



'''simple docstring'''





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= (DDIMParallelScheduler,)
 A__				= (('''eta''', 0.0), ('''num_inference_steps''', 50))
 def A_   (   self						:							List[Any]      ,     **__a						:							Tuple  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    {
           'num_train_timesteps': 1000,
           'beta_start': 0.0_0_0_1,
           'beta_end': 0.0_2,
           'beta_schedule': 'linear',
           'clip_sample': True,
       }

       config.update(**__a  )
       return config
 def A_   (   self						:							int      ,     **__a						:							Optional[int]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.scheduler_classes[0]
       __snake_case :   Any													=    self.get_scheduler_config(**__a  )
       __snake_case :   Optional[Any]													=    scheduler_class(**__a  )

       __snake_case							,     __snake_case :   Optional[int]													=    10, 0.0

       __snake_case :   Dict													=    self.dummy_model()
       __snake_case :   List[str]													=    self.dummy_sample_deter

       scheduler.set_timesteps(__a  )

       for t in scheduler.timesteps:
             __snake_case :   Dict													=    model(__a      ,     __a  )
             __snake_case :   int													=    scheduler.step(__a      ,     __a      ,     __a      ,     __a  ).prev_sample

       return sample
 def A_   (   self						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=__a  )
 def A_   (   self						:							List[Any]  )      ->   Tuple:







       '''simple docstring'''
       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=__a  )

       __snake_case :   Dict													=    self.scheduler_classes[0]
       __snake_case :   int													=    self.get_scheduler_config(steps_offset=1  )
       __snake_case :   List[str]													=    scheduler_class(**__a  )
       scheduler.set_timesteps(5  )
       assert torch.equal(scheduler.timesteps      ,     torch.LongTensor([801, 601, 401, 201, 1]  )  )
 def A_   (   self						:							int  )      ->   int:







       '''simple docstring'''
       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,     [0.0_0_2, 0.0_2, 0.2, 2]  ):
             self.check_over_configs(beta_start=__a      ,     beta_end=__a  )
 def A_   (   self						:							Optional[int]  )      ->   Optional[int]:







       '''simple docstring'''
       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=__a  )
 def A_   (   self						:							Optional[Any]  )      ->   Any:







       '''simple docstring'''
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=__a  )
 def A_   (   self						:							Tuple  )      ->   List[str]:







       '''simple docstring'''
       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=__a  )
 def A_   (   self						:							Optional[int]  )      ->   Dict:







       '''simple docstring'''
       for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=__a  )
 def A_   (   self						:							int  )      ->   str:







       '''simple docstring'''
       for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=__a  )
 def A_   (   self						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       self.check_over_configs(thresholding=__a  )
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__a      ,     prediction_type=__a      ,     sample_max_value=__a      ,     )
 def A_   (   self						:							List[str]  )      ->   int:







       '''simple docstring'''
       for t in [1, 10, 49]:
             self.check_over_forward(time_step=__a  )
 def A_   (   self						:							List[str]  )      ->   int:







       '''simple docstring'''
       for t, num_inference_steps in zip([1, 10, 50]      ,     [10, 50, 500]  ):
             self.check_over_forward(time_step=__a      ,     num_inference_steps=__a  )
 def A_   (   self						:							Optional[Any]  )      ->   List[Any]:







       '''simple docstring'''
       for t, eta in zip([1, 10, 49]      ,     [0.0, 0.5, 1.0]  ):
             self.check_over_forward(time_step=__a      ,     eta=__a  )
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    self.scheduler_classes[0]
       __snake_case :   Union[str, Any]													=    self.get_scheduler_config()
       __snake_case :   int													=    scheduler_class(**__a  )

       assert torch.sum(torch.abs(scheduler._get_variance(0      ,     0  ) - 0.0  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420      ,     400  ) - 0.1_4_7_7_1  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980      ,     960  ) - 0.3_2_4_6_0  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0      ,     0  ) - 0.0  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487      ,     486  ) - 0.0_0_9_7_9  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999      ,     998  ) - 0.0_2  )  ) < 1e-5
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.scheduler_classes[0]
       __snake_case :   Optional[Any]													=    self.get_scheduler_config()
       __snake_case :   List[Any]													=    scheduler_class(**__a  )

