

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    (PNDMScheduler,)
       lowercase_						=    (("""num_inference_steps""", 5_0),)

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             __A    ={
                 '''num_train_timesteps''': 1_0_0_0,
                 '''beta_start''': 0.0001,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
             }

             config.update(**lowercase__     )
             return config

       def     __UpperCamelCase     (							self ,   lowercase__=0 ,   **lowercase__     ):







             '''simple docstring'''



             __A    =dict(self.forward_default_kwargs     )
             __A    =kwargs.pop('''num_inference_steps''' ,   lowercase__     )
             __A    =self.dummy_sample
             __A    =0.1 * sample
             __A    =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

                   __A    =scheduler.step_prk(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample
                   __A    =new_scheduler.step_prk(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample

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

                   __A    =scheduler.step_plms(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample
                   __A    =new_scheduler.step_plms(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self ,   lowercase__=0 ,   **lowercase__     ):







             '''simple docstring'''



             __A    =dict(self.forward_default_kwargs     )
             __A    =kwargs.pop('''num_inference_steps''' ,   lowercase__     )
             __A    =self.dummy_sample
             __A    =0.1 * sample
             __A    =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                   __A    =self.get_scheduler_config()
                   __A    =scheduler_class(**lowercase__     )
                   scheduler.set_timesteps(lowercase__     )

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

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

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

                   __A    =scheduler.step_prk(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample
                   __A    =new_scheduler.step_prk(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample

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

                   __A    =scheduler.step_plms(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample
                   __A    =new_scheduler.step_plms(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample

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

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             __A    =self.scheduler_classes[0]
             __A    =self.get_scheduler_config(**lowercase__     )
             __A    =scheduler_class(**lowercase__     )

             __A    =1_0
             __A    =self.dummy_model()
             __A    =self.dummy_sample_deter
             scheduler.set_timesteps(lowercase__     )

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

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

             return sample

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =dict(self.forward_default_kwargs     )

             __A    =kwargs.pop('''num_inference_steps''' ,   lowercase__     )

             for scheduler_class in self.scheduler_classes:
                   __A    =self.get_scheduler_config()
                   __A    =scheduler_class(**lowercase__     )

                   __A    =self.dummy_sample
                   __A    =0.1 * sample

                   if num_inference_steps is not None and hasattr(lowercase__ ,   '''set_timesteps'''     ):
                         scheduler.set_timesteps(lowercase__     )
                   elif num_inference_steps is not None and not hasattr(lowercase__ ,   '''set_timesteps'''     ):
                         __A    =num_inference_steps

                   # copy over dummy past residuals (must be done after set_timesteps)
                   __A    =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                   __A    =dummy_past_residuals[:]

                   __A    =scheduler.step_prk(lowercase__ ,   0 ,   lowercase__ ,   **lowercase__     ).prev_sample
                   __A    =scheduler.step_prk(lowercase__ ,   1 ,   lowercase__ ,   **lowercase__     ).prev_sample

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

                   __A    =scheduler.step_plms(lowercase__ ,   0 ,   lowercase__ ,   **lowercase__     ).prev_sample
                   __A    =scheduler.step_plms(lowercase__ ,   1 ,   lowercase__ ,   **lowercase__     ).prev_sample

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             for timesteps in [1_0_0, 1_0_0_0]:
                   self.check_over_configs(num_train_timesteps=lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

             __A    =self.scheduler_classes[0]
             __A    =self.get_scheduler_config(steps_offset=1     )
             __A    =scheduler_class(**lowercase__     )
             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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             for beta_start, beta_end in zip([0.0001, 0.001] ,   [0.002, 0.02]     ):
                   self.check_over_configs(beta_start=lowercase__ ,   beta_end=lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             for t in [1, 5, 1_0]:
                   self.check_over_forward(time_step=lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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=lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =2_7

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

                   __A    =self.get_scheduler_config()
                   __A    =scheduler_class(**lowercase__     )

                   scheduler.set_timesteps(lowercase__     )

                   # 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]     ):
                         __A    =scheduler.step_prk(lowercase__ ,   lowercase__ ,   lowercase__     ).prev_sample

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             with self.assertRaises(lowercase__     ):
                   __A    =self.scheduler_classes[0]
                   __A    =self.get_scheduler_config()
                   __A    =scheduler_class(**lowercase__     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.full_loop()
             __A    =torch.sum(torch.abs(lowercase__     )     )
             __A    =torch.mean(torch.abs(lowercase__     )     )

             assert abs(result_sum.item() - 198.1318     ) < 1E-2
             assert abs(result_mean.item() - 0.2580     ) < 1E-3

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.full_loop(prediction_type='''v_prediction'''     )
             __A    =torch.sum(torch.abs(lowercase__     )     )
             __A    =torch.mean(torch.abs(lowercase__     )     )

             assert abs(result_sum.item() - 67.3986     ) < 1E-2
             assert abs(result_mean.item() - 0.0878     ) < 1E-3

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.full_loop(set_alpha_to_one=lowercase__ ,   beta_start=0.01     )
             __A    =torch.sum(torch.abs(lowercase__     )     )
             __A    =torch.mean(torch.abs(lowercase__     )     )

             assert abs(result_sum.item() - 230.0399     ) < 1E-2
             assert abs(result_mean.item() - 0.2995     ) < 1E-3


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.full_loop(set_alpha_to_one=lowercase__ ,   beta_start=0.01     )
             __A    =torch.sum(torch.abs(lowercase__     )     )
             __A    =torch.mean(torch.abs(lowercase__     )     )

             assert abs(result_sum.item() - 186.9482     ) < 1E-2
             assert abs(result_mean.item() - 0.2434     ) < 1E-3





import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


