




import os
from string import ascii_letters

__lowerCAmelCase		:    int	      =		ascii_letters + ' \t\n'




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      os.path.split(os.path.realpath(A       )       )
			a   =      {}
			with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
						for word in dictionary_file.read().split("\n"       ):
									a   =      None
			return english_words


__lowerCAmelCase		:    Union[str, Any]	      =		load_dictionary()




def       __magic_name__    (			A		: str       ):






			'''simple docstring'''







			a   =      message.upper()
			a   =      remove_non_letters(A       )
			a   =      message.split()
			a   =      len([word for word in possible_words if word in ENGLISH_WORDS]       )
			return float(A       ) / len(A       )




def       __magic_name__    (			A		: str       ):






			'''simple docstring'''







			return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )




def       __magic_name__    (			A		: str,  A		: int = 20,  A		: int = 85       ):






			'''simple docstring'''







			a   =      get_english_count(A       ) * 100 >= word_percentage
			a   =      len(remove_non_letters(A       )       )
			a   =      (float(A       ) / len(A       )) * 100
			a   =      message_letters_percentage >= letter_percentage
			return words_match and letters_match


if __name__ == "__main__":
		import doctest

		doctest.testmod()












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Dict										= (VQDiffusionScheduler,)





				def 							__UpperCAmelCase							(				self    :					Tuple ,				**__lowerCamelCase    :					Optional[int]							) ->					Optional[Any]:
							a   =      {
							    "num_vec_classes": 40_97,
							    "num_train_timesteps": 1_00,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Any							) ->					Union[str, Any]:
							a   =      4
							a   =      8
							a   =      8

							a   =      torch.randint(0 ,				__lowerCamelCase ,				(batch_size, height * width)							)

							return sample





				@property
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[Any]:
							assert False





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Optional[int]							) ->					int:
							def model(__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any ,				*__lowerCamelCase    :					Optional[Any]							):
										a   ,		a   =      sample.shape
										a   =      torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)							)
										a   =      F.log_softmax(logits.double() ,				dim=1							).float()
										return return_value

							return model





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Any:
							for timesteps in [2, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							for num_vec_classes in [5, 1_00, 10_00, 40_00]:
										self.check_over_configs(num_vec_classes=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[Any]:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[Any]:
							pass