       __snake_case							,     __snake_case :   List[str]													=    10, 0.0
       scheduler.set_timesteps(__a  )

       __snake_case :   Union[str, Any]													=    self.dummy_model()
       __snake_case :   List[Any]													=    self.dummy_sample_deter
       __snake_case :   List[str]													=    self.dummy_sample_deter + 0.1
       __snake_case :   Tuple													=    self.dummy_sample_deter - 0.1

       __snake_case :   List[Any]													=    samplea.shape[0]
       __snake_case :   Union[str, Any]													=    torch.stack([samplea, samplea, samplea]      ,     dim=0  )
       __snake_case :   List[str]													=    torch.arange(__a  )[0:3, None].repeat(1      ,     __a  )

       __snake_case :   Tuple													=    model(samples.flatten(0      ,     1  )      ,     timesteps.flatten(0      ,     1  )  )
       __snake_case :   str													=    scheduler.batch_step_no_noise(__a      ,     timesteps.flatten(0      ,     1  )      ,     samples.flatten(0      ,     1  )      ,     __a  )

       __snake_case :   Union[str, Any]													=    torch.sum(torch.abs(__a  )  )
       __snake_case :   Tuple													=    torch.mean(torch.abs(__a  )  )

       assert abs(result_sum.item() - 1_1_4_7.7_9_0_4  ) < 1e-2
       assert abs(result_mean.item() - 0.4_9_8_2  ) < 1e-3
 def A_   (   self						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.full_loop()

       __snake_case :   Optional[Any]													=    torch.sum(torch.abs(__a  )  )
       __snake_case :   Dict													=    torch.mean(torch.abs(__a  )  )

       assert abs(result_sum.item() - 1_7_2.0_0_6_7  ) < 1e-2
       assert abs(result_mean.item() - 0.2_2_3_9_6_7  ) < 1e-3
 def A_   (   self						:							Tuple  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    self.full_loop(prediction_type='v_prediction'  )

       __snake_case :   Dict													=    torch.sum(torch.abs(__a  )  )
       __snake_case :   Dict													=    torch.mean(torch.abs(__a  )  )

       assert abs(result_sum.item() - 5_2.5_3_0_2  ) < 1e-2
       assert abs(result_mean.item() - 0.0_6_8_4  ) < 1e-3
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       # We specify different beta, so that the first alpha is 0.99
       __snake_case :   str													=    self.full_loop(set_alpha_to_one=__a      ,     beta_start=0.0_1  )
       __snake_case :   List[Any]													=    torch.sum(torch.abs(__a  )  )
       __snake_case :   int													=    torch.mean(torch.abs(__a  )  )

       assert abs(result_sum.item() - 1_4_9.8_2_9_5  ) < 1e-2
       assert abs(result_mean.item() - 0.1_9_5_1  ) < 1e-3





 def A_   (   self						:							List[str]  )      ->   Any:







       '''simple docstring'''
       # We specify different beta, so that the first alpha is 0.99
       __snake_case :   Dict													=    self.full_loop(set_alpha_to_one=__a      ,     beta_start=0.0_1  )
       __snake_case :   Any													=    torch.sum(torch.abs(__a  )  )
       __snake_case :   List[str]													=    torch.mean(torch.abs(__a  )  )

       assert abs(result_sum.item() - 1_4_9.0_7_8_4  ) < 1e-2
       assert abs(result_mean.item() - 0.1_9_4_1  ) < 1e-3








'''simple docstring'''





from typing import TYPE_CHECKING

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


A__    :   Optional[Any]           =					{
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Tuple           =					['''SqueezeBertTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   str           =					[
        '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SqueezeBertForMaskedLM''',
        '''SqueezeBertForMultipleChoice''',
        '''SqueezeBertForQuestionAnswering''',
        '''SqueezeBertForSequenceClassification''',
        '''SqueezeBertForTokenClassification''',
        '''SqueezeBertModel''',
        '''SqueezeBertModule''',
        '''SqueezeBertPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_squeezebert import (
        SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SqueezeBertConfig,
        SqueezeBertOnnxConfig,
    )
    from .tokenization_squeezebert import SqueezeBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_squeezebert import (
            SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            SqueezeBertForMaskedLM,
            SqueezeBertForMultipleChoice,
            SqueezeBertForQuestionAnswering,
            SqueezeBertForSequenceClassification,
            SqueezeBertForTokenClassification,
            SqueezeBertModel,
            SqueezeBertModule,
            SqueezeBertPreTrainedModel,
        )

else:
    import sys

    A__    :   List[Any]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



