





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      (DPMSolverSinglestepScheduler,)
      UpperCAmelCase__						=      (("""num_inference_steps""", 25),)


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		{
                 '''num_train_timesteps''': 1000,
                 '''beta_start''': 0.0001,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
                 '''solver_order''': 2,
                 '''prediction_type''': '''epsilon''',
                 '''thresholding''': False,
                 '''sample_max_value''': 1.0,
                 '''algorithm_type''': '''dpmsolver++''',
                 '''solver_type''': '''midpoint''',
                 '''lambda_min_clipped''': -float('''inf'''),
                 '''variance_type''': None,
             }

             config.update(**a__)
             return config


      def 				__snake_case			(		self      ,							a__=0      ,							**a__):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		dict(self.forward_default_kwargs)
             _lowerCamelCase							:     Tuple	=		kwargs.pop('''num_inference_steps'''      ,							a__)
             _lowerCamelCase							:     Dict	=		self.dummy_sample
             _lowerCamelCase							:     Union[str, Any]	=		0.1 * sample
             _lowerCamelCase							:     Optional[Any]	=		[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase							:     Dict	=		self.get_scheduler_config(**a__)
                    _lowerCamelCase							:     List[str]	=		scheduler_class(**a__)
                    scheduler.set_timesteps(a__)
                    # copy over dummy past residuals
                    _lowerCamelCase							:     List[Any]	=		dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(a__)
                           _lowerCamelCase							:     Tuple	=		scheduler_class.from_pretrained(a__)
                           new_scheduler.set_timesteps(a__)
                           # copy over dummy past residuals
                           _lowerCamelCase							:     Optional[Any]	=		dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		sample, sample
                    for t in range(a__      ,							time_step + scheduler.config.solver_order + 1):
                           _lowerCamelCase							:     Optional[int]	=		scheduler.step(a__      ,							a__      ,							a__      ,							**a__).prev_sample
                           _lowerCamelCase							:     int	=		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 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self      ,							a__=0      ,							**a__):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		dict(self.forward_default_kwargs)
             _lowerCamelCase							:     Dict	=		kwargs.pop('''num_inference_steps'''      ,							a__)
             _lowerCamelCase							:     Optional[Any]	=		self.dummy_sample
             _lowerCamelCase							:     Any	=		0.1 * sample
             _lowerCamelCase							:     Optional[int]	=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

                    # copy over dummy past residuals (must be after setting timesteps)
                    _lowerCamelCase							:     str	=		dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(a__)
                           _lowerCamelCase							:     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)
                           _lowerCamelCase							:     Optional[int]	=		dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase							:     Any	=		scheduler.step(a__      ,							a__      ,							a__      ,							**a__).prev_sample
                    _lowerCamelCase							:     Tuple	=		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 				__snake_case			(		self      ,							a__=None      ,							**a__):



             """simple docstring"""





             if scheduler is None:
                    _lowerCamelCase							:     str	=		self.scheduler_classes[0]
                    _lowerCamelCase							:     List[str]	=		self.get_scheduler_config(**a__)
                    _lowerCamelCase							:     List[str]	=		scheduler_class(**a__)

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

             _lowerCamelCase							:     List[str]	=		10
             _lowerCamelCase							:     Optional[Any]	=		self.dummy_model()
             _lowerCamelCase							:     str	=		self.dummy_sample_deter
             scheduler.set_timesteps(a__)

             for i, t in enumerate(scheduler.timesteps):
                    _lowerCamelCase							:     List[Any]	=		model(a__      ,							a__)
                    _lowerCamelCase							:     str	=		scheduler.step(a__      ,							a__      ,							a__).prev_sample

             return sample


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		DPMSolverSinglestepScheduler(**self.get_scheduler_config())
             _lowerCamelCase							:     str	=		50
             _lowerCamelCase							:     Tuple	=		self.dummy_model()
             _lowerCamelCase							:     Optional[Any]	=		self.dummy_sample_deter
             scheduler.set_timesteps(a__)

             # make sure that the first t is uneven
             for i, t in enumerate(scheduler.timesteps[3:]):
                    _lowerCamelCase							:     Any	=		model(a__      ,							a__)
                    _lowerCamelCase							:     List[Any]	=		scheduler.step(a__      ,							a__      ,							a__).prev_sample

