



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class 						lowerCamelCase__			(		unittest.TestCase ):
						# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
						_lowerCAmelCase             =			(ScoreSdeVeScheduler,)
						_lowerCAmelCase             =			()


						@property
						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							Tuple									=4
													a__:							int									=3
													a__:							str									=8
													a__:							int									=8

													a__:							Union[str, Any]									=torch.rand((batch_size, num_channels, height, width)      )

													return sample


						@property
						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							Optional[Any]									=4
													a__:							Any									=3
													a__:							Optional[Any]									=8
													a__:							Any									=8

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

													return sample


						def 	_lowerCamelCase      (			self    :	int      ):





													def model(_a    :	int   ,							_a    :	Union[str, Any]   ,							*_a    :	Optional[Any]      ):
																				return sample * t / (t + 1)

													return model


						def 	_lowerCamelCase      (			self    :	Tuple   ,							**_a    :	Union[str, Any]      ):
													a__:							int									={
													    "num_train_timesteps": 2_0_0_0,
													    "snr": 0.1_5,
													    "sigma_min": 0.0_1,
													    "sigma_max": 1_3_4_8,
													    "sampling_eps": 1e-5,
													}

													config.update(**_lowerCAmelCase      )
													return config


						def 	_lowerCamelCase      (			self    :	Optional[Any]   ,							_a    :	List[Any]=0   ,							**_a    :	int      ):
													a__:							Any									=dict(self.forward_default_kwargs      )

													for scheduler_class in self.scheduler_classes:
																				a__:							Optional[Any]									=self.dummy_sample
																				a__:							str									=0.1 * sample

																				a__:							str									=self.get_scheduler_config(**_lowerCAmelCase      )
																				a__:							Tuple									=scheduler_class(**_lowerCAmelCase      )

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(_lowerCAmelCase      )
																											a__:							List[Any]									=scheduler_class.from_pretrained(_lowerCAmelCase      )

																				a__:							str									=scheduler.step_pred(
																				    _lowerCAmelCase   ,							_lowerCAmelCase   ,							_lowerCAmelCase   ,							generator=torch.manual_seed(0      )   ,							**_lowerCAmelCase      ).prev_sample
																				a__:							str									=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"

																				a__:							str									=scheduler.step_correct(_lowerCAmelCase   ,							_lowerCAmelCase   ,							generator=torch.manual_seed(0      )   ,							**_lowerCAmelCase      ).prev_sample
																				a__:							List[str]									=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    :	str   ,							_a    :	Optional[Any]=0   ,							**_a    :	Union[str, Any]      ):
													a__:							str									=dict(self.forward_default_kwargs      )
													kwargs.update(_lowerCAmelCase      )

													for scheduler_class in self.scheduler_classes:
																				a__:							Any									=self.dummy_sample
																				a__:							Union[str, Any]									=0.1 * sample

																				a__:							Optional[int]									=self.get_scheduler_config()
																				a__:							Tuple									=scheduler_class(**_lowerCAmelCase      )

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(_lowerCAmelCase      )
																											a__:							int									=scheduler_class.from_pretrained(_lowerCAmelCase      )

																				a__:							Dict									=scheduler.step_pred(
																				    _lowerCAmelCase   ,							_lowerCAmelCase   ,							_lowerCAmelCase   ,							generator=torch.manual_seed(0      )   ,							**_lowerCAmelCase      ).prev_sample
																				a__:							Dict									=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"

																				a__:							List[str]									=scheduler.step_correct(_lowerCAmelCase   ,							_lowerCAmelCase   ,							generator=torch.manual_seed(0      )   ,							**_lowerCAmelCase      ).prev_sample
																				a__:							List[str]									=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    :	List[str]      ):





													for timesteps in [1_0, 1_0_0, 1_0_0_0]:
																				self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )


						def 	_lowerCamelCase      (			self    :	Any      ):





													for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]   ,							[1, 1_0_0, 1_0_0_0]      ):
																				self.check_over_configs(sigma_min=_lowerCAmelCase   ,							sigma_max=_lowerCAmelCase      )


						def 	_lowerCamelCase      (			self    :	Dict      ):





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


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							str									=dict(self.forward_default_kwargs      )

													a__:							Optional[Any]									=self.scheduler_classes[0]
													a__:							List[Any]									=self.get_scheduler_config()
													a__:							str									=scheduler_class(**_lowerCAmelCase      )

													a__:							Dict									=3

													a__:							List[Any]									=self.dummy_model()
													a__:							List[str]									=self.dummy_sample_deter

													scheduler.set_sigmas(_lowerCAmelCase      )
													scheduler.set_timesteps(_lowerCAmelCase      )
													a__:							List[str]									=torch.manual_seed(0      )

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

																				for _ in range(scheduler.config.correct_steps      ):
																											with torch.no_grad():
																																		a__:							Optional[int]									=model(_lowerCAmelCase   ,							_lowerCAmelCase      )
																											a__:							Optional[Any]									=scheduler.step_correct(_lowerCAmelCase   ,							_lowerCAmelCase   ,							generator=_lowerCAmelCase   ,							**_lowerCAmelCase      ).prev_sample

																				with torch.no_grad():
																											a__:							Dict									=model(_lowerCAmelCase   ,							_lowerCAmelCase      )

																				a__:							Tuple									=scheduler.step_pred(_lowerCAmelCase   ,							_lowerCAmelCase   ,							_lowerCAmelCase   ,							generator=_lowerCAmelCase   ,							**_lowerCAmelCase      )
																				a__				,      a__:							Dict									=output.prev_sample, output.prev_sample_mean

													a__:							Any									=torch.sum(torch.abs(_lowerCAmelCase      )      )
													a__:							Dict									=torch.mean(torch.abs(_lowerCAmelCase      )      )

													assert np.isclose(result_sum.item()   ,							1_4_3_7_2_7_5_8_5_2_8.0      )
													assert np.isclose(result_mean.item()   ,							1_8_7_1_4_5_3_0.0      )







						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Tuple									=dict(self.forward_default_kwargs      )

													a__:							Dict									=kwargs.pop("num_inference_steps"   ,							_lowerCAmelCase      )

													for scheduler_class in self.scheduler_classes:
																				a__:							Optional[Any]									=self.get_scheduler_config()
																				a__:							str									=scheduler_class(**_lowerCAmelCase      )

																				a__:							str									=self.dummy_sample
																				a__:							List[str]									=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"      ):
																											a__:							Tuple									=num_inference_steps

																				a__:							Tuple									=scheduler.step_pred(_lowerCAmelCase   ,							0   ,							_lowerCAmelCase   ,							generator=torch.manual_seed(0      )   ,							**_lowerCAmelCase      ).prev_sample
																				a__:							Tuple									=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      )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:					Optional[int]         =							{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					List[str]         =							[
					    '''DPRContextEncoderTokenizerFast''',
					    '''DPRQuestionEncoderTokenizerFast''',
					    '''DPRReaderTokenizerFast''',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[Any]         =							[
					    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPRContextEncoder''',
					    '''DPRPretrainedContextEncoder''',
					    '''DPRPreTrainedModel''',
					    '''DPRPretrainedQuestionEncoder''',
					    '''DPRPretrainedReader''',
					    '''DPRQuestionEncoder''',
					    '''DPRReader''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[int]         =							[
					    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFDPRContextEncoder''',
					    '''TFDPRPretrainedContextEncoder''',
					    '''TFDPRPretrainedQuestionEncoder''',
					    '''TFDPRPretrainedReader''',
					    '''TFDPRQuestionEncoder''',
					    '''TFDPRReader''',
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					UpperCamelCase			:					List[Any]         =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

