






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				lowercase__    							=	(DDIMScheduler,)
				lowercase__    							=	(("""eta""", 0.0), ("""num_inference_steps""", 50))




				def     lowercase__       (					self						:   Union[str, Any],  **lowerCamelCase						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**lowerCamelCase  )
								return config




				def     lowercase__       (					self						:   Any,  **lowerCamelCase						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.scheduler_classes[0]
								lowercase__       			=      self.get_scheduler_config(**lowerCamelCase  )
								lowercase__       			=      scheduler_class(**lowerCamelCase  )

								lowercase__			,						lowercase__       			=      10, 0.0

								lowercase__       			=      self.dummy_model()
								lowercase__       			=      self.dummy_sample_deter

								scheduler.set_timesteps(lowerCamelCase  )

								for t in scheduler.timesteps:
												lowercase__       			=      model(lowerCamelCase,  lowerCamelCase  )
												lowercase__       			=      scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  ).prev_sample

								return sample




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								for timesteps in [100, 500, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[Any]  ):

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

								lowercase__       			=      self.scheduler_classes[0]
								lowercase__       			=      self.get_scheduler_config(steps_offset=1  )
								lowercase__       			=      scheduler_class(**lowerCamelCase  )
								scheduler.set_timesteps(5  )
								assert torch.equal(scheduler.timesteps,  torch.LongTensor([801, 601, 401, 201, 1]  )  )




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''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=lowerCamelCase,  beta_end=lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=lowerCamelCase  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=lowerCamelCase  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								self.check_over_configs(thresholding=lowerCamelCase  )
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase,  prediction_type=lowerCamelCase,  sample_max_value=lowerCamelCase,  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								for t in [1, 10, 49]:
												self.check_over_forward(time_step=lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 10, 50],  [10, 50, 500]  ):
												self.check_over_forward(time_step=lowerCamelCase,  num_inference_steps=lowerCamelCase  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								for t, eta in zip([1, 10, 49],  [0.0, 0.5, 1.0]  ):
												self.check_over_forward(time_step=lowerCamelCase,  eta=lowerCamelCase  )




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								lowercase__       			=      self.scheduler_classes[0]
								lowercase__       			=      self.get_scheduler_config()
								lowercase__       			=      scheduler_class(**lowerCamelCase  )

								assert torch.sum(torch.abs(scheduler._get_variance(0,  0  ) - 0.0  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(420,  400  ) - 0.14771  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(980,  960  ) - 0.32460  )  ) < 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  ) - 0.00979  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999,  998  ) - 0.02  )  ) < 1E-5




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.full_loop()

								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 172.0067  ) < 1E-2
								assert abs(result_mean.item() - 0.223967  ) < 1E-3




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.full_loop(prediction_type='''v_prediction'''  )

								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 52.5302  ) < 1E-2
								assert abs(result_mean.item() - 0.0684  ) < 1E-3




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								# We specify different beta, so that the first alpha is 0.99
								lowercase__       			=      self.full_loop(set_alpha_to_one=lowerCamelCase,  beta_start=0.01  )
								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 149.8295  ) < 1E-2
								assert abs(result_mean.item() - 0.1951  ) < 1E-3




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								# We specify different beta, so that the first alpha is 0.99
								lowercase__       			=      self.full_loop(set_alpha_to_one=lowerCamelCase,  beta_start=0.01  )
								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 149.0784  ) < 1E-2
								assert abs(result_mean.item() - 0.1941  ) < 1E-3












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__     :      int             =			{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      List[Any]             =			[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Tuple             =			['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

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




