import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Dict      =				(DPMSolverSinglestepScheduler,)
    a__     :						List[Any]      =				(("""num_inference_steps""", 2_5),)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Dict:
        __UpperCamelCase					:Optional[Any]        =		{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.00_01,
            '''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(**__lowercase)
        return config







    def       UpperCamelCase__       (     self			,			__lowercase=0			,			**__lowercase)	->     Tuple:
        __UpperCamelCase					:str        =		dict(self.forward_default_kwargs)
        __UpperCamelCase					:Dict        =		kwargs.pop('''num_inference_steps'''			,			__lowercase)
        __UpperCamelCase					:str        =		self.dummy_sample
        __UpperCamelCase					:Dict        =		0.1 * sample
        __UpperCamelCase					:Optional[int]        =		[residual + 0.2, residual + 0.15, residual + 0.10]

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(__lowercase)
                __UpperCamelCase					:str        =		scheduler_class.from_pretrained(__lowercase)
                new_scheduler.set_timesteps(__lowercase)
                # copy over dummy past residuals
                __UpperCamelCase					:str        =		dummy_past_residuals[: new_scheduler.config.solver_order]

            __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		sample, sample
            for t in range(__lowercase			,			time_step + scheduler.config.solver_order + 1):
                __UpperCamelCase					:Any        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample
                __UpperCamelCase					:Any        =		new_scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample

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







    def       UpperCamelCase__       (     self)	->     Any:
        pass







    def       UpperCamelCase__       (     self			,			__lowercase=0			,			**__lowercase)	->     int:
        __UpperCamelCase					:Optional[Any]        =		dict(self.forward_default_kwargs)
        __UpperCamelCase					:Dict        =		kwargs.pop('''num_inference_steps'''			,			__lowercase)
        __UpperCamelCase					:List[Any]        =		self.dummy_sample
        __UpperCamelCase					:str        =		0.1 * sample
        __UpperCamelCase					:Tuple        =		[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            __UpperCamelCase					:Optional[Any]        =		self.get_scheduler_config()
            __UpperCamelCase					:List[str]        =		scheduler_class(**__lowercase)
            scheduler.set_timesteps(__lowercase)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(__lowercase)
                __UpperCamelCase					:str        =		scheduler_class.from_pretrained(__lowercase)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(__lowercase)

                # copy over dummy past residual (must be after setting timesteps)
                __UpperCamelCase					:str        =		dummy_past_residuals[: new_scheduler.config.solver_order]

            __UpperCamelCase					:int        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample
            __UpperCamelCase					:Dict        =		new_scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample

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







    def       UpperCamelCase__       (     self			,			__lowercase=None			,			**__lowercase)	->     List[str]:
        if scheduler is None:
            __UpperCamelCase					:int        =		self.scheduler_classes[0]
            __UpperCamelCase					:Dict        =		self.get_scheduler_config(**__lowercase)
            __UpperCamelCase					:Dict        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:Optional[int]        =		self.scheduler_classes[0]
        __UpperCamelCase					:str        =		self.get_scheduler_config(**__lowercase)
        __UpperCamelCase					:Optional[int]        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:Tuple        =		10
        __UpperCamelCase					:str        =		self.dummy_model()
        __UpperCamelCase					:List[Any]        =		self.dummy_sample_deter
        scheduler.set_timesteps(__lowercase)

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

        return sample







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:List[str]        =		DPMSolverSinglestepScheduler(**self.get_scheduler_config())
        __UpperCamelCase					:Any        =		50
        __UpperCamelCase					:Optional[Any]        =		self.dummy_model()
        __UpperCamelCase					:Optional[int]        =		self.dummy_sample_deter
        scheduler.set_timesteps(__lowercase)

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

        __UpperCamelCase					:Tuple        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.25_74) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[Any]:
        for timesteps in [25, 50, 100, 999, 1_000]:
            self.check_over_configs(num_train_timesteps=__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        __UpperCamelCase					:Any        =		DPMSolverSinglestepScheduler(**self.get_scheduler_config())
        __UpperCamelCase					:List[str]        =		self.full_loop(scheduler=__lowercase)
        __UpperCamelCase					:Dict        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.27_91) < 1E-3

        __UpperCamelCase					:Dict        =		DEISMultistepScheduler.from_config(scheduler.config)
        __UpperCamelCase					:List[str]        =		DPMSolverMultistepScheduler.from_config(scheduler.config)
        __UpperCamelCase					:int        =		UniPCMultistepScheduler.from_config(scheduler.config)
        __UpperCamelCase					:Union[str, Any]        =		DPMSolverSinglestepScheduler.from_config(scheduler.config)

        __UpperCamelCase					:int        =		self.full_loop(scheduler=__lowercase)
        __UpperCamelCase					:List[Any]        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.27_91) < 1E-3







    def       UpperCamelCase__       (     self)	->     Tuple:
        self.check_over_configs(thresholding=__lowercase)
        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=__lowercase			,			prediction_type=__lowercase			,			sample_max_value=__lowercase			,			algorithm_type='''dpmsolver++'''			,			solver_order=__lowercase			,			solver_type=__lowercase			,			)







    def       UpperCamelCase__       (     self)	->     int:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowercase)







    def       UpperCamelCase__       (     self)	->     Tuple:
        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=__lowercase			,			solver_type=__lowercase			,			prediction_type=__lowercase			,			algorithm_type=__lowercase			,			)
                        __UpperCamelCase					:List[str]        =		self.full_loop(
                            solver_order=__lowercase			,			solver_type=__lowercase			,			prediction_type=__lowercase			,			algorithm_type=__lowercase			,			)
                        assert not torch.isnan(__lowercase).any(), "Samples have nan numbers"







    def       UpperCamelCase__       (     self)	->     Dict:
        self.check_over_configs(lower_order_final=__lowercase)
        self.check_over_configs(lower_order_final=__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        self.check_over_configs(lambda_min_clipped=-float('''inf'''))
        self.check_over_configs(lambda_min_clipped=-5.1)







    def       UpperCamelCase__       (     self)	->     Dict:
        self.check_over_configs(variance_type=__lowercase)
        self.check_over_configs(variance_type='''learned_range''')







    def       UpperCamelCase__       (     self)	->     Tuple:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
            self.check_over_forward(num_inference_steps=__lowercase			,			time_step=0)







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:int        =		self.full_loop()
        __UpperCamelCase					:Dict        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.27_91) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[str]        =		self.full_loop(use_karras_sigmas=__lowercase)
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.22_48) < 1E-3







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:Optional[Any]        =		self.full_loop(prediction_type='''v_prediction''')
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.14_53) < 1E-3







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:Any        =		self.full_loop(prediction_type='''v_prediction'''			,			use_karras_sigmas=__lowercase)
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.06_49) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:int        =		self.scheduler_classes[0]
        __UpperCamelCase					:Optional[int]        =		self.get_scheduler_config(thresholding=__lowercase			,			dynamic_thresholding_ratio=0)
        __UpperCamelCase					:List[Any]        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:Dict        =		10
        __UpperCamelCase					:Optional[Any]        =		self.dummy_model()
        __UpperCamelCase					:Dict        =		self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowercase)

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

        assert sample.dtype == torch.floataa






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





