





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 			__UpperCamelCase    (							A__    ):
     __A	:  List[str]							  =   (UniPCMultistepScheduler,)
     __A	:  List[str]							  =   (("""num_inference_steps""", 25),)





     def 		UpperCamelCase(     self				,							**_UpperCamelCase					):
        _UpperCAmelCase           =   {
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''solver_type''': '''bh2''',
        }

        config.update(**_UpperCamelCase					)
        return config





     def 		UpperCamelCase(     self				,							_UpperCamelCase=0				,							**_UpperCamelCase					):
        _UpperCAmelCase           =   dict(self.forward_default_kwargs					)
        _UpperCAmelCase           =   kwargs.pop('''num_inference_steps'''				,							_UpperCamelCase					)
        _UpperCAmelCase           =   self.dummy_sample
        _UpperCAmelCase           =   0.1 * sample
        _UpperCAmelCase           =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           _UpperCAmelCase           =   self.get_scheduler_config(**_UpperCamelCase					)
           _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)
           scheduler.set_timesteps(_UpperCamelCase					)
           # copy over dummy past residuals
           _UpperCAmelCase           =   dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_UpperCamelCase					)
              _UpperCAmelCase           =   scheduler_class.from_pretrained(_UpperCamelCase					)
              new_scheduler.set_timesteps(_UpperCamelCase					)
              # copy over dummy past residuals
              _UpperCAmelCase           =   dummy_past_residuals[: new_scheduler.config.solver_order]

           _UpperCAmelCase       ,      _UpperCAmelCase           =   sample, sample
           for t in range(_UpperCamelCase				,							time_step + scheduler.config.solver_order + 1					):
              _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					).prev_sample
              _UpperCAmelCase           =   new_scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"





     def 		UpperCamelCase(     self				,							_UpperCamelCase=0				,							**_UpperCamelCase					):
        _UpperCAmelCase           =   dict(self.forward_default_kwargs					)
        _UpperCAmelCase           =   kwargs.pop('''num_inference_steps'''				,							_UpperCamelCase					)
        _UpperCAmelCase           =   self.dummy_sample
        _UpperCAmelCase           =   0.1 * sample
        _UpperCAmelCase           =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           _UpperCAmelCase           =   self.get_scheduler_config()
           _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)
           scheduler.set_timesteps(_UpperCamelCase					)

           # copy over dummy past residuals (must be after setting timesteps)
           _UpperCAmelCase           =   dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_UpperCamelCase					)
              _UpperCAmelCase           =   scheduler_class.from_pretrained(_UpperCamelCase					)
              # copy over dummy past residuals
              new_scheduler.set_timesteps(_UpperCamelCase					)

              # copy over dummy past residual (must be after setting timesteps)
              _UpperCAmelCase           =   dummy_past_residuals[: new_scheduler.config.solver_order]

           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					).prev_sample
           _UpperCAmelCase           =   new_scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					).prev_sample

           assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"





     def 		UpperCamelCase(     self				,							_UpperCamelCase=None				,							**_UpperCamelCase					):
        if scheduler is None:
           _UpperCAmelCase           =   self.scheduler_classes[0]
           _UpperCAmelCase           =   self.get_scheduler_config(**_UpperCamelCase					)
           _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config(**_UpperCamelCase					)
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   10
        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter
        scheduler.set_timesteps(_UpperCamelCase					)

        for i, t in enumerate(scheduler.timesteps					):
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					)
           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					).prev_sample

        return sample





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   dict(self.forward_default_kwargs					)

        _UpperCAmelCase           =   kwargs.pop('''num_inference_steps'''				,							_UpperCamelCase					)

        for scheduler_class in self.scheduler_classes:
           _UpperCAmelCase           =   self.get_scheduler_config()
           _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

