


'''simple docstring'''
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     (IPNDMScheduler,)
      lowerCAmelCase__		     =     (('num_inference_steps', 5_0),)






      def lowercase_							(					self				:    Tuple						,						**_A				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				{'''num_train_timesteps''': 1_000}
          config.update(**_A )
          return config






      def lowercase_							(					self				:    List[str]						,						_A				:    Optional[int]=0						,						**_A				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				dict(self.forward_default_kwargs )
          UpperCAmelCase__			:		Optional[Any]													=				kwargs.pop('''num_inference_steps'''						,						_A )
          UpperCAmelCase__			:		str													=				self.dummy_sample
          UpperCAmelCase__			:		Optional[Any]													=				0.1 * sample
          UpperCAmelCase__			:		int													=				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__			:		Tuple													=				self.get_scheduler_config(**_A )
              UpperCAmelCase__			:		Dict													=				scheduler_class(**_A )
              scheduler.set_timesteps(_A )
              # copy over dummy past residuals
              UpperCAmelCase__			:		str													=				dummy_past_residuals[:]

              if time_step is None:
                  UpperCAmelCase__			:		Union[str, Any]													=				scheduler.timesteps[len(scheduler.timesteps ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_A )
                  UpperCAmelCase__			:		List[Any]													=				scheduler_class.from_pretrained(_A )
                  new_scheduler.set_timesteps(_A )
                  # copy over dummy past residuals
                  UpperCAmelCase__			:		Union[str, Any]													=				dummy_past_residuals[:]

              UpperCAmelCase__			:		Union[str, Any]													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		Union[str, Any]													=				new_scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample

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

              UpperCAmelCase__			:		str													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		str													=				new_scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample

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






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    List[str]						,						_A				:    str=0						,						**_A				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				dict(self.forward_default_kwargs )
          UpperCAmelCase__			:		Any													=				kwargs.pop('''num_inference_steps'''						,						_A )
          UpperCAmelCase__			:		Optional[int]													=				self.dummy_sample
          UpperCAmelCase__			:		str													=				0.1 * sample
          UpperCAmelCase__			:		str													=				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__			:		Union[str, Any]													=				self.get_scheduler_config()
              UpperCAmelCase__			:		Any													=				scheduler_class(**_A )
              scheduler.set_timesteps(_A )

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

              if time_step is None:
                  UpperCAmelCase__			:		Optional[Any]													=				scheduler.timesteps[len(scheduler.timesteps ) // 2]

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

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

              UpperCAmelCase__			:		List[Any]													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		Dict													=				new_scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample

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

              UpperCAmelCase__			:		Any													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		Union[str, Any]													=				new_scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample

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






      def lowercase_							(					self				:    Optional[Any]						,						**_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.scheduler_classes[0]
          UpperCAmelCase__			:		List[str]													=				self.get_scheduler_config(**_A )
          UpperCAmelCase__			:		int													=				scheduler_class(**_A )

          UpperCAmelCase__			:		Union[str, Any]													=				10
          UpperCAmelCase__			:		Dict													=				self.dummy_model()
          UpperCAmelCase__			:		List[Any]													=				self.dummy_sample_deter
          scheduler.set_timesteps(_A )

          for i, t in enumerate(scheduler.timesteps ):
              UpperCAmelCase__			:		str													=				model(_A						,						_A )
              UpperCAmelCase__			:		Tuple													=				scheduler.step(_A						,						_A						,						_A ).prev_sample

          for i, t in enumerate(scheduler.timesteps ):
              UpperCAmelCase__			:		Dict													=				model(_A						,						_A )
              UpperCAmelCase__			:		Tuple													=				scheduler.step(_A						,						_A						,						_A ).prev_sample

          return sample






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				dict(self.forward_default_kwargs )

          UpperCAmelCase__			:		Tuple													=				kwargs.pop('''num_inference_steps'''						,						_A )

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__			:		Dict													=				self.get_scheduler_config()
              UpperCAmelCase__			:		Dict													=				scheduler_class(**_A )

              UpperCAmelCase__			:		int													=				self.dummy_sample
              UpperCAmelCase__			:		Optional[Any]													=				0.1 * sample

              if num_inference_steps is not None and hasattr(_A						,						'''set_timesteps''' ):
                  scheduler.set_timesteps(_A )
              elif num_inference_steps is not None and not hasattr(_A						,						'''set_timesteps''' ):
                  UpperCAmelCase__			:		Any													=				num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCAmelCase__			:		str													=				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
              UpperCAmelCase__			:		str													=				dummy_past_residuals[:]

              UpperCAmelCase__			:		Union[str, Any]													=				scheduler.timesteps[5]
              UpperCAmelCase__			:		Any													=				scheduler.timesteps[6]

              UpperCAmelCase__			:		Tuple													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		Optional[Any]													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample

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

              UpperCAmelCase__			:		List[str]													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		List[Any]													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample

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






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=_A						,						time_step=_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          for t, num_inference_steps in zip([1, 5, 10]						,						[10, 50, 100] ):
              self.check_over_forward(num_inference_steps=_A						,						time_step=_A )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.full_loop()
          UpperCAmelCase__			:		Union[str, Any]													=				torch.mean(torch.abs(_A ) )

          assert abs(result_mean.item() - 2_540_529 ) < 10




'''simple docstring'''
import re

from filelock import FileLock


try:
  import nltk

  UpperCamelCase__    					=      True
except (ImportError, ModuleNotFoundError):
  UpperCamelCase__    					=      False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)





def 					a__       (					lowerCAmelCase__						)    ->	str:
    re.sub('''<n>'''  ,					''''''  ,					lowerCAmelCase__						)  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(lowerCAmelCase__						)						)
