import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   (DDPMParallelScheduler,)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      **snake_case	:							Dict			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					{
      'num_train_timesteps': 1_0_0_0,
      'beta_start': 0.0001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
      'variance_type': 'fixed_small',
      'clip_sample': True,
  }

  config.update(**snake_case			)
  return config

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Dict:

  """simple docstring"""





  for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
   self.check_over_configs(num_train_timesteps=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  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=snake_case   ,      beta_end=snake_case			)

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

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """simple docstring"""





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

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

  """simple docstring"""





  for t in [0, 5_0_0, 9_9_9]:
   self.check_over_forward(time_step=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						str =					self.get_scheduler_config()
  UpperCamelCase_					:						Optional[Any] =					scheduler_class(**snake_case			)

  assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
  assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.00979			)			) < 1e-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5

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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Tuple =					self.get_scheduler_config()
  UpperCamelCase_					:						Optional[int] =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						str =					len(snake_case			)

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

  UpperCamelCase_					:						Dict =					samplea.shape[0]
  UpperCamelCase_					:						List[Any] =					torch.stack([samplea, samplea, samplea]   ,      dim=0			)
  UpperCamelCase_					:						Tuple =					torch.arange(snake_case			)[0:3, None].repeat(1   ,      snake_case			)

  UpperCamelCase_					:						List[str] =					model(samples.flatten(0   ,      1			)   ,      timesteps.flatten(0   ,      1			)			)
  UpperCamelCase_					:						Any =					scheduler.batch_step_no_noise(snake_case   ,      timesteps.flatten(0   ,      1			)   ,      samples.flatten(0   ,      1			)			)

  UpperCamelCase_					:						int =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						Dict =					torch.mean(torch.abs(snake_case			)			)

  assert abs(result_sum.item() - 1153.1833			) < 1e-2
  assert abs(result_mean.item() - 0.5005			) < 1e-3

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.scheduler_classes[0]
  UpperCamelCase_					:						Any =					self.get_scheduler_config()
  UpperCamelCase_					:						Tuple =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						Optional[Any] =					len(snake_case			)

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

  for t in reversed(range(snake_case			)			):
   # 1. predict noise residual
   UpperCamelCase_					:						List[str] =					model(snake_case   ,      snake_case			)

   # 2. predict previous mean of sample x_t-1
   UpperCamelCase_					:						List[Any] =					scheduler.step(snake_case   ,      snake_case   ,      snake_case   ,      generator=snake_case			).prev_sample

   UpperCamelCase_					:						Any =					pred_prev_sample

  UpperCamelCase_					:						Any =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						Any =					torch.mean(torch.abs(snake_case			)			)

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

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

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Optional[Any] =					self.get_scheduler_config(prediction_type='v_prediction'			)
  UpperCamelCase_					:						Optional[int] =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					len(snake_case			)

  UpperCamelCase_					:						int =					self.dummy_model()
  UpperCamelCase_					:						List[Any] =					self.dummy_sample_deter
  UpperCamelCase_					:						Union[str, Any] =					torch.manual_seed(0			)

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

   # 2. predict previous mean of sample x_t-1
   UpperCamelCase_					:						Optional[Any] =					scheduler.step(snake_case   ,      snake_case   ,      snake_case   ,      generator=snake_case			).prev_sample

   UpperCamelCase_					:						Any =					pred_prev_sample

  UpperCamelCase_					:						Any =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						int =					torch.mean(torch.abs(snake_case			)			)

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Any =					self.get_scheduler_config()
  UpperCamelCase_					:						Any =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						str =					[1_0_0, 8_7, 5_0, 1, 0]

  scheduler.set_timesteps(timesteps=snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					scheduler.timesteps

  for i, timestep in enumerate(snake_case			):
   if i == len(snake_case			) - 1:
    UpperCamelCase_					:						Dict =					-1
   else:
    UpperCamelCase_					:						int =					timesteps[i + 1]

   UpperCamelCase_					:						Optional[int] =					scheduler.previous_timestep(snake_case			)
   UpperCamelCase_					:						Optional[Any] =					prev_t.item()

   self.assertEqual(snake_case   ,      snake_case			)

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.scheduler_classes[0]
  UpperCamelCase_					:						List[str] =					self.get_scheduler_config()
  UpperCamelCase_					:						Optional[Any] =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						List[Any] =					[1_0_0, 8_7, 5_0, 5_1, 0]

