
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler





class      _lowerCAmelCase (     unittest.TestCase	):
				_lowercase      		=(ScoreSdeVeScheduler,)
				_lowercase      		=()




				@property
				def 					__a						( self					)    ->				Dict:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     3
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

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

							return sample




				@property
				def 					__a						( self					)    ->				Any:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     3
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

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

							return sample




				def 					__a						( self					)    ->				List[str]:


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

							return model




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "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 					__a						( self    ,    _UpperCamelCase=0    ,    **_UpperCamelCase					)    ->				Any:
							lowerCAmelCase_      =     dict(self.forward_default_kwargs					)

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

										lowerCAmelCase_      =     self.get_scheduler_config(**__lowerCamelCase					)
										lowerCAmelCase_      =     scheduler_class(**__lowerCamelCase					)

										with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(__lowerCamelCase					)
													lowerCAmelCase_      =     scheduler_class.from_pretrained(__lowerCamelCase					)

										lowerCAmelCase_      =     scheduler.step_pred(
										    __lowerCamelCase    ,    __lowerCamelCase    ,    __lowerCamelCase    ,    generator=torch.manual_seed(0					)    ,    **__lowerCamelCase					).prev_sample
										lowerCAmelCase_      =     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"

										lowerCAmelCase_      =     scheduler.step_correct(__lowerCamelCase    ,    __lowerCamelCase    ,    generator=torch.manual_seed(0					)    ,    **__lowerCamelCase					).prev_sample
										lowerCAmelCase_      =     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    ,    _UpperCamelCase=0    ,    **_UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     dict(self.forward_default_kwargs					)
							kwargs.update(__lowerCamelCase					)

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

										lowerCAmelCase_      =     self.get_scheduler_config()
										lowerCAmelCase_      =     scheduler_class(**__lowerCamelCase					)

										with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(__lowerCamelCase					)
													lowerCAmelCase_      =     scheduler_class.from_pretrained(__lowerCamelCase					)

										lowerCAmelCase_      =     scheduler.step_pred(
										    __lowerCamelCase    ,    __lowerCamelCase    ,    __lowerCamelCase    ,    generator=torch.manual_seed(0					)    ,    **__lowerCamelCase					).prev_sample
										lowerCAmelCase_      =     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"

										lowerCAmelCase_      =     scheduler.step_correct(__lowerCamelCase    ,    __lowerCamelCase    ,    generator=torch.manual_seed(0					)    ,    **__lowerCamelCase					).prev_sample
										lowerCAmelCase_      =     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]:


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




				def 					__a						( self					)    ->				Dict:


							for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,    [1, 100, 1_000]					):
										self.check_over_configs(sigma_min=__lowerCamelCase    ,    sigma_max=__lowerCamelCase					)




				def 					__a						( self					)    ->				str:


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




				def 					__a						( self					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     dict(self.forward_default_kwargs					)

							lowerCAmelCase_      =     self.scheduler_classes[0]
							lowerCAmelCase_      =     self.get_scheduler_config()
							lowerCAmelCase_      =     scheduler_class(**__lowerCamelCase					)

							lowerCAmelCase_      =     3

							lowerCAmelCase_      =     self.dummy_model()
							lowerCAmelCase_      =     self.dummy_sample_deter

							scheduler.set_sigmas(__lowerCamelCase					)
							scheduler.set_timesteps(__lowerCamelCase					)
							lowerCAmelCase_      =     torch.manual_seed(0					)

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

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

										with torch.no_grad():
													lowerCAmelCase_      =     model(__lowerCamelCase    ,    __lowerCamelCase					)

										lowerCAmelCase_      =     scheduler.step_pred(__lowerCamelCase    ,    __lowerCamelCase    ,    __lowerCamelCase    ,    generator=__lowerCamelCase    ,    **__lowerCamelCase					)
										lowerCAmelCase_      =     output.prev_sample, output.prev_sample_mean

							lowerCAmelCase_      =     torch.sum(torch.abs(__lowerCamelCase					)					)
							lowerCAmelCase_      =     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 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     dict(self.forward_default_kwargs					)

							lowerCAmelCase_      =     kwargs.pop("num_inference_steps"    ,    __lowerCamelCase					)

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase_      =     self.get_scheduler_config()
										lowerCAmelCase_      =     scheduler_class(**__lowerCamelCase					)

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

										lowerCAmelCase_      =     scheduler.step_pred(__lowerCamelCase    ,    0    ,    __lowerCamelCase    ,    generator=torch.manual_seed(0					)    ,    **__lowerCamelCase					).prev_sample
										lowerCAmelCase_      =     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					)





class      _lowerCAmelCase :




				def __init__( self					)    ->				None:
							# numbermap keys are the number and its values are lists of indexes sorted
							# in ascending order
							lowerCAmelCase_      =     {}
							# indexmap keys are an index and it's values are the number at that index
							lowerCAmelCase_      =     {}




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_delete() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == item:
													array.pop(_UpperCamelCase					)
													return array
										elif array[mid] < item:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1
							raise ValueError(
							    "Either the item is not in the array or the array was unsorted"					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_insert() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == index:
													# If the item already exists in the array,
													# insert it after the existing item
													array.insert(mid + 1    ,    _UpperCamelCase					)
													return array
										elif array[mid] < index:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
							array.insert(_UpperCamelCase    ,    _UpperCamelCase					)
							return array




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				None:
							# Remove previous index
							if index in self.indexmap:
										lowerCAmelCase_      =     self.indexmap[index]
										if len(self.numbermap[n]					) == 1:
													del self.numbermap[n]
										else:
													lowerCAmelCase_      =     self.binary_search_delete(self.numbermap[n]    ,    _UpperCamelCase					)

        # Set new index
							lowerCAmelCase_      =     number

							# Number not seen before or empty so insert number value
							if number not in self.numbermap:
										lowerCAmelCase_      =     [index]

							# Here we need to perform a binary search insertion in order to insert
							# The item in the correct place
							else:
										lowerCAmelCase_      =     self.binary_search_insert(
										    self.numbermap[number]    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				int:
							return self.numbermap.get(_UpperCamelCase    ,    [-1]					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