             _lowerCamelCase							:     Any	=		torch.mean(torch.abs(a__))

             assert abs(result_mean.item() - 0.2574) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		DPMSolverSinglestepScheduler(**self.get_scheduler_config())
             _lowerCamelCase							:     List[Any]	=		self.full_loop(scheduler=a__)
             _lowerCamelCase							:     Optional[int]	=		torch.mean(torch.abs(a__))

             assert abs(result_mean.item() - 0.2791) < 1e-3

             _lowerCamelCase							:     List[Any]	=		DEISMultistepScheduler.from_config(scheduler.config)
             _lowerCamelCase							:     Dict	=		DPMSolverMultistepScheduler.from_config(scheduler.config)
             _lowerCamelCase							:     Dict	=		UniPCMultistepScheduler.from_config(scheduler.config)
             _lowerCamelCase							:     str	=		DPMSolverSinglestepScheduler.from_config(scheduler.config)

             _lowerCamelCase							:     str	=		self.full_loop(scheduler=a__)
             _lowerCamelCase							:     List[Any]	=		torch.mean(torch.abs(a__))

             assert abs(result_mean.item() - 0.2791) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             self.check_over_configs(thresholding=a__)
             for order in [1, 2, 3]:
                    for solver_type in ["midpoint", "heun"]:
                           for threshold in [0.5, 1.0, 2.0]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             thresholding=a__      ,							prediction_type=a__      ,							sample_max_value=a__      ,							algorithm_type='''dpmsolver++'''      ,							solver_order=a__      ,							solver_type=a__      ,							)


      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                    for solver_type in ["midpoint", "heun"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             solver_order=a__      ,							solver_type=a__      ,							prediction_type=a__      ,							algorithm_type=a__      ,							)
                                         _lowerCamelCase							:     Tuple	=		self.full_loop(
                                             solver_order=a__      ,							solver_type=a__      ,							prediction_type=a__      ,							algorithm_type=a__      ,							)
                                         assert not torch.isnan(a__).any(), "Samples have nan numbers"


      def 				__snake_case			(		self):



             """simple docstring"""





             self.check_over_configs(lower_order_final=a__)
             self.check_over_configs(lower_order_final=a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             self.check_over_configs(lambda_min_clipped=-float('''inf'''))
             self.check_over_configs(lambda_min_clipped=-5.1)


      def 				__snake_case			(		self):



             """simple docstring"""





             self.check_over_configs(variance_type=a__)
             self.check_over_configs(variance_type='''learned_range''')


      def 				__snake_case			(		self):



             """simple docstring"""





             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=a__      ,							time_step=0)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.full_loop()
             _lowerCamelCase							:     Tuple	=		torch.mean(torch.abs(a__))

             assert abs(result_mean.item() - 0.2791) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		self.full_loop(use_karras_sigmas=a__)
             _lowerCamelCase							:     List[Any]	=		torch.mean(torch.abs(a__))

             assert abs(result_mean.item() - 0.2248) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.full_loop(prediction_type='''v_prediction''')
             _lowerCamelCase							:     Optional[int]	=		torch.mean(torch.abs(a__))

             assert abs(result_mean.item() - 0.1453) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.full_loop(prediction_type='''v_prediction'''      ,							use_karras_sigmas=a__)
             _lowerCamelCase							:     Tuple	=		torch.mean(torch.abs(a__))

             assert abs(result_mean.item() - 0.0649) < 1e-3



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		self.scheduler_classes[0]
             _lowerCamelCase							:     List[Any]	=		self.get_scheduler_config(thresholding=a__      ,							dynamic_thresholding_ratio=0)
             _lowerCamelCase							:     Dict	=		scheduler_class(**a__)

             _lowerCamelCase							:     Dict	=		10
             _lowerCamelCase							:     Optional[int]	=		self.dummy_model()
             _lowerCamelCase							:     int	=		self.dummy_sample_deter.half()
             scheduler.set_timesteps(a__)

             for i, t in enumerate(scheduler.timesteps):
                    _lowerCamelCase							:     Union[str, Any]	=		model(a__      ,							a__)
                    _lowerCamelCase							:     Union[str, Any]	=		scheduler.step(a__      ,							a__      ,							a__).prev_sample

             assert sample.dtype == torch.floataa








import warnings

from .generation import TFGenerationMixin







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,lowerCamelCase__ ,)

