





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 							_lowerCAmelCase     (      UpperCAmelCase_				):


  '''simple docstring'''
  a_				:							List[str]          =(DDPMScheduler,)







  def UpperCamelCase_			(   self   :       Dict   ,					**UpperCamelCase   :       List[Any]				):


    '''simple docstring'''





    _snake_case :					Union[str, Any]								=						{
        'num_train_timesteps': 10_00,
        'beta_start': 0.00_01,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
        'variance_type': 'fixed_small',
        'clip_sample': True,
    }

    config.update(**UpperCamelCase				)
    return config







  def UpperCamelCase_			(   self   :       Dict				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       Optional[Any]				):


    '''simple docstring'''





    for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,					[0.0_02, 0.02, 0.2, 2]				):
      self.check_over_configs(beta_start=UpperCamelCase   ,					beta_end=UpperCamelCase				)







  def UpperCamelCase_			(   self   :       Any				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       Dict				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       List[Any]				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       List[str]				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       str				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       Optional[int]				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       str				):


    '''simple docstring'''





    _snake_case :					Dict								=						self.scheduler_classes[0]
    _snake_case :					Union[str, Any]								=						self.get_scheduler_config()
    _snake_case :					Any								=						scheduler_class(**UpperCamelCase				)

    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_09_79				)				) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(9_99				) - 0.02				)				) < 1e-5







  def UpperCamelCase_			(   self   :       Optional[Any]				):


    '''simple docstring'''





    _snake_case :					Any								=						self.scheduler_classes[0]
    _snake_case :					Any								=						self.get_scheduler_config()
    _snake_case :					str								=						scheduler_class(**UpperCamelCase				)

    _snake_case :					int								=						len(UpperCamelCase				)

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

    for t in reversed(range(UpperCamelCase				)				):
      # 1. predict noise residual
      _snake_case :					Dict								=						model(UpperCamelCase   ,					UpperCamelCase				)

      # 2. predict previous mean of sample x_t-1
      _snake_case :					List[str]								=						scheduler.step(UpperCamelCase   ,					UpperCamelCase   ,					UpperCamelCase   ,					generator=UpperCamelCase				).prev_sample

      # if t > 0:
      #     noise = self.dummy_sample_deter
      #     variance = scheduler.get_variance(t) ** (0.5) * noise
      #
      # sample = pred_prev_sample + variance
      _snake_case :					List[Any]								=						pred_prev_sample

    _snake_case :					Union[str, Any]								=						torch.sum(torch.abs(UpperCamelCase				)				)
    _snake_case :					Optional[Any]								=						torch.mean(torch.abs(UpperCamelCase				)				)

    assert abs(result_sum.item() - 2_58.96_06				) < 1e-2
    assert abs(result_mean.item() - 0.33_72				) < 1e-3







  def UpperCamelCase_			(   self   :       List[str]				):


    '''simple docstring'''





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

    _snake_case :					Union[str, Any]								=						len(UpperCamelCase				)

    _snake_case :					Union[str, Any]								=						self.dummy_model()
    _snake_case :					Dict								=						self.dummy_sample_deter
    _snake_case :					Any								=						torch.manual_seed(0				)

    for t in reversed(range(UpperCamelCase				)				):
      # 1. predict noise residual
      _snake_case :					Dict								=						model(UpperCamelCase   ,					UpperCamelCase				)

      # 2. predict previous mean of sample x_t-1
      _snake_case :					int								=						scheduler.step(UpperCamelCase   ,					UpperCamelCase   ,					UpperCamelCase   ,					generator=UpperCamelCase				).prev_sample

      # if t > 0:
      #     noise = self.dummy_sample_deter
      #     variance = scheduler.get_variance(t) ** (0.5) * noise
      #
      # sample = pred_prev_sample + variance
      _snake_case :					Optional[Any]								=						pred_prev_sample

    _snake_case :					Any								=						torch.sum(torch.abs(UpperCamelCase				)				)
    _snake_case :					Dict								=						torch.mean(torch.abs(UpperCamelCase				)				)

    assert abs(result_sum.item() - 2_02.02_96				) < 1e-2
    assert abs(result_mean.item() - 0.26_31				) < 1e-3







  def UpperCamelCase_			(   self   :       int				):


    '''simple docstring'''





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

    _snake_case :					Optional[Any]								=						[1_00, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=UpperCamelCase				)

    _snake_case :					List[str]								=						scheduler.timesteps

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

      _snake_case :					List[Any]								=						scheduler.previous_timestep(UpperCamelCase				)
      _snake_case :					List[Any]								=						prev_t.item()

      self.assertEqual(UpperCamelCase   ,					UpperCamelCase				)







  def UpperCamelCase_			(   self   :       Dict				):


    '''simple docstring'''





    _snake_case :					Any								=						self.scheduler_classes[0]
    _snake_case :					Tuple								=						self.get_scheduler_config()
    _snake_case :					List[Any]								=						scheduler_class(**UpperCamelCase				)

    _snake_case :					str								=						[1_00, 87, 50, 51, 0]

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







  def UpperCamelCase_			(   self   :       int				):


    '''simple docstring'''





    _snake_case :					Any								=						self.scheduler_classes[0]
    _snake_case :					Optional[int]								=						self.get_scheduler_config()
    _snake_case :					List[Any]								=						scheduler_class(**UpperCamelCase				)

    _snake_case :					Optional[int]								=						[1_00, 87, 50, 1, 0]
    _snake_case :					str								=						len(UpperCamelCase				)

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







  def UpperCamelCase_			(   self   :       List[str]				):


    '''simple docstring'''





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

    _snake_case :					Union[str, Any]								=						[scheduler.config.num_train_timesteps]

