






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      __snake_case  						=						(LMSDiscreteScheduler,)
      __snake_case  						=						10







      def    __lowerCAmelCase      (   self   :		Any				,      **__UpperCAmelCase   :		Optional[Any]						)					->Dict:




       """simple docstring"""


       a							=   {
           '''num_train_timesteps''': 1_100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__UpperCAmelCase						)
       return config







      def    __lowerCAmelCase      (   self   :		Optional[int]						)					->Any:




       """simple docstring"""


       for timesteps in [10, 50, 100, 1_000]:
        self.check_over_configs(num_train_timesteps=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		int						)					->List[Any]:




       """simple docstring"""


       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,      [0.0002, 0.002, 0.02]						):
        self.check_over_configs(beta_start=__UpperCAmelCase				,      beta_end=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		Any						)					->Union[str, Any]:




       """simple docstring"""


       for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		Tuple						)					->List[Any]:




       """simple docstring"""


       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		Tuple						)					->Any:




       """simple docstring"""


       for t in [0, 500, 800]:
        self.check_over_forward(time_step=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[Any]						)					->List[str]:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config()
       a							=   scheduler_class(**__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(scheduler.timesteps						):
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

       a							=   torch.sum(torch.abs(__UpperCAmelCase						)						)
       a							=   torch.mean(torch.abs(__UpperCAmelCase						)						)

       assert abs(result_sum.item() - 1006.388						) < 1e-2
       assert abs(result_mean.item() - 1.31						) < 1e-3







      def    __lowerCAmelCase      (   self   :		Optional[Any]						)					->Any:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config(prediction_type='''v_prediction'''						)
       a							=   scheduler_class(**__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(scheduler.timesteps						):
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

       a							=   torch.sum(torch.abs(__UpperCAmelCase						)						)
       a							=   torch.mean(torch.abs(__UpperCAmelCase						)						)

       assert abs(result_sum.item() - 0.0017						) < 1e-2
       assert abs(result_mean.item() - 2.2_6_7_6e-0_6						) < 1e-3







      def    __lowerCAmelCase      (   self   :		Optional[Any]						)					->str:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config()
       a							=   scheduler_class(**__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps				,      device=__UpperCAmelCase						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       a							=   sample.to(__UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

       a							=   torch.sum(torch.abs(__UpperCAmelCase						)						)
       a							=   torch.mean(torch.abs(__UpperCAmelCase						)						)

       assert abs(result_sum.item() - 1006.388						) < 1e-2
       assert abs(result_mean.item() - 1.31						) < 1e-3







      def    __lowerCAmelCase      (   self   :		Dict						)					->Tuple:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config()
       a							=   scheduler_class(**__UpperCAmelCase				,      use_karras_sigmas=__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps				,      device=__UpperCAmelCase						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter.to(__UpperCAmelCase						) * scheduler.init_noise_sigma
       a							=   sample.to(__UpperCAmelCase						)

       for t in scheduler.timesteps:
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

       a							=   torch.sum(torch.abs(__UpperCAmelCase						)						)
       a							=   torch.mean(torch.abs(__UpperCAmelCase						)						)

       assert abs(result_sum.item() - 3812.9927						) < 2e-2
       assert abs(result_mean.item() - 4.9648						) < 1e-3










import numpy as np

UpperCAmelCase__            =	[
    ["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 	lowercase_      :






      '''simple docstring'''




      def __init__(   self   :		Optional[Any]						)					->None:




       """simple docstring"""


       a							=   np.array(__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		str						)					->np.ndarray:




       """simple docstring"""


       a						,  a							=   np.where(letter == self.SQUARE						)
       a							=   np.concatenate([indexa + 1, indexa + 1]						)
       return indexes







      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		int				,      __UpperCAmelCase   :		int						)					->str:




       """simple docstring"""


       return self.SQUARE[indexa - 1, indexa - 1]







      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.lower()
       a							=   message.replace('''j'''				,      '''i'''						)

       a							=   ''''''
       for letter_index in range(len(__UpperCAmelCase						)						):
        if message[letter_index] != " ":
         a							=   self.letter_to_numbers(message[letter_index]						)
         a							=   encoded_message + str(numbers[0]						) + str(numbers[1]						)
        elif message[letter_index] == " ":
         a							=   encoded_message + ''' '''

       return encoded_message







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.replace(''' '''				,      '''  '''						)
       a							=   ''''''
       for numbers_index in range(int(len(__UpperCAmelCase						) / 2						)						):
        if message[numbers_index * 2] != " ":
         a							=   message[numbers_index * 2]
         a							=   message[numbers_index * 2 + 1]

         a							=   self.numbers_to_letter(int(__UpperCAmelCase						)				,      int(__UpperCAmelCase						)						)
         a							=   decoded_message + letter
        elif message[numbers_index * 2] == " ":
         a							=   decoded_message + ''' '''

       return decoded_message


