



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__					(		snake_case__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				(DDPMScheduler,)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        A			:	Optional[int]										=       {
            "num_train_timesteps": 1000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "variance_type": "fixed_small",
            "clip_sample": True,
        }

        config.update(**UpperCAmelCase_		)
        return config






    def       _lowerCAmelCase (	self		):
        for timesteps in [1, 5, 100, 1000]:
            self.check_over_configs(num_train_timesteps=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],		[0.002, 0.02, 0.2, 2]		):
            self.check_over_configs(beta_start=UpperCAmelCase_,		beta_end=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        self.check_over_configs(thresholding=UpperCAmelCase_		)
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=UpperCAmelCase_,		prediction_type=UpperCAmelCase_,		sample_max_value=UpperCAmelCase_,		)






    def       _lowerCAmelCase (	self		):
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
            self.check_over_configs(prediction_type=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        for t in [0, 500, 999]:
            self.check_over_forward(time_step=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        A			:	Optional[int]										=       self.scheduler_classes[0]
        A			:	List[str]										=       self.get_scheduler_config()
        A			:	Union[str, Any]										=       scheduler_class(**UpperCAmelCase_		)

        assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1e-5






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       self.scheduler_classes[0]
        A			:	Dict										=       self.get_scheduler_config()
        A			:	Optional[int]										=       scheduler_class(**UpperCAmelCase_		)

        A			:	List[Any]										=       len(UpperCAmelCase_		)

        A			:	str										=       self.dummy_model()
        A			:	str										=       self.dummy_sample_deter
        A			:	List[Any]										=       torch.manual_seed(0		)

        for t in reversed(range(UpperCAmelCase_		)		):
            # 1. predict noise residual
            A			:	Any										=       model(UpperCAmelCase_,		UpperCAmelCase_		)

            # 2. predict previous mean of sample x_t-1
            A			:	Union[str, Any]										=       scheduler.step(UpperCAmelCase_,		UpperCAmelCase_,		UpperCAmelCase_,		generator=UpperCAmelCase_		).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            A			:	List[Any]										=       pred_prev_sample

        A			:	Dict										=       torch.sum(torch.abs(UpperCAmelCase_		)		)
        A			:	List[Any]										=       torch.mean(torch.abs(UpperCAmelCase_		)		)

        assert abs(result_sum.item() - 258.9606		) < 1e-2
        assert abs(result_mean.item() - 0.3372		) < 1e-3






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.scheduler_classes[0]
        A			:	Optional[int]										=       self.get_scheduler_config(prediction_type="""v_prediction"""		)
        A			:	Optional[int]										=       scheduler_class(**UpperCAmelCase_		)

        A			:	List[Any]										=       len(UpperCAmelCase_		)

        A			:	Dict										=       self.dummy_model()
        A			:	Any										=       self.dummy_sample_deter
        A			:	str										=       torch.manual_seed(0		)

        for t in reversed(range(UpperCAmelCase_		)		):
            # 1. predict noise residual
            A			:	Tuple										=       model(UpperCAmelCase_,		UpperCAmelCase_		)

            # 2. predict previous mean of sample x_t-1
            A			:	Optional[int]										=       scheduler.step(UpperCAmelCase_,		UpperCAmelCase_,		UpperCAmelCase_,		generator=UpperCAmelCase_		).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            A			:	Optional[int]										=       pred_prev_sample

        A			:	str										=       torch.sum(torch.abs(UpperCAmelCase_		)		)
        A			:	Optional[int]										=       torch.mean(torch.abs(UpperCAmelCase_		)		)

        assert abs(result_sum.item() - 202.0296		) < 1e-2
        assert abs(result_mean.item() - 0.2631		) < 1e-3






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       self.scheduler_classes[0]
        A			:	Any										=       self.get_scheduler_config()
        A			:	Optional[int]										=       scheduler_class(**UpperCAmelCase_		)

        A			:	Union[str, Any]										=       [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=UpperCAmelCase_		)

        A			:	List[Any]										=       scheduler.timesteps

        for i, timestep in enumerate(UpperCAmelCase_		):
            if i == len(UpperCAmelCase_		) - 1:
                A			:	str										=       -1
            else:
                A			:	List[Any]										=       timesteps[i + 1]

            A			:	int										=       scheduler.previous_timestep(UpperCAmelCase_		)
            A			:	Dict										=       prev_t.item()

            self.assertEqual(UpperCAmelCase_,		UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        A			:	str										=       self.scheduler_classes[0]
        A			:	Optional[Any]										=       self.get_scheduler_config()
        A			:	Tuple										=       scheduler_class(**UpperCAmelCase_		)

        A			:	Dict										=       [100, 87, 50, 51, 0]

        with self.assertRaises(UpperCAmelCase_,		msg="""`custom_timesteps` must be in descending order."""		):
            scheduler.set_timesteps(timesteps=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       self.scheduler_classes[0]
        A			:	int										=       self.get_scheduler_config()
        A			:	Optional[Any]										=       scheduler_class(**UpperCAmelCase_		)

        A			:	Optional[int]										=       [100, 87, 50, 1, 0]
        A			:	Optional[Any]										=       len(UpperCAmelCase_		)

        with self.assertRaises(UpperCAmelCase_,		msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""		):
            scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_,		timesteps=UpperCAmelCase_		)






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       self.scheduler_classes[0]
        A			:	Union[str, Any]										=       self.get_scheduler_config()
        A			:	Dict										=       scheduler_class(**UpperCAmelCase_		)

        A			:	Any										=       [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            UpperCAmelCase_,		msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""",		):
            scheduler.set_timesteps(timesteps=UpperCAmelCase_		)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

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


