




'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class       A   (		_a   ):
							lowercase_          =    (DDPMParallelScheduler,)







							def        __lowerCAmelCase      (  self	:  int							,      **lowerCAmelCase_	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    {
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_ )
														return config







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


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Union[str, Any]:


														"""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=lowerCAmelCase_							,      beta_end=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Optional[int]:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  int )      ->							Optional[int]:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str )      ->							str:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Union[str, Any]:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Any:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  int )      ->							Any:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config()
														_a     					=    scheduler_class(**lowerCAmelCase_ )

														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_0_9_7_9 ) ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.0_2 ) ) < 1e-5







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Dict:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config()
														_a     					=    scheduler_class(**lowerCAmelCase_ )

														_a     					=    len(lowerCAmelCase_ )

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

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

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

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

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							str:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config()
														_a     					=    scheduler_class(**lowerCAmelCase_ )

														_a     					=    len(lowerCAmelCase_ )

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

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

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

																					_a     					=    pred_prev_sample

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

														assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3







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


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config(prediction_type='''v_prediction''' )
														_a     					=    scheduler_class(**lowerCAmelCase_ )

														_a     					=    len(lowerCAmelCase_ )

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

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

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

																					_a     					=    pred_prev_sample

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

														assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config()
														_a     					=    scheduler_class(**lowerCAmelCase_ )

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

														scheduler.set_timesteps(timesteps=lowerCAmelCase_ )

														_a     					=    scheduler.timesteps

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

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

																					self.assertEqual(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Dict )      ->							int:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config()
														_a     					=    scheduler_class(**lowerCAmelCase_ )

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

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







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config()
														_a     					=    scheduler_class(**lowerCAmelCase_ )

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

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







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config()
														_a     					=    scheduler_class(**lowerCAmelCase_ )

														_a     					=    [scheduler.config.num_train_timesteps]

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







'''simple docstring'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

