


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					__lowerCamelCase	(UpperCamelCase_		):
       _lowercase    							=      (EulerAncestralDiscreteScheduler,)
       _lowercase    							=      10







       def 						snake_case_					(	self:				Dict,**A_:				List[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	{
                'num_train_timesteps': 1100,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
            }

            config.update(**__SCREAMING_SNAKE_CASE			)
            return config







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE			)







       def 						snake_case_					(	self:				Tuple			):






            '''simple docstring'''




            for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
                 self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE,beta_end=__SCREAMING_SNAKE_CASE			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




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

            scheduler.set_timesteps(self.num_inference_steps			)

            __UpperCamelCase				=	torch.manual_seed(0			)

            __UpperCamelCase				=	self.dummy_model()
            __UpperCamelCase				=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            __UpperCamelCase				=	sample.to(__SCREAMING_SNAKE_CASE			)

            for i, t in enumerate(scheduler.timesteps			):
                 __UpperCamelCase				=	scheduler.scale_model_input(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

                 __UpperCamelCase				=	model(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

                 __UpperCamelCase				=	scheduler.step(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE			)
                 __UpperCamelCase				=	output.prev_sample

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

            assert abs(result_sum.item() - 152.3192			) < 1E-2
            assert abs(result_mean.item() - 0.1_9_8_3			) < 1E-3







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	self.scheduler_classes[0]
            __UpperCamelCase				=	self.get_scheduler_config(prediction_type='v_prediction'			)
            __UpperCamelCase				=	scheduler_class(**__SCREAMING_SNAKE_CASE			)

            scheduler.set_timesteps(self.num_inference_steps			)

            __UpperCamelCase				=	torch.manual_seed(0			)

            __UpperCamelCase				=	self.dummy_model()
            __UpperCamelCase				=	self.dummy_sample_deter * scheduler.init_noise_sigma
            __UpperCamelCase				=	sample.to(__SCREAMING_SNAKE_CASE			)

            for i, t in enumerate(scheduler.timesteps			):
                 __UpperCamelCase				=	scheduler.scale_model_input(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

                 __UpperCamelCase				=	model(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

                 __UpperCamelCase				=	scheduler.step(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE			)
                 __UpperCamelCase				=	output.prev_sample

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

            assert abs(result_sum.item() - 108.4439			) < 1E-2
            assert abs(result_mean.item() - 0.1_4_1_2			) < 1E-3







       def 						snake_case_					(	self:				Tuple			):






            '''simple docstring'''




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

            scheduler.set_timesteps(self.num_inference_steps,device=__SCREAMING_SNAKE_CASE			)
            __UpperCamelCase				=	torch.manual_seed(0			)

            __UpperCamelCase				=	self.dummy_model()
            __UpperCamelCase				=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            __UpperCamelCase				=	sample.to(__SCREAMING_SNAKE_CASE			)

            for t in scheduler.timesteps:
                 __UpperCamelCase				=	scheduler.scale_model_input(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

                 __UpperCamelCase				=	model(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

                 __UpperCamelCase				=	scheduler.step(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE			)
                 __UpperCamelCase				=	output.prev_sample

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

            assert abs(result_sum.item() - 152.3192			) < 1E-2
            assert abs(result_mean.item() - 0.1_9_8_3			) < 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				)


