



"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			(PNDMScheduler,)
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			(('num_inference_steps', 5_0),)



      def 						snake_case_ (					self					:     Tuple					,     **A					:     Tuple						):
           _UpperCAmelCase      :     List[Any]        =   {
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**A						)
           return config



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]=0					,     **A					:     List[Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   dict(self.forward_default_kwargs						)
           _UpperCAmelCase      :     Optional[int]        =   kwargs.pop("num_inference_steps"					,     A						)
           _UpperCAmelCase      :     int        =   self.dummy_sample
           _UpperCAmelCase      :     Dict        =   0.1 * sample
           _UpperCAmelCase      :     Optional[int]        =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                _UpperCAmelCase      :     int        =   self.get_scheduler_config(**A						)
                _UpperCAmelCase      :     Tuple        =   scheduler_class(**A						)
                scheduler.set_timesteps(A						)
                # copy over dummy past residuals
                _UpperCAmelCase      :     Union[str, Any]        =   dummy_past_residuals[:]

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(A						)
                     _UpperCAmelCase      :     Union[str, Any]        =   scheduler_class.from_pretrained(A						)
                     new_scheduler.set_timesteps(A						)
                     # copy over dummy past residuals
                     _UpperCAmelCase      :     List[Any]        =   dummy_past_residuals[:]

                _UpperCAmelCase      :     Tuple        =   scheduler.step_prk(A					,     A					,     A					,     **A						).prev_sample
                _UpperCAmelCase      :     int        =   new_scheduler.step_prk(A					,     A					,     A					,     **A						).prev_sample

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

                _UpperCAmelCase      :     List[str]        =   scheduler.step_plms(A					,     A					,     A					,     **A						).prev_sample
                _UpperCAmelCase      :     List[Any]        =   new_scheduler.step_plms(A					,     A					,     A					,     **A						).prev_sample

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



      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass



      def 						snake_case_ (					self					:     Tuple					,     A					:     List[Any]=0					,     **A					:     Optional[int]						):
           _UpperCAmelCase      :     Dict        =   dict(self.forward_default_kwargs						)
           _UpperCAmelCase      :     Union[str, Any]        =   kwargs.pop("num_inference_steps"					,     A						)
           _UpperCAmelCase      :     Optional[Any]        =   self.dummy_sample
           _UpperCAmelCase      :     Dict        =   0.1 * sample
           _UpperCAmelCase      :     Tuple        =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                _UpperCAmelCase      :     str        =   self.get_scheduler_config()
                _UpperCAmelCase      :     List[Any]        =   scheduler_class(**A						)
                scheduler.set_timesteps(A						)

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

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

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

                _UpperCAmelCase      :     List[str]        =   scheduler.step_prk(A					,     A					,     A					,     **A						).prev_sample
                _UpperCAmelCase      :     Tuple        =   new_scheduler.step_prk(A					,     A					,     A					,     **A						).prev_sample

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

                _UpperCAmelCase      :     Any        =   scheduler.step_plms(A					,     A					,     A					,     **A						).prev_sample
                _UpperCAmelCase      :     Tuple        =   new_scheduler.step_plms(A					,     A					,     A					,     **A						).prev_sample

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



      def 						snake_case_ (					self					:     Dict					,     **A					:     int						):
           _UpperCAmelCase      :     Any        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     Tuple        =   self.get_scheduler_config(**A						)
           _UpperCAmelCase      :     str        =   scheduler_class(**A						)

           _UpperCAmelCase      :     Any        =   1_0
           _UpperCAmelCase      :     List[Any]        =   self.dummy_model()
           _UpperCAmelCase      :     int        =   self.dummy_sample_deter
           scheduler.set_timesteps(A						)

           for i, t in enumerate(scheduler.prk_timesteps						):
                _UpperCAmelCase      :     Union[str, Any]        =   model(A					,     A						)
                _UpperCAmelCase      :     Optional[Any]        =   scheduler.step_prk(A					,     A					,     A						).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps						):
                _UpperCAmelCase      :     Optional[int]        =   model(A					,     A						)
                _UpperCAmelCase      :     Optional[int]        =   scheduler.step_plms(A					,     A					,     A						).prev_sample

           return sample



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     List[str]        =   dict(self.forward_default_kwargs						)

           _UpperCAmelCase      :     Tuple        =   kwargs.pop("num_inference_steps"					,     A						)

           for scheduler_class in self.scheduler_classes:
                _UpperCAmelCase      :     Tuple        =   self.get_scheduler_config()
                _UpperCAmelCase      :     Dict        =   scheduler_class(**A						)

