



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					int		      =		(DDIMParallelScheduler,)
       UpperCamelCase__						:					Optional[Any]		      =		(('''eta''', 0.0), ('''num_inference_steps''', 50))



       def 	_lowerCamelCase    (	self							:  List[str]  ,	**__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       {
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 	_lowerCamelCase    (	self							:  Dict  ,	**__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
           __a		 =       scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       10, 0.0

           __a		 =       self.dummy_model()
           __a		 =       self.dummy_sample_deter

           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
               __a		 =       model(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               __a		 =       scheduler.step(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE).prev_sample

           return sample



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           for timesteps in [100, 500, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__SCREAMING_SNAKE_CASE)

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config(steps_offset=1)
           __a		 =       scheduler_class(**__SCREAMING_SNAKE_CASE)
           scheduler.set_timesteps(5)
           assert torch.equal(scheduler.timesteps  ,	torch.LongTensor([801, 601, 401, 201, 1]))



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,	[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE  ,	beta_end=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__SCREAMING_SNAKE_CASE  ,	prediction_type=__SCREAMING_SNAKE_CASE  ,	sample_max_value=__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           for t in [1, 10, 49]:
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           for t, num_inference_steps in zip([1, 10, 50]  ,	[10, 50, 500]):
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE  ,	num_inference_steps=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           for t, eta in zip([1, 10, 49]  ,	[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE  ,	eta=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config()
           __a		 =       scheduler_class(**__SCREAMING_SNAKE_CASE)

           assert torch.sum(torch.abs(scheduler._get_variance(0  ,	0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(420  ,	400) - 0.1_47_71)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(980  ,	960) - 0.3_24_60)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(0  ,	0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487  ,	486) - 0.0_09_79)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999  ,	998) - 0.02)) < 1E-5



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config()
           __a		 =       scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       10, 0.0
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

           __a		 =       self.dummy_model()
           __a		 =       self.dummy_sample_deter
           __a		 =       self.dummy_sample_deter + 0.1
           __a		 =       self.dummy_sample_deter - 0.1

           __a		 =       samplea.shape[0]
           __a		 =       torch.stack([samplea, samplea, samplea]  ,	dim=0)
           __a		 =       torch.arange(__SCREAMING_SNAKE_CASE)[0:3, None].repeat(1  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       model(samples.flatten(0  ,	1)  ,	timesteps.flatten(0  ,	1))
           __a		 =       scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE  ,	timesteps.flatten(0  ,	1)  ,	samples.flatten(0  ,	1)  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 1_147.7_904) < 1E-2
           assert abs(result_mean.item() - 0.49_82) < 1E-3



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self.full_loop()

           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 172.0_067) < 1E-2
           assert abs(result_mean.item() - 0.22_39_67) < 1E-3



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       self.full_loop(prediction_type='''v_prediction''')

           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 52.53_02) < 1E-2
           assert abs(result_mean.item() - 0.06_84) < 1E-3



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,	beta_start=0.01)
           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 149.8_295) < 1E-2
           assert abs(result_mean.item() - 0.19_51) < 1E-3





       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,	beta_start=0.01)
           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 149.0_784) < 1E-2
           assert abs(result_mean.item() - 0.19_41) < 1E-3





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       ''''''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       [chr(i + 65       ) for i in range(26       )]
    # Remove duplicate characters from key
    __a		 =       remove_duplicates(key.upper()       )
    __a		 =       len(_UpperCAmelCase       )
    # First fill cipher with key characters
    __a		 =       {alphabet[i]: char for i, char in enumerate(_UpperCAmelCase       )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase       )     ,				26       ):
        __a		 =       alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __a		 =       alphabet[i - offset]
        __a		 =       char
    return cipher_alphabet





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return "".join(cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    ):
    __a		 =       input('''Enter message to encode or decode: '''       ).strip()
    __a		 =       input('''Enter keyword: '''       ).strip()
    __a		 =       input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
    try:
        __a		 =       {'''e''': encipher, '''d''': decipher}[option]
    except KeyError:
        raise KeyError('''invalid input option'''       )
    __a		 =       create_cipher_map(_UpperCAmelCase       )
    print(func(_UpperCAmelCase     ,				_UpperCAmelCase       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





