from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 	__snake_case	(    __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				NestedDataStructureLike[PathLike]    ,  _UpperCamelCase     :				Optional[NamedSplit] = None    ,  _UpperCamelCase     :				Optional[Features] = None    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				Optional[int] = None    ,  **_UpperCamelCase     :				Tuple    ,  )				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              _UpperCamelCase    ,  split=_UpperCamelCase    ,  features=_UpperCamelCase    ,  cache_dir=_UpperCamelCase    ,  keep_in_memory=_UpperCamelCase    ,  streaming=_UpperCamelCase    ,  num_proc=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[Any] 						=						path_or_paths if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else {self.split: path_or_paths}
          _lowerCamelCase     :		Any 						=						Text(
              cache_dir=_UpperCamelCase    ,  data_files=_UpperCamelCase    ,  features=_UpperCamelCase    ,  **_UpperCamelCase    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          if self.streaming:
              _lowerCamelCase     :		Tuple 						=						self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
              _lowerCamelCase     :		List[Any] 						=						None
              _lowerCamelCase     :		Any 						=						None
              _lowerCamelCase     :		List[str] 						=						None
              _lowerCamelCase     :		Dict 						=						None

              self.builder.download_and_prepare(
                  download_config=_UpperCamelCase    ,  download_mode=_UpperCamelCase    ,  verification_mode=_UpperCamelCase    ,  base_path=_UpperCamelCase    ,  num_proc=self.num_proc    ,  )
              _lowerCamelCase     :		Optional[int] 						=						self.builder.as_dataset(
                  split=self.split    ,  verification_mode=_UpperCamelCase    ,  in_memory=self.keep_in_memory)
          return dataset





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       (EulerDiscreteScheduler,)
      _snake_case    		=       10



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  **_UpperCamelCase     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{
              """num_train_timesteps""": 1100,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**_UpperCamelCase)
          return config



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


          """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=_UpperCamelCase    ,  beta_end=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Union[str, Any]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		str 						=						self.get_scheduler_config()
          _lowerCamelCase     :		Any 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		str 						=						torch.manual_seed(0)

          _lowerCamelCase     :		str 						=						self.dummy_model()
          _lowerCamelCase     :		List[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		int 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Dict 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Any 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[Any] 						=						self.get_scheduler_config(prediction_type="""v_prediction""")
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		Any 						=						torch.manual_seed(0)

          _lowerCamelCase     :		int 						=						self.dummy_model()
          _lowerCamelCase     :		int 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		Dict 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[Any] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						output.prev_sample

          _lowerCamelCase     :		Union[str, Any] 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Optional[int] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
          assert abs(result_mean.item() - 2.2_6_7_6E-0_6) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		int 						=						self.get_scheduler_config()
          _lowerCamelCase     :		List[Any] 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps    ,  device=_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		Optional[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Tuple 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		List[Any] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		List[Any] 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[Any] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[int] 						=						self.get_scheduler_config()
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase    ,  use_karras_sigmas=_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps    ,  device=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		str 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Optional[int] 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		Tuple 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		int 						=						output.prev_sample

          _lowerCamelCase     :		Tuple 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[str] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
          assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3




