


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      (DEISMultistepScheduler,)
     _lowerCamelCase   =      (("""num_inference_steps""", 25),)


     def 	snake_case_  (	self  ,       **__A    ):
        __a					=  {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
        }

        config.update(**__A    )
        return config


     def 	snake_case_  (	self  ,       __A=0  ,       **__A    ):
        __a					=  dict(self.forward_default_kwargs    )
        __a					=  kwargs.pop("""num_inference_steps"""  ,       __A    )
        __a					=  self.dummy_sample
        __a					=  0.1 * sample
        __a					=  [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self  ,       __A=0  ,       **__A    ):
        __a					=  dict(self.forward_default_kwargs    )
        __a					=  kwargs.pop("""num_inference_steps"""  ,       __A    )
        __a					=  self.dummy_sample
        __a					=  0.1 * sample
        __a					=  [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           __a					=  self.get_scheduler_config()
           __a					=  scheduler_class(**__A    )
           scheduler.set_timesteps(__A    )

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

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

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

           __a					=  scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample
           __a					=  new_scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample

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


     def 	snake_case_  (	self  ,       __A=None  ,       **__A    ):
        if scheduler is None:
           __a					=  self.scheduler_classes[0]
           __a					=  self.get_scheduler_config(**__A    )
           __a					=  scheduler_class(**__A    )

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

        __a					=  10
        __a					=  self.dummy_model()
        __a					=  self.dummy_sample_deter
        scheduler.set_timesteps(__A    )

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

        return sample


     def 	snake_case_  (	self    ):
        __a					=  dict(self.forward_default_kwargs    )

        __a					=  kwargs.pop("""num_inference_steps"""  ,       __A    )

        for scheduler_class in self.scheduler_classes:
           __a					=  self.get_scheduler_config()
           __a					=  scheduler_class(**__A    )

           __a					=  self.dummy_sample
           __a					=  0.1 * sample

           if num_inference_steps is not None and hasattr(__A  ,       """set_timesteps"""    ):
              scheduler.set_timesteps(__A    )
           elif num_inference_steps is not None and not hasattr(__A  ,       """set_timesteps"""    ):
              __a					=  num_inference_steps

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

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

           __a					=  scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample
           __a					=  scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample

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


     def 	snake_case_  (	self    ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        __a					=  DEISMultistepScheduler(**self.get_scheduler_config()    )
        __a					=  self.full_loop(scheduler=__A    )
        __a					=  torch.mean(torch.abs(__A    )    )

        assert abs(result_mean.item() - 0.23916    ) < 1E-3

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

        __a					=  self.full_loop(scheduler=__A    )
        __a					=  torch.mean(torch.abs(__A    )    )

        assert abs(result_mean.item() - 0.23916    ) < 1E-3


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


     def 	snake_case_  (	self    ):
        self.check_over_configs(thresholding=__A    )
        for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
              for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        thresholding=__A  ,       prediction_type=__A  ,       sample_max_value=__A  ,       algorithm_type="""deis"""  ,       solver_order=__A  ,       solver_type=__A  ,       )


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


     def 	snake_case_  (	self    ):
        for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
              for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        solver_order=__A  ,       solver_type=__A  ,       prediction_type=__A  ,       algorithm_type=__A  ,       )
                    __a					=  self.full_loop(
                        solver_order=__A  ,       solver_type=__A  ,       prediction_type=__A  ,       algorithm_type=__A  ,       )
                    assert not torch.isnan(__A    ).any(), "Samples have nan numbers"


     def 	snake_case_  (	self    ):
        self.check_over_configs(lower_order_final=__A    )
        self.check_over_configs(lower_order_final=__A    )


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


     def 	snake_case_  (	self    ):
        __a					=  self.full_loop()
        __a					=  torch.mean(torch.abs(__A    )    )

        assert abs(result_mean.item() - 0.23916    ) < 1E-3


     def 	snake_case_  (	self    ):
        __a					=  self.full_loop(prediction_type="""v_prediction"""    )
        __a					=  torch.mean(torch.abs(__A    )    )

        assert abs(result_mean.item() - 0.091    ) < 1E-3


     def 	snake_case_  (	self    ):
        __a					=  self.scheduler_classes[0]
        __a					=  self.get_scheduler_config(thresholding=__A  ,       dynamic_thresholding_ratio=0    )
        __a					=  scheduler_class(**__A    )

        __a					=  10
        __a					=  self.dummy_model()
        __a					=  self.dummy_sample_deter.half()
        scheduler.set_timesteps(__A    )

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

        assert sample.dtype == torch.floataa











def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   str:





    """simple docstring"""

    __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       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   dict[str, str]:





    """simple docstring"""

    __A       =						[chr(i + 6_5     ) for i in range(2_6     )]
    # Remove duplicate characters from key
    __A       =						remove_duplicates(key.upper()     )
    __A       =						len(__lowercase     )
    # First fill cipher with key characters
    __A       =						{alphabet[i]: char for i, char in enumerate(__lowercase     )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(__lowercase     )      ,					2_6     ):
        __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       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    return "".join(cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    __A       =						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





    """simple docstring"""

    __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(__lowercase     )
    print(func(__lowercase      ,					__lowercase     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