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.scheduler_classes[0]
  UpperCamelCase_					:						int =					self.get_scheduler_config()
  UpperCamelCase_					:						Optional[Any] =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						Optional[Any] =					[1_0_0, 8_7, 5_0, 1, 0]
  UpperCamelCase_					:						str =					len(snake_case			)

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


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

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						str =					self.get_scheduler_config()
  UpperCamelCase_					:						Optional[int] =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						Any =					[scheduler.config.num_train_timesteps]

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




import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						Union[str, Any] =					test_file.split(os.path.sep	)
 if components[0:2] != ["tests", "models"]:
  raise ValueError(
      '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
      F"{test_file} instead."	)
 UpperCamelCase_					:						str =					components[-1]
 if not test_fn.endswith('py'	):
  raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."	)
 if not test_fn.startswith('test_modeling_'	):
  raise ValueError(
      F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."	)

 UpperCamelCase_					:						Union[str, Any] =					components[:-1] + [test_fn.replace('.py'  ,  ''	)]
 UpperCamelCase_					:						List[Any] =					'.'.join(lowerCamelCase	)

 return test_module_path





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					get_module_path(lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					importlib.import_module(lowerCamelCase	)

 return test_module





def    __lowercase     (						lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						int =					[]
 UpperCamelCase_					:						Tuple =					get_test_module(lowerCamelCase	)
 for attr in dir(lowerCamelCase	):
  if attr.endswith('ModelTester'	):
   tester_classes.append(getattr(lowerCamelCase  ,  lowerCamelCase	)	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						List[str] =					[]
 UpperCamelCase_					:						Union[str, Any] =					get_test_module(lowerCamelCase	)
 for attr in dir(lowerCamelCase	):
  UpperCamelCase_					:						Dict =					getattr(lowerCamelCase  ,  lowerCamelCase	)
  # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
  # `all_model_classes` is not empty (which also excludes other special classes).
  UpperCamelCase_					:						Optional[int] =					getattr(lowerCamelCase  ,  'all_model_classes'  ,  []	)
  if len(lowerCamelCase	) > 0:
   test_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						int =					get_test_classes(lowerCamelCase	)
 UpperCamelCase_					:						List[Any] =					set()
 for test_class in test_classes:
  model_classes.update(test_class.all_model_classes	)

 # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						int =					test_class()
 if hasattr(lowerCamelCase  ,  'setUp'	):
  test.setUp()

 UpperCamelCase_					:						List[Any] =					None
 if hasattr(lowerCamelCase  ,  'model_tester'	):
  # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
  if test.model_tester is not None:
   UpperCamelCase_					:						Optional[Any] =					test.model_tester.__class__

 return model_tester





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						Optional[Any] =					get_test_classes(lowerCamelCase	)

 UpperCamelCase_					:						Tuple =					[]
 for test_class in test_classes:
  if model_class in test_class.all_model_classes:
   target_test_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						List[Any] =					get_test_classes_for_model(lowerCamelCase  ,  lowerCamelCase	)

 UpperCamelCase_					:						int =					[]
 for test_class in test_classes:
  UpperCamelCase_					:						Tuple =					get_model_tester_from_test_class(lowerCamelCase	)
  if tester_class is not None:
   tester_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Tuple =					get_test_classes(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					{test_class: get_model_tester_from_test_class(lowerCamelCase	) for test_class in test_classes}
 return test_tester_mapping





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[str] =					get_model_classes(lowerCamelCase	)
 UpperCamelCase_					:						int =					{
     model_class: get_test_classes_for_model(lowerCamelCase  ,  lowerCamelCase	) for model_class in model_classes
 }
 return model_test_mapping





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						Tuple =					get_model_classes(lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					{
     model_class: get_tester_classes_for_model(lowerCamelCase  ,  lowerCamelCase	) for model_class in model_classes
 }
 return model_to_tester_mapping






def    __lowercase     (						lowerCamelCase					:     Any	):
 if isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return o
 elif isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return o.__name__
 elif isinstance(lowerCamelCase  ,  (list, tuple)	):
  return [to_json(lowerCamelCase	) for x in o]
 elif isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return {to_json(lowerCamelCase	): to_json(lowerCamelCase	) for k, v in o.items()}
 else:
  return o



