

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  (CMStochasticIterativeScheduler,)
		a__					       =  1_0


		def 		A__					(				self	,       **__snake_case):
			_UpperCamelCase   :		str			=						{
			    'num_train_timesteps': 2_01,
			    'sigma_min': 0.0_0_2,
			    'sigma_max': 8_0.0,
			}

			config.update(**__snake_case)
			return config


		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						10

			_UpperCamelCase   :		Any			=						self.get_scheduler_config()
			_UpperCamelCase   :		str			=						self.scheduler_classes[0](**__snake_case)

			scheduler.set_timesteps(__snake_case)

			_UpperCamelCase   :		Optional[int]			=						scheduler.timesteps[0]
			_UpperCamelCase   :		Union[str, Any]			=						scheduler.timesteps[1]

			_UpperCamelCase   :		Union[str, Any]			=						self.dummy_sample
			_UpperCamelCase   :		Tuple			=						0.1 * sample

			_UpperCamelCase   :		Any			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case).prev_sample
			_UpperCamelCase   :		Union[str, Any]			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case).prev_sample

			self.assertEqual(output_a.shape	,       sample.shape)
			self.assertEqual(output_a.shape	,       output_a.shape)


		def 		A__					(				self):
			for timesteps in [10, 50, 1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=__snake_case)


		def 		A__					(				self):
			for clip_denoised in [True, False]:
				self.check_over_configs(clip_denoised=__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.scheduler_classes[0]
			_UpperCamelCase   :		Dict			=						self.get_scheduler_config()
			_UpperCamelCase   :		Union[str, Any]			=						scheduler_class(**__snake_case)

			_UpperCamelCase   :		Dict			=						1
			scheduler.set_timesteps(__snake_case)
			_UpperCamelCase   :		Union[str, Any]			=						scheduler.timesteps

			_UpperCamelCase   :		List[Any]			=						torch.manual_seed(0)

			_UpperCamelCase   :		str			=						self.dummy_model()
			_UpperCamelCase   :		Dict			=						self.dummy_sample_deter * scheduler.init_noise_sigma

			for i, t in enumerate(__snake_case):
				# 1. scale model input
				_UpperCamelCase   :		Union[str, Any]			=						scheduler.scale_model_input(__snake_case	,       __snake_case)

				# 2. predict noise residual
				_UpperCamelCase   :		Optional[Any]			=						model(__snake_case	,       __snake_case)

				# 3. predict previous sample x_t-1
				_UpperCamelCase   :		Tuple			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       generator=__snake_case).prev_sample

				_UpperCamelCase   :		str			=						pred_prev_sample

			_UpperCamelCase   :		Any			=						torch.sum(torch.abs(__snake_case))
			_UpperCamelCase   :		str			=						torch.mean(torch.abs(__snake_case))

			assert abs(result_sum.item() - 1_9_2.7_6_1_4) < 1e-2
			assert abs(result_mean.item() - 0.2_5_1_0) < 1e-3


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						self.scheduler_classes[0]
			_UpperCamelCase   :		Tuple			=						self.get_scheduler_config()
			_UpperCamelCase   :		Union[str, Any]			=						scheduler_class(**__snake_case)

			_UpperCamelCase   :		int			=						[1_06, 0]
			scheduler.set_timesteps(timesteps=__snake_case)
			_UpperCamelCase   :		str			=						scheduler.timesteps

			_UpperCamelCase   :		Tuple			=						torch.manual_seed(0)

			_UpperCamelCase   :		Tuple			=						self.dummy_model()
			_UpperCamelCase   :		Union[str, Any]			=						self.dummy_sample_deter * scheduler.init_noise_sigma

			for t in timesteps:
				# 1. scale model input
				_UpperCamelCase   :		Optional[Any]			=						scheduler.scale_model_input(__snake_case	,       __snake_case)

				# 2. predict noise residual
				_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case	,       __snake_case)

				# 3. predict previous sample x_t-1
				_UpperCamelCase   :		Optional[Any]			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       generator=__snake_case).prev_sample

				_UpperCamelCase   :		Optional[int]			=						pred_prev_sample

			_UpperCamelCase   :		int			=						torch.sum(torch.abs(__snake_case))
			_UpperCamelCase   :		str			=						torch.mean(torch.abs(__snake_case))

			assert abs(result_sum.item() - 3_4_7.6_3_5_7) < 1e-2
			assert abs(result_mean.item() - 0.4_5_2_7) < 1e-3


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.scheduler_classes[0]
			_UpperCamelCase   :		Optional[Any]			=						self.get_scheduler_config()
			_UpperCamelCase   :		Dict			=						scheduler_class(**__snake_case)

			_UpperCamelCase   :		List[str]			=						[39, 30, 12, 15, 0]

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


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.scheduler_classes[0]
			_UpperCamelCase   :		Optional[int]			=						self.get_scheduler_config()
			_UpperCamelCase   :		Dict			=						scheduler_class(**__snake_case)

			_UpperCamelCase   :		Tuple			=						[39, 30, 12, 1, 0]
			_UpperCamelCase   :		Tuple			=						len(__snake_case)

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






		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.scheduler_classes[0]
			_UpperCamelCase   :		Any			=						self.get_scheduler_config()
			_UpperCamelCase   :		Any			=						scheduler_class(**__snake_case)

			_UpperCamelCase   :		Optional[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)







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