                _UpperCAmelCase      :     str        =   self.dummy_sample
                _UpperCAmelCase      :     List[Any]        =   0.1 * sample

                if num_inference_steps is not None and hasattr(A					,     "set_timesteps"						):
                     scheduler.set_timesteps(A						)
                elif num_inference_steps is not None and not hasattr(A					,     "set_timesteps"						):
                     _UpperCAmelCase      :     Union[str, Any]        =   num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                _UpperCAmelCase      :     Union[str, Any]        =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                _UpperCAmelCase      :     Optional[int]        =   dummy_past_residuals[:]

                _UpperCAmelCase      :     Optional[Any]        =   scheduler.step_prk(A					,     0					,     A					,     **A						).prev_sample
                _UpperCAmelCase      :     Dict        =   scheduler.step_prk(A					,     1					,     A					,     **A						).prev_sample

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

                _UpperCAmelCase      :     Tuple        =   scheduler.step_plms(A					,     0					,     A					,     **A						).prev_sample
                _UpperCAmelCase      :     str        =   scheduler.step_plms(A					,     1					,     A					,     **A						).prev_sample

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



      def 						snake_case_ (					self					:     Any						):
           for timesteps in [1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=A						)



      def 						snake_case_ (					self					:     List[str]						):
           for steps_offset in [0, 1]:
                self.check_over_configs(steps_offset=A						)

           _UpperCAmelCase      :     Optional[int]        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     Tuple        =   self.get_scheduler_config(steps_offset=1						)
           _UpperCAmelCase      :     List[str]        =   scheduler_class(**A						)
           scheduler.set_timesteps(1_0						)
           assert torch.equal(
               scheduler.timesteps					,     torch.LongTensor(
                   [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]						)					,     )



      def 						snake_case_ (					self					:     Dict						):
           for beta_start, beta_end in zip([0.0_001, 0.001]					,     [0.002, 0.02]						):
                self.check_over_configs(beta_start=A					,     beta_end=A						)



      def 						snake_case_ (					self					:     Union[str, Any]						):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=A						)



      def 						snake_case_ (					self					:     Tuple						):
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=A						)



      def 						snake_case_ (					self					:     str						):
           for t in [1, 5, 1_0]:
                self.check_over_forward(time_step=A						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           for t, num_inference_steps in zip([1, 5, 1_0]					,     [1_0, 5_0, 1_0_0]						):
                self.check_over_forward(num_inference_steps=A						)



      def 						snake_case_ (					self					:     List[str]						):
           # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
           _UpperCAmelCase      :     int        =   2_7

           for scheduler_class in self.scheduler_classes:
                _UpperCAmelCase      :     Any        =   self.dummy_sample
                _UpperCAmelCase      :     Optional[int]        =   0.1 * sample

                _UpperCAmelCase      :     str        =   self.get_scheduler_config()
                _UpperCAmelCase      :     List[Any]        =   scheduler_class(**A						)

                scheduler.set_timesteps(A						)

                # before power of 3 fix, would error on first step, so we only need to do two
                for i, t in enumerate(scheduler.prk_timesteps[:2]						):
                     _UpperCAmelCase      :     Optional[int]        =   scheduler.step_prk(A					,     A					,     A						).prev_sample



      def 						snake_case_ (					self					:     List[str]						):
           with self.assertRaises(A						):
                _UpperCAmelCase      :     Any        =   self.scheduler_classes[0]
                _UpperCAmelCase      :     str        =   self.get_scheduler_config()
                _UpperCAmelCase      :     Optional[Any]        =   scheduler_class(**A						)

                scheduler.step_plms(self.dummy_sample					,     1					,     self.dummy_sample						).prev_sample



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     int        =   self.full_loop()
           _UpperCAmelCase      :     int        =   torch.sum(torch.abs(A						)						)
           _UpperCAmelCase      :     Dict        =   torch.mean(torch.abs(A						)						)

           assert abs(result_sum.item() - 198.1_318						) < 1e-2
           assert abs(result_mean.item() - 0.2_580						) < 1e-3



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     int        =   self.full_loop(prediction_type="v_prediction"						)
           _UpperCAmelCase      :     str        =   torch.sum(torch.abs(A						)						)
           _UpperCAmelCase      :     str        =   torch.mean(torch.abs(A						)						)

           assert abs(result_sum.item() - 67.3_986						) < 1e-2
           assert abs(result_mean.item() - 0.0_878						) < 1e-3



      def 						snake_case_ (					self					:     Any						):
           # We specify different beta, so that the first alpha is 0.99
           _UpperCAmelCase      :     List[Any]        =   self.full_loop(set_alpha_to_one=A					,     beta_start=0.01						)
           _UpperCAmelCase      :     Union[str, Any]        =   torch.sum(torch.abs(A						)						)
           _UpperCAmelCase      :     List[Any]        =   torch.mean(torch.abs(A						)						)

