




"""simple docstring"""

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class 			lowerCamelCase__    (  unittest.TestCase			):
			a__ :      List[str]								=     (ScoreSdeVeScheduler,)
			a__ :      Union[str, Any]								=     ()


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				4
					snake_case		:						Tuple								=				3
					snake_case		:						Optional[Any]								=				8
					snake_case		:						Dict								=				8

					snake_case		:						List[Any]								=				torch.rand((batch_size, num_channels, height, width)							)

					return sample


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				4
					snake_case		:						Optional[Any]								=				3
					snake_case		:						List[str]								=				8
					snake_case		:						Dict								=				8

					snake_case		:						List[str]								=				batch_size * num_channels * height * width
					snake_case		:						Any								=				torch.arange(_lowerCamelCase							)
					snake_case		:						Any								=				sample.reshape(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase							)
					snake_case		:						Dict								=				sample / num_elems
					snake_case		:						Optional[int]								=				sample.permute(3				, 0				, 1				, 2							)

					return sample


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					def model(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, *SCREAMING_SNAKE_CASE							):
							return sample * t / (t + 1)

					return model


			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						List[str]								=				{
					    """num_train_timesteps""": 2_000,
					    """snr""": 0.15,
					    """sigma_min""": 0.01,
					    """sigma_max""": 1_348,
					    """sampling_eps""": 1E-5,
					}

					config.update(**_lowerCamelCase							)
					return config


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=0				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						str								=				dict(self.forward_default_kwargs							)

					for scheduler_class in self.scheduler_classes:
							snake_case		:						int								=				self.dummy_sample
							snake_case		:						Tuple								=				0.1 * sample

							snake_case		:						int								=				self.get_scheduler_config(**_lowerCamelCase							)
							snake_case		:						Dict								=				scheduler_class(**_lowerCamelCase							)

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(_lowerCamelCase							)
									snake_case		:						Union[str, Any]								=				scheduler_class.from_pretrained(_lowerCamelCase							)

							snake_case		:						Tuple								=				scheduler.step_pred(
							    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample
							snake_case		:						Optional[int]								=				new_scheduler.step_pred(
							    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample

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

							snake_case		:						Optional[int]								=				scheduler.step_correct(_lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample
							snake_case		:						Union[str, Any]								=				new_scheduler.step_correct(
							    _lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample

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


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=0				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				dict(self.forward_default_kwargs							)
					kwargs.update(_lowerCamelCase							)

					for scheduler_class in self.scheduler_classes:
							snake_case		:						List[str]								=				self.dummy_sample
							snake_case		:						List[str]								=				0.1 * sample

							snake_case		:						Optional[Any]								=				self.get_scheduler_config()
							snake_case		:						Optional[Any]								=				scheduler_class(**_lowerCamelCase							)

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(_lowerCamelCase							)
									snake_case		:						Union[str, Any]								=				scheduler_class.from_pretrained(_lowerCamelCase							)

							snake_case		:						Optional[int]								=				scheduler.step_pred(
							    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample
							snake_case		:						Any								=				new_scheduler.step_pred(
							    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample

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

							snake_case		:						Optional[Any]								=				scheduler.step_correct(_lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample
							snake_case		:						Dict								=				new_scheduler.step_correct(
							    _lowerCamelCase				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample

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


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					for timesteps in [10, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=_lowerCamelCase							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]				, [1, 100, 1_000]							):
							self.check_over_configs(sigma_min=_lowerCamelCase				, sigma_max=_lowerCamelCase							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					for t in [0.1, 0.5, 0.75]:
							self.check_over_forward(time_step=_lowerCamelCase							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				dict(self.forward_default_kwargs							)

					snake_case		:						int								=				self.scheduler_classes[0]
					snake_case		:						int								=				self.get_scheduler_config()
					snake_case		:						Dict								=				scheduler_class(**_lowerCamelCase							)

					snake_case		:						List[str]								=				3

					snake_case		:						List[Any]								=				self.dummy_model()
					snake_case		:						int								=				self.dummy_sample_deter

					scheduler.set_sigmas(_lowerCamelCase							)
					scheduler.set_timesteps(_lowerCamelCase							)
					snake_case		:						Tuple								=				torch.manual_seed(0							)

					for i, t in enumerate(scheduler.timesteps							):
							snake_case		:						Tuple								=				scheduler.sigmas[i]

							for _ in range(scheduler.config.correct_steps							):
									with torch.no_grad():
											snake_case		:						Optional[int]								=				model(_lowerCamelCase				, _lowerCamelCase							)
									snake_case		:						List[str]								=				scheduler.step_correct(_lowerCamelCase				, _lowerCamelCase				, generator=_lowerCamelCase				, **_lowerCamelCase							).prev_sample

							with torch.no_grad():
									snake_case		:						Tuple								=				model(_lowerCamelCase				, _lowerCamelCase							)

							snake_case		:						str								=				scheduler.step_pred(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, generator=_lowerCamelCase				, **_lowerCamelCase							)
							snake_case		:						List[str]								=				output.prev_sample, output.prev_sample_mean

					snake_case		:						int								=				torch.sum(torch.abs(_lowerCamelCase							)							)
					snake_case		:						str								=				torch.mean(torch.abs(_lowerCamelCase							)							)

					assert np.isclose(result_sum.item()				, 14_372_758_528.0							)
					assert np.isclose(result_mean.item()				, 18_714_530.0							)



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				dict(self.forward_default_kwargs							)

					snake_case		:						Union[str, Any]								=				kwargs.pop("num_inference_steps"				, _lowerCamelCase							)

					for scheduler_class in self.scheduler_classes:
							snake_case		:						Dict								=				self.get_scheduler_config()
							snake_case		:						List[Any]								=				scheduler_class(**_lowerCamelCase							)

							snake_case		:						str								=				self.dummy_sample
							snake_case		:						Tuple								=				0.1 * sample

							if num_inference_steps is not None and hasattr(_lowerCamelCase				, "set_timesteps"							):
									scheduler.set_timesteps(_lowerCamelCase							)
							elif num_inference_steps is not None and not hasattr(_lowerCamelCase				, "set_timesteps"							):
									snake_case		:						Dict								=				num_inference_steps

							snake_case		:						str								=				scheduler.step_pred(_lowerCamelCase				, 0				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample
							snake_case		:						int								=				scheduler.step_pred(_lowerCamelCase				, 1				, _lowerCamelCase				, generator=torch.manual_seed(0							)				, **_lowerCamelCase							).prev_sample

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










"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)


			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if text is None and images is None:
							raise ValueError("You have to specify either text or images. Both cannot be none."							)

					if text is not None:
							snake_case		:						Dict								=				self.tokenizer(
							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					if images is not None:
							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					if text is not None and images is not None:
							snake_case		:						Dict								=				image_features.pixel_values
							return encoding
					elif text is not None:
							return encoding
					else:
							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)



			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
					snake_case		:						Any								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