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









from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching

def 	_lowerCAmelCase (    ):
      import os as original_os
      from os import path as original_path
      from os import rename as original_rename
      from os.path import dirname as original_dirname
      from os.path import join as original_join

      assert _test_patching.os is original_os
      assert _test_patching.path is original_path
      assert _test_patching.join is original_join

      assert _test_patching.renamed_os is original_os
      assert _test_patching.renamed_path is original_path
      assert _test_patching.renamed_join is original_join

      lowercase__  =	'__test_patch_submodule_mock__'
      with patch_submodule(_test_patching    ,	'os.path.join'    ,	A__						):
            # Every way to access os.path.join must be patched, and the rest must stay untouched

            # check os.path.join
            assert isinstance(_test_patching.os    ,	_PatchedModuleObj						)
            assert isinstance(_test_patching.os.path    ,	_PatchedModuleObj						)
            assert _test_patching.os.path.join is mock

            # check path.join
            assert isinstance(_test_patching.path    ,	_PatchedModuleObj						)
            assert _test_patching.path.join is mock

            # check join
            assert _test_patching.join is mock

            # check that the other attributes are untouched
            assert _test_patching.os.rename is original_rename
            assert _test_patching.path.dirname is original_dirname
            assert _test_patching.os.path.dirname is original_dirname

            # Even renamed modules or objects must be patched

            # check renamed_os.path.join
            assert isinstance(_test_patching.renamed_os    ,	_PatchedModuleObj						)
            assert isinstance(_test_patching.renamed_os.path    ,	_PatchedModuleObj						)
            assert _test_patching.renamed_os.path.join is mock

            # check renamed_path.join
            assert isinstance(_test_patching.renamed_path    ,	_PatchedModuleObj						)
            assert _test_patching.renamed_path.join is mock

            # check renamed_join
            assert _test_patching.renamed_join is mock

            # check that the other attributes are untouched
            assert _test_patching.renamed_os.rename is original_rename
            assert _test_patching.renamed_path.dirname is original_dirname
            assert _test_patching.renamed_os.path.dirname is original_dirname

      # check that everthing is back to normal when the patch is over

      assert _test_patching.os is original_os
      assert _test_patching.path is original_path
      assert _test_patching.join is original_join

      assert _test_patching.renamed_os is original_os
      assert _test_patching.renamed_path is original_path
      assert _test_patching.renamed_join is original_join

def 	_lowerCAmelCase (    ):
      assert _test_patching.open is open

      lowercase__  =	'__test_patch_submodule_builtin_mock__'
      # _test_patching has "open" in its globals
      assert _test_patching.open is open
      with patch_submodule(_test_patching    ,	'open'    ,	A__						):
            assert _test_patching.open is mock

      # check that everthing is back to normal when the patch is over

      assert _test_patching.open is open

def 	_lowerCAmelCase (    ):
      # pandas.read_csv is not present in _test_patching
      lowercase__  =	'__test_patch_submodule_missing_mock__'
      with patch_submodule(_test_patching    ,	'pandas.read_csv'    ,	A__						):
            pass

def 	_lowerCAmelCase (    ):
      # builtin should always be mocked even if they're not in the globals
      # in case they're loaded at one point
      lowercase__  =	'__test_patch_submodule_missing_builtin_mock__'
      # _test_patching doesn't have "len" in its globals
      assert getattr(_test_patching    ,	'len'    ,	A__						) is None
      with patch_submodule(_test_patching    ,	'len'    ,	A__						):
            assert _test_patching.len is mock
      assert _test_patching.len is len

def 	_lowerCAmelCase (    ):
      lowercase__  =	'__test_patch_submodule_start_and_stop_mock__'
      lowercase__  =	patch_submodule(_test_patching    ,	'open'    ,	A__						)
      assert _test_patching.open is open
      patch.start()
      assert _test_patching.open is mock
      patch.stop()
      assert _test_patching.open is open

def 	_lowerCAmelCase (    ):
      from os import rename as original_rename
      from os.path import dirname as original_dirname
      from os.path import join as original_join

      lowercase__  =	'__test_patch_submodule_successive_join__'
      lowercase__  =	'__test_patch_submodule_successive_dirname__'
      lowercase__  =	'__test_patch_submodule_successive_rename__'
      assert _test_patching.os.path.join is original_join
      assert _test_patching.os.path.dirname is original_dirname
      assert _test_patching.os.rename is original_rename

      with patch_submodule(_test_patching    ,	'os.path.join'    ,	A__						):
            with patch_submodule(_test_patching    ,	'os.rename'    ,	A__						):
                  with patch_submodule(_test_patching    ,	'os.path.dirname'    ,	A__						):
                        assert _test_patching.os.path.join is mock_join
                        assert _test_patching.os.path.dirname is mock_dirname
                        assert _test_patching.os.rename is mock_rename

    # try another order
      with patch_submodule(_test_patching    ,	'os.rename'    ,	A__						):
            with patch_submodule(_test_patching    ,	'os.path.join'    ,	A__						):
                  with patch_submodule(_test_patching    ,	'os.path.dirname'    ,	A__						):
                        assert _test_patching.os.path.join is mock_join
                        assert _test_patching.os.path.dirname is mock_dirname
                        assert _test_patching.os.rename is mock_rename

      assert _test_patching.os.path.join is original_join
      assert _test_patching.os.path.dirname is original_dirname
      assert _test_patching.os.rename is original_rename

def 	_lowerCAmelCase (    ):
      lowercase__  =	'__test_patch_submodule_doesnt_exist_mock__'
      with patch_submodule(_test_patching    ,	'__module_that_doesn_exist__.__attribute_that_doesn_exist__'    ,	A__						):
            pass
      with patch_submodule(_test_patching    ,	'os.__attribute_that_doesn_exist__'    ,	A__						):
            pass
