





'''simple docstring'''






import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar




class  UpperCAmelCase     (			unittest.TestCase   ):




      def 				lowercase__    (			self			: List[Any]  )				->      Optional[Any]:
            _lowerCAmelCase							     =							logging.get_logger()

            # the current default level is logging.WARNING
            _lowerCAmelCase							     =							logging.get_verbosity()

            logging.set_verbosity_error()
            self.assertEqual(logger.getEffectiveLevel()			,			logging.get_verbosity()  )

            logging.set_verbosity_warning()
            self.assertEqual(logger.getEffectiveLevel()			,			logging.get_verbosity()  )

            logging.set_verbosity_info()
            self.assertEqual(logger.getEffectiveLevel()			,			logging.get_verbosity()  )

            logging.set_verbosity_debug()
            self.assertEqual(logger.getEffectiveLevel()			,			logging.get_verbosity()  )

            # restore to the original level
            logging.set_verbosity(__snake_case  )




      def 				lowercase__    (			self			: int  )				->      str:
            _lowerCAmelCase							     =							logging.get_verbosity()

            _lowerCAmelCase							     =							logging.get_logger("""transformers.models.bart.tokenization_bart"""  )
            _lowerCAmelCase							     =							"""Testing 1, 2, 3"""

            # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
            if level_origin <= logging.WARNING:
                  with CaptureLogger(__snake_case  ) as cl:
                        logger.warning(__snake_case  )
                  self.assertEqual(cl.out			,			msg + """\n"""  )

            # this is setting the level for all of `transformers.*` loggers
            logging.set_verbosity_error()

            # should not be able to log warnings
            with CaptureLogger(__snake_case  ) as cl:
                  logger.warning(__snake_case  )
            self.assertEqual(cl.out			,			""""""  )

            # should be able to log warnings again
            logging.set_verbosity_warning()
            with CaptureLogger(__snake_case  ) as cl:
                  logger.warning(__snake_case  )
            self.assertEqual(cl.out			,			msg + """\n"""  )

            # restore to the original level
            logging.set_verbosity(__snake_case  )




      @mockenv(TRANSFORMERS_VERBOSITY="""error"""  )
      def 				lowercase__    (			self			: Tuple  )				->      Tuple:
            # reset for the env var to take effect, next time some logger call is made
            transformers.utils.logging._reset_library_root_logger()
            # this action activates the env var
            _lowerCAmelCase							     =							logging.get_logger("""transformers.models.bart.tokenization_bart"""  )

            _lowerCAmelCase							     =							os.getenv("""TRANSFORMERS_VERBOSITY"""			,			__snake_case  )
            _lowerCAmelCase							     =							logging.log_levels[env_level_str]

            _lowerCAmelCase							     =							logging.get_verbosity()
            self.assertEqual(
                __snake_case			,			__snake_case			,			f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"			,			)

            # restore to the original level
            _lowerCAmelCase							     =							""""""
            transformers.utils.logging._reset_library_root_logger()




      @mockenv(TRANSFORMERS_VERBOSITY="""super-error"""  )
      def 				lowercase__    (			self			: Optional[int]  )				->      Any:
            # reset for the env var to take effect, next time some logger call is made
            transformers.utils.logging._reset_library_root_logger()
            _lowerCAmelCase							     =							logging.logging.getLogger()
            with CaptureLogger(__snake_case  ) as cl:
                  # this action activates the env var
                  logging.get_logger("""transformers.models.bart.tokenization_bart"""  )
            self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""			,			cl.out  )

            # no need to restore as nothing was changed




      def 				lowercase__    (			self			: Dict  )				->      Any:
            # testing `logger.warning_advice()`
            transformers.utils.logging._reset_library_root_logger()