           assert abs(result_sum.item() - 230.0_399						) < 1e-2
           assert abs(result_mean.item() - 0.2_995						) < 1e-3



      def 						snake_case_ (					self					:     Tuple						):
           # We specify different beta, so that the first alpha is 0.99
           _UpperCAmelCase      :     List[str]        =   self.full_loop(set_alpha_to_one=A					,     beta_start=0.01						)
           _UpperCAmelCase      :     Union[str, Any]        =   torch.sum(torch.abs(A						)						)
           _UpperCAmelCase      :     Tuple        =   torch.mean(torch.abs(A						)						)

           assert abs(result_sum.item() - 186.9_482						) < 1e-2
           assert abs(result_mean.item() - 0.2_434						) < 1e-3





"""simple docstring"""




from collections.abc import Callable






class      UpperCAmelCase_  :



      def __init__(					self					:     Dict					,     A					:     Callable | None = None						):
           # Stores actual heap items.
           _UpperCAmelCase      :     list        =   []
           # Stores indexes of each item for supporting updates and deletion.
           _UpperCAmelCase      :     dict        =   {}
           # Stores current size of heap.
           _UpperCAmelCase      :     Tuple        =   0
           # Stores function used to evaluate the score of an item on which basis ordering
           # will be done.
           _UpperCAmelCase      :     Any        =   key or (lambda A						: x)



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           return int((i - 1) / 2						) if i > 0 else None



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   int(2 * i + 1						)
           return left if 0 < left < self.size else None



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   int(2 * i + 2						)
           return right if 0 < right < self.size else None



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     int					,     A					:     int						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   (
               self.pos_map[self.arr[j][0]],
               self.pos_map[self.arr[i][0]],
           )
           # Then swap the items in the list.
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   self.arr[j], self.arr[i]



      def 						snake_case_ (					self					:     List[str]					,     A					:     int					,     A					:     int						):
           return self.arr[i][1] < self.arr[j][1]



      def 						snake_case_ (					self					:     Dict					,     A					:     int						):
           _UpperCAmelCase      :     str        =   self._left(A						)
           _UpperCAmelCase      :     str        =   self._right(A						)
           _UpperCAmelCase      :     List[Any]        =   i

           if left is not None and not self._cmp(A					,     A						):
                _UpperCAmelCase      :     Optional[int]        =   left
           if right is not None and not self._cmp(A					,     A						):
                _UpperCAmelCase      :     Any        =   right

           return valid_parent



      def 						snake_case_ (					self					:     Tuple					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   self._parent(A						)
           while parent is not None and not self._cmp(A					,     A						):
                self._swap(A					,     A						)
                _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   parent, self._parent(A						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   self._get_valid_parent(A						)
           while valid_parent != index:
                self._swap(A					,     A						)
                _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[Any]        =   valid_parent, self._get_valid_parent(A						)



      def 						snake_case_ (					self					:     Dict					,     A					:     int					,     A					:     int						):
           if item not in self.pos_map:
                return
           _UpperCAmelCase      :     Any        =   self.pos_map[item]
           _UpperCAmelCase      :     Optional[int]        =   [item, self.key(A						)]
           # Make sure heap is right in both up and down direction.
           # Ideally only one of them will make any change.
           self._heapify_up(A						)
           self._heapify_down(A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     int						):
           if item not in self.pos_map:
                return
           _UpperCAmelCase      :     str        =   self.pos_map[item]
           del self.pos_map[item]
           _UpperCAmelCase      :     Tuple        =   self.arr[self.size - 1]
           _UpperCAmelCase      :     List[Any]        =   index
           self.size -= 1
           # Make sure heap is right in both up and down direction. Ideally only one
           # of them will make any change- so no performance loss in calling both.
           if self.size > index:
                self._heapify_up(A						)
                self._heapify_down(A						)



      def 						snake_case_ (					self					:     Any					,     A					:     int					,     A					:     int						):
           _UpperCAmelCase      :     Any        =   len(self.arr						)
           if arr_len == self.size:
                self.arr.append([item, self.key(A						)]						)
           else:
                _UpperCAmelCase      :     Any        =   [item, self.key(A						)]
           _UpperCAmelCase      :     List[Any]        =   self.size
           self.size += 1
           self._heapify_up(self.size - 1						)



      def 						snake_case_ (					self					:     Tuple						):
           return self.arr[0] if self.size else None



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Dict        =   self.get_top()
           if top_item_tuple:
                self.delete_item(top_item_tuple[0]						)
           return top_item_tuple







def 						__snake_case						(				)  ->       None:





     '''simple docstring'''



if __name__ == "__main__":
 import doctest

 doctest.testmod()
