


'''simple docstring'''


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 	lowerCAmelCase__			(			UpperCamelCase_	):
			lowerCAmelCase :	str          =     (DDIMParallelScheduler,)
			lowerCAmelCase :	str          =     (("eta", 0.0), ("num_inference_steps", 50))




			def 							lowerCAmelCase__    (		self  :     Tuple	,  **lowerCamelCase__  :     Dict )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      {
					    '''num_train_timesteps''': 10_00,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					    '''clip_sample''': True,
					}

					config.update(**lowerCamelCase__ )
					return config




			def 							lowerCAmelCase__    (		self  :     int	,  **lowerCamelCase__  :     Tuple )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      self.scheduler_classes[0]
					_UpperCAmelCase		:       int								=      self.get_scheduler_config(**lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      scheduler_class(**lowerCamelCase__ )

					_UpperCAmelCase		:       str								=      10, 0.0

					_UpperCAmelCase		:       Tuple								=      self.dummy_model()
					_UpperCAmelCase		:       int								=      self.dummy_sample_deter

					scheduler.set_timesteps(lowerCamelCase__ )

					for t in scheduler.timesteps:
							_UpperCAmelCase		:       Optional[Any]								=      model(lowerCamelCase__	,  lowerCamelCase__ )
							_UpperCAmelCase		:       List[str]								=      scheduler.step(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ ).prev_sample

					return sample




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



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Dict:



					'''simple docstring'''







					for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=lowerCamelCase__ )

					_UpperCAmelCase		:       Any								=      self.scheduler_classes[0]
					_UpperCAmelCase		:       Optional[int]								=      self.get_scheduler_config(steps_offset=1 )
					_UpperCAmelCase		:       Optional[int]								=      scheduler_class(**lowerCamelCase__ )
					scheduler.set_timesteps(5 )
					assert torch.equal(scheduler.timesteps	,  torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )




			def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



					'''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  :     Optional[Any] )   ->Optional[int]:



					'''simple docstring'''







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




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



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[int]:



					'''simple docstring'''







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




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



					'''simple docstring'''







					for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->str:



					'''simple docstring'''







					for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Dict:



					'''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 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->List[str]:



					'''simple docstring'''







					for t in [1, 10, 49]:
							self.check_over_forward(time_step=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Any )   ->Any:



					'''simple docstring'''







					for t, num_inference_steps in zip([1, 10, 50]	,  [10, 50, 5_00] ):
							self.check_over_forward(time_step=lowerCamelCase__	,  num_inference_steps=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->str:



					'''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 							lowerCAmelCase__    (		self  :     List[Any] )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      self.scheduler_classes[0]
					_UpperCAmelCase		:       str								=      self.get_scheduler_config()
					_UpperCAmelCase		:       List[str]								=      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(4_20	,  4_00 ) - 0.1_4_7_7_1 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_80	,  9_60 ) - 0.3_2_4_6_0 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(0	,  0 ) - 0.0 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87	,  4_86 ) - 0.0_0_9_7_9 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99	,  9_98 ) - 0.0_2 ) ) < 1E-5




			def 							lowerCAmelCase__    (		self  :     Any )   ->int:



					'''simple docstring'''







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

					_UpperCAmelCase		:       Dict								=      10, 0.0
					scheduler.set_timesteps(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      self.dummy_model()
					_UpperCAmelCase		:       Tuple								=      self.dummy_sample_deter
					_UpperCAmelCase		:       Tuple								=      self.dummy_sample_deter + 0.1
					_UpperCAmelCase		:       str								=      self.dummy_sample_deter - 0.1

					_UpperCAmelCase		:       List[Any]								=      samplea.shape[0]
					_UpperCAmelCase		:       Any								=      torch.stack([samplea, samplea, samplea]	,  dim=0 )
					_UpperCAmelCase		:       List[str]								=      torch.arange(lowerCamelCase__ )[0:3, None].repeat(1	,  lowerCamelCase__ )

					_UpperCAmelCase		:       Optional[Any]								=      model(samples.flatten(0	,  1 )	,  timesteps.flatten(0	,  1 ) )
					_UpperCAmelCase		:       List[Any]								=      scheduler.batch_step_no_noise(lowerCamelCase__	,  timesteps.flatten(0	,  1 )	,  samples.flatten(0	,  1 )	,  lowerCamelCase__ )

					_UpperCAmelCase		:       Any								=      torch.sum(torch.abs(lowerCamelCase__ ) )
					_UpperCAmelCase		:       int								=      torch.mean(torch.abs(lowerCamelCase__ ) )

					assert abs(result_sum.item() - 1_1_4_7.7_9_0_4 ) < 1E-2
					assert abs(result_mean.item() - 0.4_9_8_2 ) < 1E-3




			def 							lowerCAmelCase__    (		self  :     str )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.full_loop()

					_UpperCAmelCase		:       Any								=      torch.sum(torch.abs(lowerCamelCase__ ) )
					_UpperCAmelCase		:       List[str]								=      torch.mean(torch.abs(lowerCamelCase__ ) )

					assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
					assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3




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



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.full_loop(prediction_type="v_prediction" )

					_UpperCAmelCase		:       str								=      torch.sum(torch.abs(lowerCamelCase__ ) )
					_UpperCAmelCase		:       Union[str, Any]								=      torch.mean(torch.abs(lowerCamelCase__ ) )

					assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
					assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3




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



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      self.full_loop(set_alpha_to_one=lowerCamelCase__	,  beta_start=0.0_1 )
					_UpperCAmelCase		:       Optional[Any]								=      torch.sum(torch.abs(lowerCamelCase__ ) )
					_UpperCAmelCase		:       List[Any]								=      torch.mean(torch.abs(lowerCamelCase__ ) )

					assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
					assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3






			def 							lowerCAmelCase__    (		self  :     Any )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.full_loop(set_alpha_to_one=lowerCamelCase__	,  beta_start=0.0_1 )
					_UpperCAmelCase		:       int								=      torch.sum(torch.abs(lowerCamelCase__ ) )
					_UpperCAmelCase		:       Tuple								=      torch.mean(torch.abs(lowerCamelCase__ ) )

					assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
					assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3










'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__    	=     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'InstructBlipQFormerModel',
						    'InstructBlipPreTrainedModel',
						    'InstructBlipForConditionalGeneration',
						    'InstructBlipVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



