




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (ScoreSdeVeScheduler,)
						lowerCAmelCase_							=       ()






						@property
						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					4
							snake_case_		   =					3
							snake_case_		   =					8
							snake_case_		   =					8

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

							return sample






						@property
						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					4
							snake_case_		   =					3
							snake_case_		   =					8
							snake_case_		   =					8

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

							return sample






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							def model(__lowercase							:					List[Any]	,     __lowercase							:					List[Any]	,     *__lowercase							:					str						):
								return sample * t / (t + 1)

							return model






						def 	snake_case__						(						self							:					Dict	,     **__lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 20_00,
							    "snr": 0.15,
							    "sigma_min": 0.01,
							    "sigma_max": 13_48,
							    "sampling_eps": 1E-5,
							}

							config.update(**snake_case__						)
							return config






						def 	snake_case__						(						self							:					int	,     __lowercase							:					List[Any]=0	,     **__lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							snake_case_		   =					dict(self.forward_default_kwargs						)

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

								snake_case_		   =					self.get_scheduler_config(**snake_case__						)
								snake_case_		   =					scheduler_class(**snake_case__						)

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(snake_case__						)
									snake_case_		   =					scheduler_class.from_pretrained(snake_case__						)

								snake_case_		   =					scheduler.step_pred(
								    snake_case__	,     snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample
								snake_case_		   =					new_scheduler.step_pred(
								    snake_case__	,     snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample

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

								snake_case_		   =					scheduler.step_correct(snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample
								snake_case_		   =					new_scheduler.step_correct(
								    snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample

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






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					int=0	,     **__lowercase							:					str						):






							"""simple docstring"""






							snake_case_		   =					dict(self.forward_default_kwargs						)
							kwargs.update(snake_case__						)

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

								snake_case_		   =					self.get_scheduler_config()
								snake_case_		   =					scheduler_class(**snake_case__						)

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(snake_case__						)
									snake_case_		   =					scheduler_class.from_pretrained(snake_case__						)

								snake_case_		   =					scheduler.step_pred(
								    snake_case__	,     snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample
								snake_case_		   =					new_scheduler.step_pred(
								    snake_case__	,     snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample

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

								snake_case_		   =					scheduler.step_correct(snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample
								snake_case_		   =					new_scheduler.step_correct(
								    snake_case__	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample

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






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]	,     [1, 1_00, 10_00]						):
								self.check_over_configs(sigma_min=snake_case__	,     sigma_max=snake_case__						)






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							snake_case_		   =					dict(self.forward_default_kwargs						)

							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**snake_case__						)

							snake_case_		   =					3

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter

							scheduler.set_sigmas(snake_case__						)
							scheduler.set_timesteps(snake_case__						)
							snake_case_		   =					torch.manual_seed(0						)

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

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

								with torch.no_grad():
									snake_case_		   =					model(snake_case__	,     snake_case__						)

								snake_case_		   =					scheduler.step_pred(snake_case__	,     snake_case__	,     snake_case__	,     generator=snake_case__	,     **snake_case__						)
								snake_case_   ,				snake_case_		   =					output.prev_sample, output.prev_sample_mean

							snake_case_		   =					torch.sum(torch.abs(snake_case__						)						)
							snake_case_		   =					torch.mean(torch.abs(snake_case__						)						)

							assert np.isclose(result_sum.item()	,     1_43_72_75_85_28.0						)
							assert np.isclose(result_mean.item()	,     18_71_45_30.0						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							snake_case_		   =					dict(self.forward_default_kwargs						)

							snake_case_		   =					kwargs.pop("num_inference_steps"	,     snake_case__						)

							for scheduler_class in self.scheduler_classes:
								snake_case_		   =					self.get_scheduler_config()
								snake_case_		   =					scheduler_class(**snake_case__						)

								snake_case_		   =					self.dummy_sample
								snake_case_		   =					0.1 * sample

								if num_inference_steps is not None and hasattr(snake_case__	,     "set_timesteps"						):
									scheduler.set_timesteps(snake_case__						)
								elif num_inference_steps is not None and not hasattr(snake_case__	,     "set_timesteps"						):
									snake_case_		   =					num_inference_steps

								snake_case_		   =					scheduler.step_pred(snake_case__	,     0	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample
								snake_case_		   =					scheduler.step_pred(snake_case__	,     1	,     snake_case__	,     generator=torch.manual_seed(0						)	,     **snake_case__						).prev_sample

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowercase__    :					List[str]					=						{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Optional[Any]					=						["XLMProphetNetTokenizer"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					List[Any]					=						[
		    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "XLMProphetNetDecoder",
		    "XLMProphetNetEncoder",
		    "XLMProphetNetForCausalLM",
		    "XLMProphetNetForConditionalGeneration",
		    "XLMProphetNetModel",
		    "XLMProphetNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_prophetnet import (
				    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMProphetNetDecoder,
				    XLMProphetNetEncoder,
				    XLMProphetNetForCausalLM,
				    XLMProphetNetForConditionalGeneration,
				    XLMProphetNetModel,
				    XLMProphetNetPreTrainedModel,
				)

else:
		import sys

		lowercase__    :					Any					=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
