


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					return math.sqrt(sum(pow(a - b						,							2  ) for a, b in zip(_lowerCAmelCase						,							_lowerCAmelCase  )  )  )


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):




					if dataset.ndim != value_array.ndim:
										lowercase_  							=			(
										    "Wrong input data's dimensions... "
										    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
										)
										raise ValueError(_lowerCAmelCase  )

					try:
										if dataset.shape[1] != value_array.shape[1]:
															lowercase_  							=			(
															    "Wrong input data's shape... "
															    F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
															)
															raise ValueError(_lowerCAmelCase  )
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError("""Wrong shape"""  )

					if dataset.dtype != value_array.dtype:
										lowercase_  							=			(
										    "Input data have different datatype... "
										    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
										)
										raise TypeError(_lowerCAmelCase  )

					lowercase_  							=			[]

					for value in value_array:
										lowercase_  							=			euclidean(_lowerCAmelCase						,							dataset[0]  )
										lowercase_  							=			dataset[0].tolist()

										for dataset_value in dataset[1:]:
															lowercase_  							=			euclidean(_lowerCAmelCase						,							_lowerCAmelCase  )

															if dist > temp_dist:
																				lowercase_  							=			temp_dist
																				lowercase_  							=			dataset_value.tolist()

										answer.append([vector, dist]  )

					return answer


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					return np.dot(_lowerCAmelCase						,							_lowerCAmelCase  ) / (norm(_lowerCAmelCase  ) * norm(_lowerCAmelCase  ))


if __name__ == "__main__":
							import doctest

							doctest.testmod()






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   (UnCLIPScheduler,)

					def   UpperCAmelCase__       (     self       :   int							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """num_train_timesteps""": 1_000,
										    """variance_type""": """fixed_small_log""",
										    """clip_sample""": True,
										    """clip_sample_range""": 1.0,
										    """prediction_type""": """epsilon""",
										}

										config.update(**UpperCamelCase__       )
										return config

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																									continue

																				self.check_over_forward(time_step=UpperCamelCase__							,					prev_timestep=UpperCamelCase__       )

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






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""fixed_small_log"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000e-10       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1e-5

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""learned_range"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			0.5

										assert scheduler._get_variance(1							,					predicted_variance=UpperCamelCase__       ) - -10.1_712_790 < 1e-5
										assert scheduler._get_variance(487							,					predicted_variance=UpperCamelCase__       ) - -5.7_998_052 < 1e-5
										assert scheduler._get_variance(999							,					predicted_variance=UpperCamelCase__       ) - -0.0_010_011 < 1e-5

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 252.2_682_495       ) < 1e-2
										assert abs(result_mean.item() - 0.3_284_743       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										scheduler.set_timesteps(25       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															if i + 1 == timesteps.shape[0]:
																				lowercase_  							=			None
															else:
																				lowercase_  							=			timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(
															    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					prev_timestep=UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 258.2_044_983       ) < 1e-2
										assert abs(result_mean.item() - 0.3_362_038       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										pass







					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										pass


