelCase					: int        =TypeVar("""VAL""")





@dataclass(frozen=UpperCamelCase_						,					slots=UpperCamelCase_			)
class 				_A		( Generic[KEY, VAL]			):
      snake_case__    :     Optional[int]													=			42
      snake_case__    :     Union[str, Any]													=			42






class 				_A		( _Item			):

      def __init__(		self		):







            """simple docstring"""



            super().__init__(__A	, __A		)







      def __bool__(		self		):







            """simple docstring"""



            return False






__lowerCAmelCase					: List[str]        =_DeletedItem()





class 				_A		( MutableMapping[KEY, VAL]			):

      def __init__(		self	, __lowerCAmelCase = 8	, __lowerCAmelCase = 0.7_5		):







            """simple docstring"""



            lowercase														=			initial_block_size
            lowercase														=			[None] * initial_block_size
            assert 0.0 < capacity_factor < 1.0
            lowercase														=			capacity_factor
            lowercase														=			0

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            return hash(__A		) % len(self._buckets		)

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            return (ind + 1) % len(self._buckets		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self._buckets[ind]
            if not stored:
                  lowercase														=			_Item(__A	, __A		)
                  self._len += 1
                  return True
            elif stored.key == key:
                  lowercase														=			_Item(__A	, __A		)
                  return True
            else:
                  return False

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			len(self._buckets		) * self._capacity_factor
            return len(self		) >= int(__A		)

      def        A__ (		self		):







            """simple docstring"""



            if len(self._buckets		) <= self._initial_block_size:
                  return False
            lowercase														=			len(self._buckets		) * self._capacity_factor / 2
            return len(self		) < limit

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self._buckets
            lowercase														=			[None] * new_size
            lowercase														=			0
            for item in old_buckets:
                  if item:
                        self._add_item(item.key	, item.val		)

      def        A__ (		self		):







            """simple docstring"""



            self._resize(len(self._buckets		) * 2		)

      def        A__ (		self		):







            """simple docstring"""



            self._resize(len(self._buckets		) // 2		)

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self._get_bucket_index(__A		)
            for _ in range(len(self._buckets		)		):
                  yield ind
                  lowercase														=			self._get_next_ind(__A		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            for ind in self._iterate_buckets(__A		):
                  if self._try_set(__A	, __A	, __A		):
                        break

      def __setitem__(		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            if self._is_full():
                  self._size_up()

            self._add_item(__A	, __A		)

      def __delitem__(		self	, __lowerCAmelCase		):







            """simple docstring"""



            for ind in self._iterate_buckets(__A		):
                  lowercase														=			self._buckets[ind]
                  if item is None:
                        raise KeyError(__A		)
                  if item is _deleted:
                        continue
                  if item.key == key:
                        lowercase														=			_deleted
                        self._len -= 1
                        break
            if self._is_sparse():
                  self._size_down()

      def __getitem__(		self	, __lowerCAmelCase		):







            """simple docstring"""



            for ind in self._iterate_buckets(__A		):
                  lowercase														=			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		):







            """simple docstring"""



            return self._len

      def __iter__(		self		):







            """simple docstring"""



            yield from (item.key for item in self._buckets if item)







      def __repr__(		self		):







            """simple docstring"""



            lowercase														=			" ,".join(
                f'{item.key}: {item.val}' for item in self._buckets if item		)
            return f'HashMap({val_string})'




"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Union[str, Any]													=			(IPNDMScheduler,)
      snake_case__    :     List[str]													=			(('num_inference_steps', 50),)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			{"""num_train_timesteps""": 1000}
            config.update(**__lowerCAmelCase		)
            return config

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowercase														=			dummy_past_residuals[:]

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)

                  # copy over dummy past residuals (must be after setting timesteps)
                  lowercase														=			dummy_past_residuals[:]

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

                        # copy over dummy past residual (must be after setting timesteps)
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scheduler_classes[0]
            lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
            lowercase														=			scheduler_class(**__lowerCAmelCase		)

            lowercase														=			10
            lowercase														=			self.dummy_model()
            lowercase														=			self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCAmelCase		)

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

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

            return sample

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)

            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)

                  lowercase														=			self.dummy_sample
                  lowercase														=			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"""		):
                        lowercase														=			num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                  lowercase														=			dummy_past_residuals[:]

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



            for t, num_inference_steps in zip([1, 5, 10]	, [10, 50, 100]		):
                  self.check_over_forward(num_inference_steps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.full_loop()
            lowercase														=			torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_mean.item() - 254_0529		) < 10



