
"""simple docstring"""




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (DDPMParallelScheduler,)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {
           "num_train_timesteps": 10_00,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "variance_type": "fixed_small",
           "clip_sample": True,
       }

       config.update(**_a						)
       return config







  def 	UpperCAmelCase_ (self						):
       for timesteps in [1, 5, 1_00, 10_00]:
            self.check_over_configs(num_train_timesteps=_a						)







  def 	UpperCAmelCase_ (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=_a	,      beta_end=_a						)







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







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







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







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







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







  def 	UpperCAmelCase_ (self						):
       for t in [0, 5_00, 9_99]:
            self.check_over_forward(time_step=_a						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config()
       UpperCamelCase__				=    scheduler_class(**_a						)

       assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_0979						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.02						)						) < 1E-5







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config()
       UpperCamelCase__				=    scheduler_class(**_a						)

       UpperCamelCase__				=    len(_a						)

       UpperCamelCase__				=    self.dummy_model()
       UpperCamelCase__				=    self.dummy_sample_deter
       UpperCamelCase__				=    self.dummy_sample_deter + 0.1
       UpperCamelCase__				=    self.dummy_sample_deter - 0.1

       UpperCamelCase__				=    samplea.shape[0]
       UpperCamelCase__				=    torch.stack([samplea, samplea, samplea]	,      dim=0						)
       UpperCamelCase__				=    torch.arange(_a						)[0:3, None].repeat(1	,      _a						)

       UpperCamelCase__				=    model(samples.flatten(0	,      1						)	,      timesteps.flatten(0	,      1						)						)
       UpperCamelCase__				=    scheduler.batch_step_no_noise(_a	,      timesteps.flatten(0	,      1						)	,      samples.flatten(0	,      1						)						)

       UpperCamelCase__				=    torch.sum(torch.abs(_a						)						)
       UpperCamelCase__				=    torch.mean(torch.abs(_a						)						)

       assert abs(result_sum.item() - 11_53.18_33						) < 1E-2
       assert abs(result_mean.item() - 0.5005						) < 1E-3







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config()
       UpperCamelCase__				=    scheduler_class(**_a						)

       UpperCamelCase__				=    len(_a						)

       UpperCamelCase__				=    self.dummy_model()
       UpperCamelCase__				=    self.dummy_sample_deter
       UpperCamelCase__				=    torch.manual_seed(0						)

       for t in reversed(range(_a						)						):
            # 1. predict noise residual
            UpperCamelCase__				=    model(_a	,      _a						)

            # 2. predict previous mean of sample x_t-1
            UpperCamelCase__				=    scheduler.step(_a	,      _a	,      _a	,      generator=_a						).prev_sample

            UpperCamelCase__				=    pred_prev_sample

       UpperCamelCase__				=    torch.sum(torch.abs(_a						)						)
       UpperCamelCase__				=    torch.mean(torch.abs(_a						)						)

       assert abs(result_sum.item() - 2_58.96_06						) < 1E-2
       assert abs(result_mean.item() - 0.3372						) < 1E-3







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config(prediction_type="""v_prediction"""						)
       UpperCamelCase__				=    scheduler_class(**_a						)

       UpperCamelCase__				=    len(_a						)

       UpperCamelCase__				=    self.dummy_model()
       UpperCamelCase__				=    self.dummy_sample_deter
       UpperCamelCase__				=    torch.manual_seed(0						)

       for t in reversed(range(_a						)						):
            # 1. predict noise residual
            UpperCamelCase__				=    model(_a	,      _a						)

            # 2. predict previous mean of sample x_t-1
            UpperCamelCase__				=    scheduler.step(_a	,      _a	,      _a	,      generator=_a						).prev_sample

            UpperCamelCase__				=    pred_prev_sample

       UpperCamelCase__				=    torch.sum(torch.abs(_a						)						)
       UpperCamelCase__				=    torch.mean(torch.abs(_a						)						)

       assert abs(result_sum.item() - 2_02.02_96						) < 1E-2
       assert abs(result_mean.item() - 0.2631						) < 1E-3







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config()
       UpperCamelCase__				=    scheduler_class(**_a						)

       UpperCamelCase__				=    [1_00, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=_a						)

       UpperCamelCase__				=    scheduler.timesteps

       for i, timestep in enumerate(_a						):
            if i == len(_a						) - 1:
                 UpperCamelCase__				=    -1
            else:
                 UpperCamelCase__				=    timesteps[i + 1]

            UpperCamelCase__				=    scheduler.previous_timestep(_a						)
            UpperCamelCase__				=    prev_t.item()

            self.assertEqual(_a	,      _a						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config()
       UpperCamelCase__				=    scheduler_class(**_a						)

       UpperCamelCase__				=    [1_00, 87, 50, 51, 0]

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config()
       UpperCamelCase__				=    scheduler_class(**_a						)

       UpperCamelCase__				=    [1_00, 87, 50, 1, 0]
       UpperCamelCase__				=    len(_a						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.scheduler_classes[0]
       UpperCamelCase__				=    self.get_scheduler_config()
       UpperCamelCase__				=    scheduler_class(**_a						)

       UpperCamelCase__				=    [scheduler.config.num_train_timesteps]

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







from typing import TYPE_CHECKING

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


lowerCamelCase_						     =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

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