            _lowerCAmelCase							     =							logging.get_logger("""transformers.models.bart.tokenization_bart"""  )
            _lowerCAmelCase							     =							"""Testing 1, 2, 3"""

            with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""  ):
                  # nothing should be logged as env var disables this method
                  with CaptureLogger(__snake_case  ) as cl:
                        logger.warning_advice(__snake_case  )
                  self.assertEqual(cl.out			,			""""""  )

            with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""  ):
                  # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
                  with CaptureLogger(__snake_case  ) as cl:
                        logger.warning_advice(__snake_case  )
                  self.assertEqual(cl.out			,			msg + """\n"""  )




def    UpperCamelCase__      (		):





      """simple docstring"""


      disable_progress_bar()
      assert are_progress_bars_disabled()

      enable_progress_bar()
      assert not are_progress_bars_disabled()










'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					(IPNDMScheduler,)
    lowercase    =					(('num_inference_steps', 50),)

    def __lowercase   (   self    :  Optional[Any]    ,						**lowerCamelCase    :  int		)   ->  Tuple:
      lowerCAmelCase_			:      Dict			=   {"""num_train_timesteps""": 10_00}
      config.update(**lowerCamelCase		)
      return config

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Tuple=0    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      List[str]			=   self.get_scheduler_config(**lowerCamelCase		)
        lowerCAmelCase_			:      int			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)
        # copy over dummy past residuals
        lowerCAmelCase_			:      Optional[int]			=   dummy_past_residuals[:]

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

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

        lowerCAmelCase_			:      int			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Dict			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Any			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  str		)   ->  Tuple:
      pass

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  int=0    ,						**lowerCamelCase    :  Any		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      List[str]			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      List[Any]			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config()
        lowerCAmelCase_			:      Optional[int]			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)

        # copy over dummy past residuals (must be after setting timesteps)
        lowerCAmelCase_			:      int			=   dummy_past_residuals[:]

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

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      Optional[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          # copy over dummy past residuals
          new_scheduler.set_timesteps(lowerCamelCase		)

          # copy over dummy past residual (must be after setting timesteps)
          lowerCAmelCase_			:      Union[str, Any]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[Any]			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      str			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  Optional[int]    ,						**lowerCamelCase    :  Optional[Any]		)   ->  str:
      lowerCAmelCase_			:      Optional[int]			=   self.scheduler_classes[0]
      lowerCAmelCase_			:      List[Any]			=   self.get_scheduler_config(**lowerCamelCase		)
      lowerCAmelCase_			:      str			=   scheduler_class(**lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   10
      lowerCAmelCase_			:      Optional[int]			=   self.dummy_model()
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCamelCase		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      str			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      return sample

    def __lowercase   (   self    :  Tuple		)   ->  Dict:
      lowerCAmelCase_			:      List[Any]			=   dict(self.forward_default_kwargs		)

      lowerCAmelCase_			:      int			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Tuple			=   self.get_scheduler_config()
        lowerCAmelCase_			:      List[str]			=   scheduler_class(**lowerCamelCase		)

        lowerCAmelCase_			:      int			=   self.dummy_sample
        lowerCAmelCase_			:      Tuple			=   0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          scheduler.set_timesteps(lowerCamelCase		)
        elif num_inference_steps is not None and not hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          lowerCAmelCase_			:      Dict			=   num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        lowerCAmelCase_			:      List[str]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      List[str]			=   scheduler.timesteps[5]
        lowerCAmelCase_			:      str			=   scheduler.timesteps[6]

        lowerCAmelCase_			:      Dict			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[int]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  Any		)   ->  int:
      for timesteps in [1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase    ,						time_step=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]		)   ->  Optional[int]:
      for t, num_inference_steps in zip([1, 5, 10]    ,						[10, 50, 1_00]		):
        self.check_over_forward(num_inference_steps=lowerCamelCase    ,						time_step=lowerCamelCase		)





    def __lowercase   (   self    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      List[Any]			=   self.full_loop()
      lowerCAmelCase_			:      Any			=   torch.mean(torch.abs(lowerCamelCase		)		)

      assert abs(result_mean.item() - 2_54_05_29		) < 10



