





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:str							=     (DPMSolverSinglestepScheduler,)
      _UpperCAmelCase			:Any							=     (('num_inference_steps', 2_5),)





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     {
           "num_train_timesteps": 1000,
           "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(**A_     )
       return config





      def  __UpperCamelCase(				self ,							A_=0 ,							**A_     ):
       '''simple docstring'''




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

       for scheduler_class in self.scheduler_classes:
        UpperCamelCase			:      int 			=     self.get_scheduler_config(**A_     )
        UpperCamelCase			:      str 			=     scheduler_class(**A_     )
        scheduler.set_timesteps(A_     )
        # copy over dummy past residuals
        UpperCamelCase			:      Dict 			=     dummy_past_residuals[: scheduler.config.solver_order]

        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			:      Any 			=     dummy_past_residuals[: new_scheduler.config.solver_order]

        UpperCamelCase			,					UpperCamelCase			:      str 			=     sample, sample
        for t in range(A_ ,							time_step + scheduler.config.solver_order + 1     ):
         UpperCamelCase			:      Union[str, Any] 			=     scheduler.step(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample
         UpperCamelCase			:      List[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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self ,							A_=0 ,							**A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     dict(self.forward_default_kwargs     )
       UpperCamelCase			:      Dict 			=     kwargs.pop("num_inference_steps" ,							A_     )
       UpperCamelCase			:      int 			=     self.dummy_sample
       UpperCamelCase			:      Dict 			=     0.1 * sample
       UpperCamelCase			:      Any 			=     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(A_     )
         UpperCamelCase			:      Dict 			=     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			:      Dict 			=     dummy_past_residuals[: new_scheduler.config.solver_order]

        UpperCamelCase			:      Tuple 			=     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  __UpperCamelCase(				self ,							A_=None ,							**A_     ):
       '''simple docstring'''




       if scheduler is None:
        UpperCamelCase			:      Any 			=     self.scheduler_classes[0]
        UpperCamelCase			:      Any 			=     self.get_scheduler_config(**A_     )
        UpperCamelCase			:      Union[str, Any] 			=     scheduler_class(**A_     )

       UpperCamelCase			:      Any 			=     self.scheduler_classes[0]
       UpperCamelCase			:      Dict 			=     self.get_scheduler_config(**A_     )
       UpperCamelCase			:      Union[str, Any] 			=     scheduler_class(**A_     )

       UpperCamelCase			:      Optional[Any] 			=     10
       UpperCamelCase			:      Union[str, Any] 			=     self.dummy_model()
       UpperCamelCase			:      int 			=     self.dummy_sample_deter
       scheduler.set_timesteps(A_     )

       for i, t in enumerate(scheduler.timesteps     ):
        UpperCamelCase			:      Optional[int] 			=     model(A_ ,							A_     )
        UpperCamelCase			:      Any 			=     scheduler.step(A_ ,							A_ ,							A_     ).prev_sample

       return sample





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
       UpperCamelCase			:      List[str] 			=     50
       UpperCamelCase			:      Tuple 			=     self.dummy_model()
       UpperCamelCase			:      Dict 			=     self.dummy_sample_deter
       scheduler.set_timesteps(A_     )

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

       UpperCamelCase			:      List[str] 			=     torch.mean(torch.abs(A_     )     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
       UpperCamelCase			:      Optional[int] 			=     self.full_loop(scheduler=A_     )
       UpperCamelCase			:      Tuple 			=     torch.mean(torch.abs(A_     )     )

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

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

       UpperCamelCase			:      Optional[Any] 			=     self.full_loop(scheduler=A_     )
       UpperCamelCase			:      Optional[Any] 			=     torch.mean(torch.abs(A_     )     )

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





      def  __UpperCamelCase(				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  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				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_ ,							)
           UpperCamelCase			:      List[Any] 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.check_over_configs(lower_order_final=A_     )
       self.check_over_configs(lower_order_final=A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.check_over_configs(variance_type=A_     )
       self.check_over_configs(variance_type="learned_range"     )





      def  __UpperCamelCase(				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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.full_loop()
       UpperCamelCase			:      Any 			=     torch.mean(torch.abs(A_     )     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.full_loop(use_karras_sigmas=A_     )
       UpperCamelCase			:      List[Any] 			=     torch.mean(torch.abs(A_     )     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.full_loop(prediction_type="v_prediction"     )
       UpperCamelCase			:      Dict 			=     torch.mean(torch.abs(A_     )     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.full_loop(prediction_type="v_prediction" ,							use_karras_sigmas=A_     )
       UpperCamelCase			:      Any 			=     torch.mean(torch.abs(A_     )     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.scheduler_classes[0]
       UpperCamelCase			:      Optional[Any] 			=     self.get_scheduler_config(thresholding=A_ ,							dynamic_thresholding_ratio=0     )
       UpperCamelCase			:      Any 			=     scheduler_class(**A_     )

       UpperCamelCase			:      Optional[Any] 			=     10
       UpperCamelCase			:      int 			=     self.dummy_model()
       UpperCamelCase			:      List[Any] 			=     self.dummy_sample_deter.half()
       scheduler.set_timesteps(A_     )

       for i, t in enumerate(scheduler.timesteps     ):
        UpperCamelCase			:      Optional[int] 			=     model(A_ ,							A_     )
        UpperCamelCase			:      List[str] 			=     scheduler.step(A_ ,							A_ ,							A_     ).prev_sample

       assert sample.dtype == torch.floataa







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase :  int										=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""ConvBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  [
        """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvBertForMaskedLM""",
        """ConvBertForMultipleChoice""",
        """ConvBertForQuestionAnswering""",
        """ConvBertForSequenceClassification""",
        """ConvBertForTokenClassification""",
        """ConvBertLayer""",
        """ConvBertModel""",
        """ConvBertPreTrainedModel""",
        """load_tf_weights_in_convbert""",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  str										=  [
        """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFConvBertForMaskedLM""",
        """TFConvBertForMultipleChoice""",
        """TFConvBertForQuestionAnswering""",
        """TFConvBertForSequenceClassification""",
        """TFConvBertForTokenClassification""",
        """TFConvBertLayer""",
        """TFConvBertModel""",
        """TFConvBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase :  List[str]										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
