


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			List[Any]       			=  (PNDMScheduler,)
		lowerCamelCase_	:			Optional[int]       			=  (("""num_inference_steps""", 5_0),)






		def _snake_case		(						self				:     int   ,  **UpperCAmelCase				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:int				     =						{
				    "num_train_timesteps": 10_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**UpperCAmelCase)
				return config






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     int=0   ,  **UpperCAmelCase				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						dict(self.forward_default_kwargs)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						kwargs.pop("num_inference_steps"   ,  UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:int				     =						self.dummy_sample
				SCREAMING_SNAKE_CASE_				:Dict				     =						0.1 * sample
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_scheduler_config(**UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:str				     =						scheduler_class(**UpperCAmelCase)
						scheduler.set_timesteps(UpperCAmelCase)
						# copy over dummy past residuals
						SCREAMING_SNAKE_CASE_				:Tuple				     =						dummy_past_residuals[:]

						with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(UpperCAmelCase)
								SCREAMING_SNAKE_CASE_				:Any				     =						scheduler_class.from_pretrained(UpperCAmelCase)
								new_scheduler.set_timesteps(UpperCAmelCase)
								# copy over dummy past residuals
								SCREAMING_SNAKE_CASE_				:Tuple				     =						dummy_past_residuals[:]

						SCREAMING_SNAKE_CASE_				:Dict				     =						scheduler.step_prk(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample
						SCREAMING_SNAKE_CASE_				:int				     =						new_scheduler.step_prk(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample

						assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"

						SCREAMING_SNAKE_CASE_				:int				     =						scheduler.step_plms(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample
						SCREAMING_SNAKE_CASE_				:Any				     =						new_scheduler.step_plms(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample

						assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"






		def _snake_case		(						self				:     Dict):
				pass






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Tuple=0   ,  **UpperCAmelCase				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Any				     =						dict(self.forward_default_kwargs)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						kwargs.pop("num_inference_steps"   ,  UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.dummy_sample
				SCREAMING_SNAKE_CASE_				:Any				     =						0.1 * sample
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						scheduler_class(**UpperCAmelCase)
						scheduler.set_timesteps(UpperCAmelCase)

						# copy over dummy past residuals (must be after setting timesteps)
						SCREAMING_SNAKE_CASE_				:str				     =						dummy_past_residuals[:]

						with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(UpperCAmelCase)
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						scheduler_class.from_pretrained(UpperCAmelCase)
								# copy over dummy past residuals
								new_scheduler.set_timesteps(UpperCAmelCase)

								# copy over dummy past residual (must be after setting timesteps)
								SCREAMING_SNAKE_CASE_				:Any				     =						dummy_past_residuals[:]

						SCREAMING_SNAKE_CASE_				:str				     =						scheduler.step_prk(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample
						SCREAMING_SNAKE_CASE_				:Any				     =						new_scheduler.step_prk(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample

						assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"

						SCREAMING_SNAKE_CASE_				:Any				     =						scheduler.step_plms(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						new_scheduler.step_plms(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample

						assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"






		def _snake_case		(						self				:     List[Any]   ,  **UpperCAmelCase				:     Any):
				SCREAMING_SNAKE_CASE_				:int				     =						self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.get_scheduler_config(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						scheduler_class(**UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						10
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.dummy_model()
				SCREAMING_SNAKE_CASE_				:str				     =						self.dummy_sample_deter
				scheduler.set_timesteps(UpperCAmelCase)

				for i, t in enumerate(scheduler.prk_timesteps):
						SCREAMING_SNAKE_CASE_				:Dict				     =						model(UpperCAmelCase   ,  UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Dict				     =						scheduler.step_prk(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase).prev_sample

				for i, t in enumerate(scheduler.plms_timesteps):
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						scheduler.step_plms(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase).prev_sample

				return sample






		def _snake_case		(						self				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						dict(self.forward_default_kwargs)

				SCREAMING_SNAKE_CASE_				:Any				     =						kwargs.pop("num_inference_steps"   ,  UpperCAmelCase)

				for scheduler_class in self.scheduler_classes:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_				:str				     =						scheduler_class(**UpperCAmelCase)

						SCREAMING_SNAKE_CASE_				:Dict				     =						self.dummy_sample
						SCREAMING_SNAKE_CASE_				:List[str]				     =						0.1 * sample

						if num_inference_steps is not None and hasattr(UpperCAmelCase   ,  "set_timesteps"):
								scheduler.set_timesteps(UpperCAmelCase)
						elif num_inference_steps is not None and not hasattr(UpperCAmelCase   ,  "set_timesteps"):
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						num_inference_steps

