


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class      __snake_case				(   unittest.TestCase):
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					SCREAMING_SNAKE_CASE_		:       str								=			(ScoreSdeVeScheduler,)
					SCREAMING_SNAKE_CASE_		:       Tuple								=			()
					@property
					def 	a			(						self       :						int  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      4
											__magic_name__					=      3
											__magic_name__					=      8
											__magic_name__					=      8

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

											return sample
					@property
					def 	a			(						self       :						Optional[Any]  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      4
											__magic_name__					=      3
											__magic_name__					=      8
											__magic_name__					=      8

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

											return sample
					def 	a			(						self       :						int  )							->					int:



											'''simple docstring'''





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

											return model
					def 	a			(						self       :						Tuple	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      {
											    'num_train_timesteps': 2_0_0_0,
											    'snr': 0.15,
											    'sigma_min': 0.01,
											    'sigma_max': 1_3_4_8,
											    'sampling_eps': 1E-5,
											}

											config.update(**_lowerCAmelCase  )
											return config
					def 	a			(						self       :						Optional[Any]	,      _lowerCAmelCase       :						List[Any]=0	,      **_lowerCAmelCase       :						int  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      dict(self.forward_default_kwargs  )

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

																	__magic_name__					=      self.get_scheduler_config(**_lowerCAmelCase  )
																	__magic_name__					=      scheduler_class(**_lowerCAmelCase  )

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

																	__magic_name__					=      scheduler.step_pred(
																	    _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      generator=torch.manual_seed(0  )	,      **_lowerCAmelCase  ).prev_sample
																	__magic_name__					=      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"

																	__magic_name__					=      scheduler.step_correct(_lowerCAmelCase	,      _lowerCAmelCase	,      generator=torch.manual_seed(0  )	,      **_lowerCAmelCase  ).prev_sample
																	__magic_name__					=      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 	a			(						self       :						str	,      _lowerCAmelCase       :						Optional[Any]=0	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      dict(self.forward_default_kwargs  )
											kwargs.update(_lowerCAmelCase  )

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

																	__magic_name__					=      self.get_scheduler_config()
																	__magic_name__					=      scheduler_class(**_lowerCAmelCase  )

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

																	__magic_name__					=      scheduler.step_pred(
																	    _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      generator=torch.manual_seed(0  )	,      **_lowerCAmelCase  ).prev_sample
																	__magic_name__					=      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"

																	__magic_name__					=      scheduler.step_correct(_lowerCAmelCase	,      _lowerCAmelCase	,      generator=torch.manual_seed(0  )	,      **_lowerCAmelCase  ).prev_sample
																	__magic_name__					=      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 	a			(						self       :						List[str]  )							->					Tuple:



											'''simple docstring'''





											for timesteps in [1_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_lowerCAmelCase  )
					def 	a			(						self       :						Any  )							->					Tuple:



											'''simple docstring'''





											for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]	,      [1, 1_0_0, 1_0_0_0]  ):
																	self.check_over_configs(sigma_min=_lowerCAmelCase	,      sigma_max=_lowerCAmelCase  )
					def 	a			(						self       :						Dict  )							->					Optional[Any]:



											'''simple docstring'''





											for t in [0.1, 0.5, 0.75]:
																	self.check_over_forward(time_step=_lowerCAmelCase  )
					def 	a			(						self       :						List[str]  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      dict(self.forward_default_kwargs  )

											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config()
											__magic_name__					=      scheduler_class(**_lowerCAmelCase  )

											__magic_name__					=      3

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter

											scheduler.set_sigmas(_lowerCAmelCase  )
											scheduler.set_timesteps(_lowerCAmelCase  )
											__magic_name__					=      torch.manual_seed(0  )

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

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

																	with torch.no_grad():
																							__magic_name__					=      model(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      scheduler.step_pred(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      generator=_lowerCAmelCase	,      **_lowerCAmelCase  )
																	__magic_name__		,      __magic_name__					=      output.prev_sample, output.prev_sample_mean

											__magic_name__					=      torch.sum(torch.abs(_lowerCAmelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_lowerCAmelCase  )  )

											assert np.isclose(result_sum.item()	,      143_7275_8528.0  )
											assert np.isclose(result_mean.item()	,      1871_4530.0  )







					def 	a			(						self       :						List[Any]  )							->					str:



											'''simple docstring'''





											__magic_name__					=      dict(self.forward_default_kwargs  )

											__magic_name__					=      kwargs.pop('num_inference_steps'	,      _lowerCAmelCase  )

											for scheduler_class in self.scheduler_classes:
																	__magic_name__					=      self.get_scheduler_config()
																	__magic_name__					=      scheduler_class(**_lowerCAmelCase  )

																	__magic_name__					=      self.dummy_sample
																	__magic_name__					=      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'  ):
																							__magic_name__					=      num_inference_steps

																	__magic_name__					=      scheduler.step_pred(_lowerCAmelCase	,      0	,      _lowerCAmelCase	,      generator=torch.manual_seed(0  )	,      **_lowerCAmelCase  ).prev_sample
																	__magic_name__					=      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__)