           _UpperCAmelCase           =   self.dummy_sample
           _UpperCAmelCase           =   0.1 * sample

           if num_inference_steps is not None and hasattr(_UpperCamelCase				,							'''set_timesteps'''					):
              scheduler.set_timesteps(_UpperCamelCase					)
           elif num_inference_steps is not None and not hasattr(_UpperCamelCase				,							'''set_timesteps'''					):
              _UpperCAmelCase           =   num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           _UpperCAmelCase           =   [residual + 0.2, residual + 0.15, residual + 0.10]
           _UpperCAmelCase           =   dummy_past_residuals[: scheduler.config.solver_order]

           _UpperCAmelCase           =   scheduler.timesteps[5]
           _UpperCAmelCase           =   scheduler.timesteps[6]

           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					).prev_sample
           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					).prev_sample

           self.assertEqual(output_a.shape				,							sample.shape					)
           self.assertEqual(output_a.shape				,							output_a.shape					)





     def 		UpperCamelCase(     self					):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        _UpperCAmelCase           =   UniPCMultistepScheduler(**self.get_scheduler_config()					)
        _UpperCAmelCase           =   self.full_loop(scheduler=_UpperCamelCase					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_mean.item() - 0.2464					) < 1e-3

        _UpperCAmelCase           =   DPMSolverSinglestepScheduler.from_config(scheduler.config					)
        _UpperCAmelCase           =   DEISMultistepScheduler.from_config(scheduler.config					)
        _UpperCAmelCase           =   DPMSolverMultistepScheduler.from_config(scheduler.config					)
        _UpperCAmelCase           =   UniPCMultistepScheduler.from_config(scheduler.config					)

        _UpperCAmelCase           =   self.full_loop(scheduler=_UpperCamelCase					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_mean.item() - 0.2464					) < 1e-3





     def 		UpperCamelCase(     self					):
        for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        self.check_over_configs(thresholding=_UpperCamelCase					)
        for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        thresholding=_UpperCamelCase				,							prediction_type=_UpperCamelCase				,							sample_max_value=_UpperCamelCase				,							solver_order=_UpperCamelCase				,							solver_type=_UpperCamelCase				,							)





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





     def 		UpperCamelCase(     self					):
        for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=_UpperCamelCase				,							solver_type=_UpperCamelCase				,							prediction_type=_UpperCamelCase				,							)
                 _UpperCAmelCase           =   self.full_loop(
                     solver_order=_UpperCamelCase				,							solver_type=_UpperCamelCase				,							prediction_type=_UpperCamelCase				,							)
                 assert not torch.isnan(_UpperCamelCase					).any(), "Samples have nan numbers"





     def 		UpperCamelCase(     self					):
        self.check_over_configs(lower_order_final=_UpperCamelCase					)
        self.check_over_configs(lower_order_final=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=_UpperCamelCase				,							time_step=0					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.full_loop()
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_mean.item() - 0.2464					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.full_loop(prediction_type='''v_prediction'''					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_mean.item() - 0.1014					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config(thresholding=_UpperCamelCase				,							dynamic_thresholding_ratio=0					)
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   10
        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter.half()
        scheduler.set_timesteps(_UpperCamelCase					)

        for i, t in enumerate(scheduler.timesteps					):
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					)
           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					).prev_sample

        assert sample.dtype == torch.floataa





     def 		UpperCamelCase(     self				,							**_UpperCamelCase					):
        for scheduler_class in self.scheduler_classes:
           _UpperCAmelCase           =   self.get_scheduler_config(**_UpperCamelCase					)
           _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
           assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps






UpperCAmelCase_           =	{
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_           =	{value: key for key, value in MORSE_CODE_DICT.items()}
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  str:






   """simple docstring"""







   return " ".join(MORSE_CODE_DICT[char] for char in message.upper()						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  str:






   """simple docstring"""







   return "".join(REVERSE_DICT[char] for char in message.split()						)
def      A__       (    )  ->  None:






   """simple docstring"""







   _UpperCAmelCase           =   '''Morse code here!'''
   print(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   encrypt(SCREAMING_SNAKE_CASE_						)
   print(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   decrypt(SCREAMING_SNAKE_CASE_						)
   print(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
   main()