






"""simple docstring"""







import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					(VQDiffusionScheduler,)




						def 		UpperCAmelCase_       (   self       ,  **snake_case__			):
												'''simple docstring'''
												lowercase__				:      int=       {
												    "num_vec_classes": 4097,
												    "num_train_timesteps": 100,
												}

												config.update(**snake_case__			)
												return config




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       4
												lowercase__				:      str=       8
												lowercase__				:      Optional[int]=       8

												lowercase__				:      int=       torch.randint(0       ,  snake_case__       ,  (batch_size, height * width)			)

												return sample




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												assert False




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												def model(snake_case__       ,  snake_case__       ,  *snake_case__			):
																		lowercase__,	lowercase__				:      List[str]=       sample.shape
																		lowercase__				:      List[str]=       torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
																		lowercase__				:      Tuple=       F.log_softmax(logits.double()       ,  dim=1			).float()
																		return return_value

												return model




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												for timesteps in [2, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												for num_vec_classes in [5, 100, 1000, 4000]:
																		self.check_over_configs(num_vec_classes=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												for t in [0, 50, 99]:
																		self.check_over_forward(time_step=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass














"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






