





def 			__UpperCamelCase				(			lowercase__	:       str		)	->      str:
    '''simple docstring'''





    lowerCAmelCase_					:	int						=   word.upper()
    lowerCAmelCase_					:	List[str]						=   0
    lowerCAmelCase_					:	Any						=   {}
    lowerCAmelCase_					:	Optional[int]						=   []

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


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

  __UpperCAmelCase						=      time.time()
  with open('dictionary.txt') as in_file:
    __UpperCAmelCase						=      in_file.read().splitlines()

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

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

  __UpperCAmelCase						=      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 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


