import numpy as np

__snake_case         =     [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]







class       __snake_case :
 def __init__(	self    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=np.array(snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				np.ndarray:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Dict     	=np.where(letter == self.SQUARE    )
       UpperCAmelCase     :			Tuple     	=np.concatenate([indexa + 1, indexa + 1]    )
       return indexes
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				str:
       '''simple docstring'''





       return self.SQUARE[indexa - 1, indexa - 1]
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=message.lower()
       UpperCAmelCase     :			Any     	=message.replace('''j'''		, '''i'''    )

       UpperCAmelCase     :			List[Any]     	=''''''
       for letter_index in range(len(snake_case__    )    ):
             if message[letter_index] != " ":
                   UpperCAmelCase     :			List[Any]     	=self.letter_to_numbers(message[letter_index]    )
                   UpperCAmelCase     :			int     	=encoded_message + str(numbers[0]    ) + str(numbers[1]    )
             elif message[letter_index] == " ":
                   UpperCAmelCase     :			List[Any]     	=encoded_message + ''' '''

       return encoded_message






 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=message.replace(''' '''		, '''  '''    )
       UpperCAmelCase     :			Dict     	=''''''
       for numbers_index in range(int(len(snake_case__    ) / 2    )    ):
             if message[numbers_index * 2] != " ":
                   UpperCAmelCase     :			List[Any]     	=message[numbers_index * 2]
                   UpperCAmelCase     :			Union[str, Any]     	=message[numbers_index * 2 + 1]

                   UpperCAmelCase     :			Dict     	=self.numbers_to_letter(int(snake_case__    )		, int(snake_case__    )    )
                   UpperCAmelCase     :			int     	=decoded_message + letter
             elif message[numbers_index * 2] == " ":
                   UpperCAmelCase     :			List[str]     	=decoded_message + ''' '''

       return decoded_message

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Dict 	=   (VQDiffusionScheduler,)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	={
           '''num_vec_classes''': 4097,
           '''num_train_timesteps''': 100,
       }

       config.update(**snake_case__    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=4
       UpperCAmelCase     :			Optional[int]     	=8
       UpperCAmelCase     :			Optional[Any]     	=8

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

       return sample
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       assert False
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





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

       return model
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





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





       for t in [0, 50, 99]:
             self.check_over_forward(time_step=snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       pass
