
"""simple docstring"""



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class       lowerCAmelCase__				(						A_	):
     __a						=					(PNDMScheduler,)
     __a						=					(("""num_inference_steps""", 50),)







     def      lowercase	(							self			:			Dict    ,      **_lowerCamelCase			:			Dict			):
       _snake_case					     =       {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0_0_0_1,
           '''beta_end''': 0.0_2,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_lowerCamelCase			)
       return config







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Dict=0    ,      **_lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       dict(self.forward_default_kwargs			)
       _snake_case					     =       kwargs.pop('''num_inference_steps'''    ,      _lowerCamelCase			)
       _snake_case					     =       self.dummy_sample
       _snake_case					     =       0.1 * sample
       _snake_case					     =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

         _snake_case					     =       scheduler.step_prk(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_prk(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample

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

         _snake_case					     =       scheduler.step_plms(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_plms(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample

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







     def      lowercase	(							self			:			List[Any]			):
       pass







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			Tuple=0    ,      **_lowerCamelCase			:			str			):
       _snake_case					     =       dict(self.forward_default_kwargs			)
       _snake_case					     =       kwargs.pop('''num_inference_steps'''    ,      _lowerCamelCase			)
       _snake_case					     =       self.dummy_sample
       _snake_case					     =       0.1 * sample
       _snake_case					     =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**_lowerCamelCase			)
         scheduler.set_timesteps(_lowerCamelCase			)

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

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_lowerCamelCase			)
           _snake_case					     =       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)
           _snake_case					     =       dummy_past_residuals[:]

         _snake_case					     =       scheduler.step_prk(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_prk(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample

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

         _snake_case					     =       scheduler.step_plms(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_plms(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample

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







     def      lowercase	(							self			:			str    ,      **_lowerCamelCase			:			str			):
       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config(**_lowerCamelCase			)
       _snake_case					     =       scheduler_class(**_lowerCamelCase			)

       _snake_case					     =       10
       _snake_case					     =       self.dummy_model()
       _snake_case					     =       self.dummy_sample_deter
       scheduler.set_timesteps(_lowerCamelCase			)

       for i, t in enumerate(scheduler.prk_timesteps			):
         _snake_case					     =       model(_lowerCamelCase    ,      _lowerCamelCase			)
         _snake_case					     =       scheduler.step_prk(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps			):
         _snake_case					     =       model(_lowerCamelCase    ,      _lowerCamelCase			)
         _snake_case					     =       scheduler.step_plms(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			).prev_sample

       return sample







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       dict(self.forward_default_kwargs			)

       _snake_case					     =       kwargs.pop('''num_inference_steps'''    ,      _lowerCamelCase			)

       for scheduler_class in self.scheduler_classes:
         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**_lowerCamelCase			)

         _snake_case					     =       self.dummy_sample
         _snake_case					     =       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'''			):
           _snake_case					     =       num_inference_steps

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

         _snake_case					     =       scheduler.step_prk(_lowerCamelCase    ,      0    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample
         _snake_case					     =       scheduler.step_prk(_lowerCamelCase    ,      1    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample

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

         _snake_case					     =       scheduler.step_plms(_lowerCamelCase    ,      0    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample
         _snake_case					     =       scheduler.step_plms(_lowerCamelCase    ,      1    ,      _lowerCamelCase    ,      **_lowerCamelCase			).prev_sample

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







     def      lowercase	(							self			:			Dict			):
       for timesteps in [100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]			):
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=_lowerCamelCase			)

       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config(steps_offset=1			)
       _snake_case					     =       scheduler_class(**_lowerCamelCase			)
       scheduler.set_timesteps(10			)
       assert torch.equal(
           scheduler.timesteps    ,      torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]			)    ,      )







     def      lowercase	(							self			:			Optional[int]			):
       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,      [0.0_0_2, 0.0_2]			):
         self.check_over_configs(beta_start=_lowerCamelCase    ,      beta_end=_lowerCamelCase			)







     def      lowercase	(							self			:			int			):
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=_lowerCamelCase			)







     def      lowercase	(							self			:			int			):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       for t in [1, 5, 10]:
         self.check_over_forward(time_step=_lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       for t, num_inference_steps in zip([1, 5, 10]    ,      [10, 50, 100]			):
         self.check_over_forward(num_inference_steps=_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]			):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       _snake_case					     =       27

       for scheduler_class in self.scheduler_classes:
         _snake_case					     =       self.dummy_sample
         _snake_case					     =       0.1 * sample

         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**_lowerCamelCase			)

         scheduler.set_timesteps(_lowerCamelCase			)

         # 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]			):
           _snake_case					     =       scheduler.step_prk(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			).prev_sample







     def      lowercase	(							self			:			int			):
       with self.assertRaises(_lowerCamelCase			):
         _snake_case					     =       self.scheduler_classes[0]
         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**_lowerCamelCase			)

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







     def      lowercase	(							self			:			str			):
       _snake_case					     =       self.full_loop()
       _snake_case					     =       torch.sum(torch.abs(_lowerCamelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(_lowerCamelCase			)			)

       assert abs(result_sum.item() - 1_9_8.1_3_1_8			) < 1e-2
       assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.full_loop(prediction_type='''v_prediction'''			)
       _snake_case					     =       torch.sum(torch.abs(_lowerCamelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(_lowerCamelCase			)			)

       assert abs(result_sum.item() - 6_7.3_9_8_6			) < 1e-2
       assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3







     def      lowercase	(							self			:			List[Any]			):
       # We specify different beta, so that the first alpha is 0.99
       _snake_case					     =       self.full_loop(set_alpha_to_one=_lowerCamelCase    ,      beta_start=0.0_1			)
       _snake_case					     =       torch.sum(torch.abs(_lowerCamelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(_lowerCamelCase			)			)

       assert abs(result_sum.item() - 2_3_0.0_3_9_9			) < 1e-2
       assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3







     def      lowercase	(							self			:			List[Any]			):
       # We specify different beta, so that the first alpha is 0.99
       _snake_case					     =       self.full_loop(set_alpha_to_one=_lowerCamelCase    ,      beta_start=0.0_1			)
       _snake_case					     =       torch.sum(torch.abs(_lowerCamelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(_lowerCamelCase			)			)

       assert abs(result_sum.item() - 1_8_6.9_4_8_2			) < 1e-2
       assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3





"""simple docstring"""



from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class       lowerCAmelCase__				(						A_	):







     def __lt__(							self			:			Any    ,      _lowerCamelCase			:			int			):
       return self[-1] < other[-1]







     def __eq__(							self			:			int    ,      _lowerCamelCase			:			Optional[Any]			):
       return self[-1] == other[-1]






def 					_UpperCAmelCase      ( __lowerCamelCase   :       list		)     ->					list:
  _snake_case					     =       []
  # sort into stacks
  for element in collection:
    _snake_case					     =       Stack([element]		)
    _snake_case					     =       bisect_left(__lowerCamelCase ,					__lowerCamelCase		)
    if i != len(__lowerCamelCase		):
      stacks[i].append(__lowerCamelCase		)
    else:
      stacks.append(__lowerCamelCase		)

    # use a heap-based merge to merge stack efficiently
  _snake_case					     =       merge(*(reversed(__lowerCamelCase		) for stack in stacks)		)
  return collection


if __name__ == "__main__":
 UpperCAmelCase__          = input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase__          = [int(item) for item in user_input.split(',')]
 print(patience_sort(unsorted))