						# copy over dummy past residuals (must be done after set_timesteps)
						SCREAMING_SNAKE_CASE_				:int				     =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						SCREAMING_SNAKE_CASE_				:Dict				     =						dummy_past_residuals[:]

						SCREAMING_SNAKE_CASE_				:int				     =						scheduler.step_prk(UpperCAmelCase   ,  0   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample
						SCREAMING_SNAKE_CASE_				:str				     =						scheduler.step_prk(UpperCAmelCase   ,  1   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample

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

						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						scheduler.step_plms(UpperCAmelCase   ,  0   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample
						SCREAMING_SNAKE_CASE_				:Any				     =						scheduler.step_plms(UpperCAmelCase   ,  1   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample

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






		def _snake_case		(						self				:     List[Any]):
				for timesteps in [1_00, 10_00]:
						self.check_over_configs(num_train_timesteps=UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]):
				for steps_offset in [0, 1]:
						self.check_over_configs(steps_offset=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE_				:Any				     =						self.get_scheduler_config(steps_offset=1)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						scheduler_class(**UpperCAmelCase)
				scheduler.set_timesteps(10)
				assert torch.equal(
				    scheduler.timesteps   ,  torch.LongTensor(
				        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1])   ,  )






		def _snake_case		(						self				:     str):
				for beta_start, beta_end in zip([0.0001, 0.001]   ,  [0.002, 0.02]):
						self.check_over_configs(beta_start=UpperCAmelCase   ,  beta_end=UpperCAmelCase)






		def _snake_case		(						self				:     Optional[int]):
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=UpperCAmelCase)






		def _snake_case		(						self				:     Tuple):
				for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=UpperCAmelCase)






		def _snake_case		(						self				:     int):
				for t in [1, 5, 10]:
						self.check_over_forward(time_step=UpperCAmelCase)






		def _snake_case		(						self				:     Tuple):
				for t, num_inference_steps in zip([1, 5, 10]   ,  [10, 50, 1_00]):
						self.check_over_forward(num_inference_steps=UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]):
				# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						27

				for scheduler_class in self.scheduler_classes:
						SCREAMING_SNAKE_CASE_				:str				     =						self.dummy_sample
						SCREAMING_SNAKE_CASE_				:str				     =						0.1 * sample

						SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						scheduler_class(**UpperCAmelCase)

						scheduler.set_timesteps(UpperCAmelCase)

						# before power of 3 fix, would error on first step, so we only need to do two
						for i, t in enumerate(scheduler.prk_timesteps[:2]):
								SCREAMING_SNAKE_CASE_				:Dict				     =						scheduler.step_prk(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase).prev_sample






		def _snake_case		(						self				:     Dict):
				with self.assertRaises(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						scheduler_class(**UpperCAmelCase)

						scheduler.step_plms(self.dummy_sample   ,  1   ,  self.dummy_sample).prev_sample






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.full_loop()
				SCREAMING_SNAKE_CASE_				:Dict				     =						torch.sum(torch.abs(UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:int				     =						torch.mean(torch.abs(UpperCAmelCase))

				assert abs(result_sum.item() - 198.1318) < 1E-2
				assert abs(result_mean.item() - 0.2580) < 1E-3






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:int				     =						self.full_loop(prediction_type="v_prediction")
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.sum(torch.abs(UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:Tuple				     =						torch.mean(torch.abs(UpperCAmelCase))

				assert abs(result_sum.item() - 67.3986) < 1E-2
				assert abs(result_mean.item() - 0.0878) < 1E-3






		def _snake_case		(						self				:     List[Any]):
				# We specify different beta, so that the first alpha is 0.99
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.full_loop(set_alpha_to_one=UpperCAmelCase   ,  beta_start=0.01)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.sum(torch.abs(UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.mean(torch.abs(UpperCAmelCase))

				assert abs(result_sum.item() - 230.0399) < 1E-2
				assert abs(result_mean.item() - 0.2995) < 1E-3







		def _snake_case		(						self				:     List[str]):
				# We specify different beta, so that the first alpha is 0.99
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.full_loop(set_alpha_to_one=UpperCAmelCase   ,  beta_start=0.01)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						torch.sum(torch.abs(UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:Dict				     =						torch.mean(torch.abs(UpperCAmelCase))

				assert abs(result_sum.item() - 186.9482) < 1E-2
				assert abs(result_mean.item() - 0.2434) < 1E-3










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,
)


SCREAMING_SNAKE_CASE__     					=			{
    "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:
		SCREAMING_SNAKE_CASE__     					=			["OwlViTFeatureExtractor"]
		SCREAMING_SNAKE_CASE__     					=			["OwlViTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE__     					=			[
		    "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

		SCREAMING_SNAKE_CASE__     					=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






