


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase_		(							__snake_case):
       '''simple docstring'''
       __UpperCamelCase					:   Any							=    (LMSDiscreteScheduler,)
       __UpperCamelCase					:   List[str]							=    10






       def        _lowercase      (   self       ,    **__SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[Any]      		=						{
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
              }

              config.update(**__SCREAMING_SNAKE_CASE  )
              return config






       def        _lowercase      (   self  ):




              """simple docstring"""




              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,    [0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE       ,    beta_end=__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




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






       def        _lowercase      (   self  ):




              """simple docstring"""




              for t in [0, 500, 800]:
                     self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Dict      		=						self.scheduler_classes[0]
              UpperCamelCase			:		Tuple      		=						self.get_scheduler_config()
              UpperCamelCase			:		Optional[Any]      		=						scheduler_class(**__SCREAMING_SNAKE_CASE  )

              scheduler.set_timesteps(self.num_inference_steps  )

              UpperCamelCase			:		Dict      		=						self.dummy_model()
              UpperCamelCase			:		Optional[Any]      		=						self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     UpperCamelCase			:		Tuple      		=						scheduler.scale_model_input(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		int      		=						model(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		Optional[Any]      		=						scheduler.step(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )
                     UpperCamelCase			:		int      		=						output.prev_sample

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

              assert abs(result_sum.item() - 1_006.388  ) < 1e-2
              assert abs(result_mean.item() - 1.31  ) < 1e-3






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		int      		=						self.scheduler_classes[0]
              UpperCamelCase			:		Optional[Any]      		=						self.get_scheduler_config(prediction_type='''v_prediction'''  )
              UpperCamelCase			:		int      		=						scheduler_class(**__SCREAMING_SNAKE_CASE  )

              scheduler.set_timesteps(self.num_inference_steps  )

              UpperCamelCase			:		str      		=						self.dummy_model()
              UpperCamelCase			:		Any      		=						self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     UpperCamelCase			:		Tuple      		=						scheduler.scale_model_input(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		List[Any]      		=						model(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		List[Any]      		=						scheduler.step(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )
                     UpperCamelCase			:		Any      		=						output.prev_sample

              UpperCamelCase			:		str      		=						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
              UpperCamelCase			:		str      		=						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1e-2
              assert abs(result_mean.item() - 2.2676e-06  ) < 1e-3






       def        _lowercase      (   self  ):




              """simple docstring"""




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

              scheduler.set_timesteps(self.num_inference_steps       ,    device=__SCREAMING_SNAKE_CASE  )

              UpperCamelCase			:		Optional[int]      		=						self.dummy_model()
              UpperCamelCase			:		Tuple      		=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase			:		Any      		=						sample.to(__SCREAMING_SNAKE_CASE  )

              for i, t in enumerate(scheduler.timesteps  ):
                     UpperCamelCase			:		str      		=						scheduler.scale_model_input(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		Optional[Any]      		=						model(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		Optional[int]      		=						scheduler.step(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )
                     UpperCamelCase			:		Dict      		=						output.prev_sample

              UpperCamelCase			:		Optional[Any]      		=						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
              UpperCamelCase			:		Tuple      		=						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1e-2
              assert abs(result_mean.item() - 1.31  ) < 1e-3






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Dict      		=						self.scheduler_classes[0]
              UpperCamelCase			:		int      		=						self.get_scheduler_config()
              UpperCamelCase			:		Tuple      		=						scheduler_class(**__SCREAMING_SNAKE_CASE       ,    use_karras_sigmas=__SCREAMING_SNAKE_CASE  )

              scheduler.set_timesteps(self.num_inference_steps       ,    device=__SCREAMING_SNAKE_CASE  )

              UpperCamelCase			:		Optional[Any]      		=						self.dummy_model()
              UpperCamelCase			:		Union[str, Any]      		=						self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE  ) * scheduler.init_noise_sigma
              UpperCamelCase			:		Any      		=						sample.to(__SCREAMING_SNAKE_CASE  )

              for t in scheduler.timesteps:
                     UpperCamelCase			:		List[Any]      		=						scheduler.scale_model_input(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		Optional[Any]      		=						model(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )

                     UpperCamelCase			:		int      		=						scheduler.step(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  )
                     UpperCamelCase			:		List[str]      		=						output.prev_sample

              UpperCamelCase			:		Tuple      		=						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
              UpperCamelCase			:		List[Any]      		=						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2e-2
              assert abs(result_mean.item() - 4.9_648  ) < 1e-3




from typing import TYPE_CHECKING

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


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)