


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 					__lowerCamelCase	(_a		):
       _lowercase    							=      (DDIMScheduler,)
       _lowercase    							=      (("""eta""", 0.0), ("""num_inference_steps""", 50))







       def 						snake_case_					(	self:				Tuple,**A_:				List[str]			):






            '''simple docstring'''




            __UpperCamelCase				=	{
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**A_			)
            return config







       def 						snake_case_					(	self:				int,**A_:				str			):






            '''simple docstring'''




            __UpperCamelCase				=	self.scheduler_classes[0]
            __UpperCamelCase				=	self.get_scheduler_config(**A_			)
            __UpperCamelCase				=	scheduler_class(**A_			)

            __UpperCamelCase,  __UpperCamelCase				=	10, 0.0

            __UpperCamelCase				=	self.dummy_model()
            __UpperCamelCase				=	self.dummy_sample_deter

            scheduler.set_timesteps(A_			)

            for t in scheduler.timesteps:
                 __UpperCamelCase				=	model(A_,A_			)
                 __UpperCamelCase				=	scheduler.step(A_,A_,A_,A_			).prev_sample

            return sample







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






            '''simple docstring'''




            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=A_			)







       def 						snake_case_					(	self:				Tuple			):






            '''simple docstring'''




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

            __UpperCamelCase				=	self.scheduler_classes[0]
            __UpperCamelCase				=	self.get_scheduler_config(steps_offset=1			)
            __UpperCamelCase				=	scheduler_class(**A_			)
            scheduler.set_timesteps(5			)
            assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1]			)			)







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






            '''simple docstring'''




            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1],[0.0_0_2, 0.0_2, 0.2, 2]			):
                 self.check_over_configs(beta_start=A_,beta_end=A_			)







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






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Tuple			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=A_			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=A_			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=A_			)







       def 						snake_case_					(	self:				Tuple			):






            '''simple docstring'''




            self.check_over_configs(thresholding=A_			)
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=A_,prediction_type=A_,sample_max_value=A_,)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=A_			)







       def 						snake_case_					(	self:				Tuple			):






            '''simple docstring'''




            for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500]			):
                 self.check_over_forward(time_step=A_,num_inference_steps=A_			)







       def 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




            for t, eta in zip([1, 10, 49],[0.0, 0.5, 1.0]			):
                 self.check_over_forward(time_step=A_,eta=A_			)







       def 						snake_case_					(	self:				List[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	self.scheduler_classes[0]
            __UpperCamelCase				=	self.get_scheduler_config()
            __UpperCamelCase				=	scheduler_class(**A_			)

            assert torch.sum(torch.abs(scheduler._get_variance(0,0			) - 0.0			)			) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420,400			) - 0.1_4_7_7_1			)			) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980,960			) - 0.3_2_4_6_0			)			) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0,0			) - 0.0			)			) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487,486			) - 0.0_0_9_7_9			)			) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999,998			) - 0.0_2			)			) < 1E-5







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	self.full_loop()

            __UpperCamelCase				=	torch.sum(torch.abs(A_			)			)
            __UpperCamelCase				=	torch.mean(torch.abs(A_			)			)

            assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1E-3







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	self.full_loop(prediction_type='v_prediction'			)

            __UpperCamelCase				=	torch.sum(torch.abs(A_			)			)
            __UpperCamelCase				=	torch.mean(torch.abs(A_			)			)

            assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4			) < 1E-3







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	self.full_loop(set_alpha_to_one=A_,beta_start=0.0_1			)
            __UpperCamelCase				=	torch.sum(torch.abs(A_			)			)
            __UpperCamelCase				=	torch.mean(torch.abs(A_			)			)

            assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1			) < 1E-3







       def 						snake_case_					(	self:				Any			):






            '''simple docstring'''




            __UpperCamelCase				=	self.full_loop(set_alpha_to_one=A_,beta_start=0.0_1			)
            __UpperCamelCase				=	torch.sum(torch.abs(A_			)			)
            __UpperCamelCase				=	torch.mean(torch.abs(A_			)			)

            assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1			) < 1E-3






import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__snake_case     =		2 << 2_0  # 2mb
__snake_case     =		get_logger(__name__)



class 					__lowerCamelCase	(_a		):







       def 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	self._options.get_all_options().get('runner'			)
            return runner in [None, "DirectRunner", "PortableRunner"]






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase=False				)			->				List[Any]:


     """simple docstring"""


     __UpperCamelCase				=	FileSystems
     if fs.exists(_lowercase				):
          if force_upload:
               logger.info(f'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''				)
          else:
               logger.info(f'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''				)
               return
     with fs.create(_lowercase				) as remote_file:
          with open(_lowercase			,       'rb'				) as local_file:
               __UpperCamelCase				=	local_file.read(_lowercase				)
               while chunk:
                    remote_file.write(_lowercase				)
                    __UpperCamelCase				=	local_file.read(_lowercase				)






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase=False				)			->				Optional[Any]:


     """simple docstring"""


     __UpperCamelCase				=	FileSystems
     if os.path.exists(_lowercase				):
          if force_download:
               logger.info(f'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''				)
          else:
               logger.info(f'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''				)
               return
     with fs.open(_lowercase				) as remote_file:
          with open(_lowercase			,       'wb'				) as local_file:
               __UpperCamelCase				=	remote_file.read(_lowercase				)
               while chunk:
                    local_file.write(_lowercase				)
                    __UpperCamelCase				=	remote_file.read(_lowercase				)


