



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler





class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		(ScoreSdeVeScheduler,)
							snake_case_        =		()


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		4
													a_					=		3
													a_					=		8
													a_					=		8

													a_					=		torch.rand((batch_size, num_channels, height, width)		)

													return sample


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		4
													a_					=		3
													a_					=		8
													a_					=		8

													a_					=		batch_size * num_channels * height * width
													a_					=		torch.arange(_UpperCAmelCase		)
													a_					=		sample.reshape(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													a_					=		sample / num_elems
													a_					=		sample.permute(3			,    0			,    1			,    2		)

													return sample


							def 						lowercase__     (   self		):







													"""simple docstring"""


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

													return model


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		{
													    """num_train_timesteps""": 2_000,
													    """snr""": 0.1_5,
													    """sigma_min""": 0.0_1,
													    """sigma_max""": 1_348,
													    """sampling_eps""": 1e-5,
													}

													config.update(**_UpperCAmelCase		)
													return config


							def 						lowercase__     (   self			,    _UpperCAmelCase=0			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		dict(self.forward_default_kwargs		)

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

																			a_					=		self.get_scheduler_config(**_UpperCAmelCase		)
																			a_					=		scheduler_class(**_UpperCAmelCase		)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(_UpperCAmelCase		)
																									a_					=		scheduler_class.from_pretrained(_UpperCAmelCase		)

																			a_					=		scheduler.step_pred(
																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
																			a_					=		new_scheduler.step_pred(
																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample

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

																			a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
																			a_					=		new_scheduler.step_correct(
																			    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample

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


							def 						lowercase__     (   self			,    _UpperCAmelCase=0			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		dict(self.forward_default_kwargs		)
													kwargs.update(_UpperCAmelCase		)

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

																			a_					=		self.get_scheduler_config()
																			a_					=		scheduler_class(**_UpperCAmelCase		)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(_UpperCAmelCase		)
																									a_					=		scheduler_class.from_pretrained(_UpperCAmelCase		)

																			a_					=		scheduler.step_pred(
																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
																			a_					=		new_scheduler.step_pred(
																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample

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

																			a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
																			a_					=		new_scheduler.step_correct(
																			    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample

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


							def 						lowercase__     (   self		):







													"""simple docstring"""


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


							def 						lowercase__     (   self		):







													"""simple docstring"""


													for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]			,    [1, 100, 1_000]		):
																			self.check_over_configs(sigma_min=_UpperCAmelCase			,    sigma_max=_UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													for t in [0.1, 0.5, 0.7_5]:
																			self.check_over_forward(time_step=_UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		dict(self.forward_default_kwargs		)

													a_					=		self.scheduler_classes[0]
													a_					=		self.get_scheduler_config()
													a_					=		scheduler_class(**_UpperCAmelCase		)

													a_					=		3

													a_					=		self.dummy_model()
													a_					=		self.dummy_sample_deter

													scheduler.set_sigmas(_UpperCAmelCase		)
													scheduler.set_timesteps(_UpperCAmelCase		)
													a_					=		torch.manual_seed(0		)

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

																			for _ in range(scheduler.config.correct_steps		):
																									with torch.no_grad():
																															a_					=		model(_UpperCAmelCase			,    _UpperCAmelCase		)
																									a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=_UpperCAmelCase			,    **_UpperCAmelCase		).prev_sample

																			with torch.no_grad():
																									a_					=		model(_UpperCAmelCase			,    _UpperCAmelCase		)

																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=_UpperCAmelCase			,    **_UpperCAmelCase		)
																			a_			,   a_					=		output.prev_sample, output.prev_sample_mean

													a_					=		torch.sum(torch.abs(_UpperCAmelCase		)		)
													a_					=		torch.mean(torch.abs(_UpperCAmelCase		)		)

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






							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		dict(self.forward_default_kwargs		)

													a_					=		kwargs.pop("""num_inference_steps"""			,    _UpperCAmelCase		)

													for scheduler_class in self.scheduler_classes:
																			a_					=		self.get_scheduler_config()
																			a_					=		scheduler_class(**_UpperCAmelCase		)

																			a_					=		self.dummy_sample
																			a_					=		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"""		):
																									a_					=		num_inference_steps

																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    0			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    1			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample

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



from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch





class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		['''image_processor''', '''tokenizer''']
							snake_case_        =		'''OneFormerImageProcessor'''
							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if image_processor is None:
																			raise ValueError("""You need to specify an `image_processor`."""		)
													if tokenizer is None:
																			raise ValueError("""You need to specify a `tokenizer`."""		)

													a_					=		max_seq_length
													a_					=		task_seq_length

													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):







													"""simple docstring"""


													if text_list is None:
																			raise ValueError("""tokens cannot be None."""		)

													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)

													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]

													a_					=		[]
													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
																			token_inputs.append(token.unsqueeze(0		)		)

													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
													return token_inputs


							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)






							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)