






'''simple docstring'''
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class 	UpperCamelCase__					(     a_			):

		"""simple docstring"""
		UpperCAmelCase__     			=						(DDPMScheduler,)




		def       snake_case     ( self						:						List[str]							,    **__A						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= {
							    "num_train_timesteps": 1_0_0_0,
							    "beta_start": 0.0_0_0_1,
							    "beta_end": 0.0_2,
							    "beta_schedule": "linear",
							    "variance_type": "fixed_small",
							    "clip_sample": True,
							}

							config.update(**__UpperCamelCase    )
							return config




		def       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__UpperCamelCase    )




		def       snake_case     ( self						:						Optional[int]    ):







							"""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=__UpperCamelCase							,    beta_end=__UpperCamelCase    )




		def       snake_case     ( self						:						str    ):







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




		def       snake_case     ( self						:						Optional[int]    ):







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




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







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




		def       snake_case     ( self						:						Optional[int]    ):







							"""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       snake_case     ( self						:						Dict    ):







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




		def       snake_case     ( self						:						List[Any]    ):







							"""simple docstring"""
							for t in [0, 5_0_0, 9_9_9]:
												self.check_over_forward(time_step=__UpperCamelCase    )




		def       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= self.scheduler_classes[0]
							_lowercase			= self.get_scheduler_config()
							_lowercase			= 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_8_7    ) - 0.0_0_9_7_9    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ) - 0.0_2    )    ) < 1e-5




		def       snake_case     ( self						:						Any    ):







							"""simple docstring"""
							_lowercase			= self.scheduler_classes[0]
							_lowercase			= self.get_scheduler_config()
							_lowercase			= scheduler_class(**__UpperCamelCase    )

							_lowercase			= len(__UpperCamelCase    )

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

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

												# 2. predict previous mean of sample x_t-1
												_lowercase			= 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
												_lowercase			= pred_prev_sample

							_lowercase			= torch.sum(torch.abs(__UpperCamelCase    )    )
							_lowercase			= torch.mean(torch.abs(__UpperCamelCase    )    )

							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       snake_case     ( self						:						Dict    ):







							"""simple docstring"""
							_lowercase			= self.scheduler_classes[0]
							_lowercase			= self.get_scheduler_config(prediction_type="v_prediction"    )
							_lowercase			= scheduler_class(**__UpperCamelCase    )

							_lowercase			= len(__UpperCamelCase    )

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

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

												# 2. predict previous mean of sample x_t-1
												_lowercase			= 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
												_lowercase			= pred_prev_sample

							_lowercase			= torch.sum(torch.abs(__UpperCamelCase    )    )
							_lowercase			= torch.mean(torch.abs(__UpperCamelCase    )    )

							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       snake_case     ( self						:						str    ):







							"""simple docstring"""
							_lowercase			= self.scheduler_classes[0]
							_lowercase			= self.get_scheduler_config()
							_lowercase			= scheduler_class(**__UpperCamelCase    )

							_lowercase			= [1_0_0, 8_7, 5_0, 1, 0]

							scheduler.set_timesteps(timesteps=__UpperCamelCase    )

							_lowercase			= scheduler.timesteps

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

												_lowercase			= scheduler.previous_timestep(__UpperCamelCase    )
												_lowercase			= prev_t.item()

												self.assertEqual(__UpperCamelCase							,    __UpperCamelCase    )




		def       snake_case     ( self						:						int    ):







							"""simple docstring"""
							_lowercase			= self.scheduler_classes[0]
							_lowercase			= self.get_scheduler_config()
							_lowercase			= scheduler_class(**__UpperCamelCase    )

							_lowercase			= [1_0_0, 8_7, 5_0, 5_1, 0]

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




		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							_lowercase			= self.scheduler_classes[0]
							_lowercase			= self.get_scheduler_config()
							_lowercase			= scheduler_class(**__UpperCamelCase    )

							_lowercase			= [1_0_0, 8_7, 5_0, 1, 0]
							_lowercase			= 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       snake_case     ( self						:						Union[str, Any]    ):







							"""simple docstring"""
							_lowercase			= self.scheduler_classes[0]
							_lowercase			= self.get_scheduler_config()
							_lowercase			= scheduler_class(**__UpperCamelCase    )

							_lowercase			= [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 typing import TYPE_CHECKING

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


lowerCamelCase      :       List[Any]    =						{
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[Any]    =						['''OwlViTFeatureExtractor''']
 lowerCamelCase      :       str    =						['''OwlViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[str]    =						[
     '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''OwlViTModel''',
     '''OwlViTPreTrainedModel''',
     '''OwlViTTextModel''',
     '''OwlViTVisionModel''',
     '''OwlViTForObjectDetection''',
 ]

if TYPE_CHECKING:
 from .configuration_owlvit import (
     OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     OwlViTConfig,
     OwlViTOnnxConfig,
     OwlViTTextConfig,
     OwlViTVisionConfig,
 )
 from .processing_owlvit import OwlViTProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_owlvit import OwlViTFeatureExtractor
  from .image_processing_owlvit import OwlViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_owlvit import (
      OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OwlViTForObjectDetection,
      OwlViTModel,
      OwlViTPreTrainedModel,
      OwlViTTextModel,
      OwlViTVisionModel,
  )

else:
 import sys

 lowerCamelCase      :       List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






