import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(EulerAncestralDiscreteScheduler,)
	__SCREAMING_SNAKE_CASE              =					10


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   ={
		    "num_train_timesteps": 1100,
		    "beta_start": 0.0_0_0_1,
		    "beta_end": 0.0_2,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase_       )
		return config


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


	def __lowerCAmelCase      (   self       ):
		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=lowerCAmelCase_       ,  beta_end=lowerCAmelCase_       )


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


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.scheduler_classes[0]
		_lowercase   =self.get_scheduler_config()
		_lowercase   =scheduler_class(**lowerCAmelCase_       )

		scheduler.set_timesteps(self.num_inference_steps       )

		_lowercase   =torch.manual_seed(0       )

		_lowercase   =self.dummy_model()
		_lowercase   =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		_lowercase   =sample.to(lowerCAmelCase_       )

		for i, t in enumerate(scheduler.timesteps       ):
			_lowercase   =scheduler.scale_model_input(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =model(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =scheduler.step(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  generator=lowerCAmelCase_       )
			_lowercase   =output.prev_sample

		_lowercase   =torch.sum(torch.abs(lowerCAmelCase_       )       )
		_lowercase   =torch.mean(torch.abs(lowerCAmelCase_       )       )

		assert abs(result_sum.item() - 1_5_2.3_1_9_2       ) < 1e-2
		assert abs(result_mean.item() - 0.1_9_8_3       ) < 1e-3


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.scheduler_classes[0]
		_lowercase   =self.get_scheduler_config(prediction_type="v_prediction"       )
		_lowercase   =scheduler_class(**lowerCAmelCase_       )

		scheduler.set_timesteps(self.num_inference_steps       )

		_lowercase   =torch.manual_seed(0       )

		_lowercase   =self.dummy_model()
		_lowercase   =self.dummy_sample_deter * scheduler.init_noise_sigma
		_lowercase   =sample.to(lowerCAmelCase_       )

		for i, t in enumerate(scheduler.timesteps       ):
			_lowercase   =scheduler.scale_model_input(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =model(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =scheduler.step(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  generator=lowerCAmelCase_       )
			_lowercase   =output.prev_sample

		_lowercase   =torch.sum(torch.abs(lowerCAmelCase_       )       )
		_lowercase   =torch.mean(torch.abs(lowerCAmelCase_       )       )

		assert abs(result_sum.item() - 1_0_8.4_4_3_9       ) < 1e-2
		assert abs(result_mean.item() - 0.1_4_1_2       ) < 1e-3


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.scheduler_classes[0]
		_lowercase   =self.get_scheduler_config()
		_lowercase   =scheduler_class(**lowerCAmelCase_       )

		scheduler.set_timesteps(self.num_inference_steps       ,  device=lowerCAmelCase_       )
		_lowercase   =torch.manual_seed(0       )

		_lowercase   =self.dummy_model()
		_lowercase   =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		_lowercase   =sample.to(lowerCAmelCase_       )

		for t in scheduler.timesteps:
			_lowercase   =scheduler.scale_model_input(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =model(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =scheduler.step(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  generator=lowerCAmelCase_       )
			_lowercase   =output.prev_sample

		_lowercase   =torch.sum(torch.abs(lowerCAmelCase_       )       )
		_lowercase   =torch.mean(torch.abs(lowerCAmelCase_       )       )

		assert abs(result_sum.item() - 1_5_2.3_1_9_2       ) < 1e-2
		assert abs(result_mean.item() - 0.1_9_8_3       ) < 1e-3








"""simple docstring"""


import os

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

from .logging import get_logger


a_											=     2 << 20  # 2mb
a_											=     get_logger(__name__)
class snake_case   (	_UpperCamelCase):







		def 			a_			(       self   :							Tuple		)							->    Optional[int]:





						'''simple docstring'''







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






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Any:
				_A       	=      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:
												_A       	=      local_file.read(__lowercase )
												while chunk:
																remote_file.write(__lowercase )
																_A       	=      local_file.read(__lowercase )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Union[str, Any]:
				_A       	=      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:
												_A       	=      remote_file.read(__lowercase )
												while chunk:
																local_file.write(__lowercase )
																_A       	=      remote_file.read(__lowercase )