
def        lowerCamelCase__    (    __lowerCAmelCase  :  str    ):




			"""simple docstring"""


			lowerCAmelCase_      =     word.upper()
			lowerCAmelCase_      =     0
			lowerCAmelCase_      =     {}
			lowerCAmelCase_      =     []

			for letter in word:
						if letter not in letter_nums:
									lowerCAmelCase_      =     str(__lowerCAmelCase    )
									next_num += 1
						word_pattern.append(letter_nums[letter]    )
			return ".".join(__lowerCAmelCase    )


if __name__ == "__main__":
			import pprint
			import time

			_A       =					time.time()
			with open("dictionary.txt") as in_file:
						_A       =					in_file.read().splitlines()

			_A       =					{}
			for word in word_list:
						_A       =					get_word_pattern(word)
						if pattern in all_patterns:
									all_patterns[pattern].append(word)
						else:
									_A       =					[word]

			with open("word_patterns.txt", "w") as out_file:
						out_file.write(pprint.pformat(all_patterns))

			_A       =					round(time.time() - start_time, 2)
			print(f"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
			# Done!  9,581 word patterns found in 0.58 seconds.





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




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



