





"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Dict		   =    (LMSDiscreteScheduler,)
  SCREAMING_SNAKE_CASE__							:Optional[int]		   =    10

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      **__a      :				Optional[int]		)						-> Union[str, Any]:
        _UpperCamelCase					:		str  =				{
            "num_train_timesteps": 1100,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__a		)
        return config

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> Any:
        for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Optional[int]:
        for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,      [0.00_02, 0.0_02, 0.02]		):
              self.check_over_configs(beta_start=__a    ,      beta_end=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> str:
        for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> int:
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> Tuple:
        for t in [0, 500, 800]:
              self.check_over_forward(time_step=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> List[Any]:
        _UpperCamelCase					:		int  =				self.scheduler_classes[0]
        _UpperCamelCase					:		List[str]  =				self.get_scheduler_config()
        _UpperCamelCase					:		Optional[Any]  =				scheduler_class(**__a		)

        scheduler.set_timesteps(self.num_inference_steps		)

        _UpperCamelCase					:		Dict  =				self.dummy_model()
        _UpperCamelCase					:		str  =				self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps		):
              _UpperCamelCase					:		str  =				scheduler.scale_model_input(__a    ,      __a		)

              _UpperCamelCase					:		List[Any]  =				model(__a    ,      __a		)

              _UpperCamelCase					:		Union[str, Any]  =				scheduler.step(__a    ,      __a    ,      __a		)
              _UpperCamelCase					:		Optional[int]  =				output.prev_sample

        _UpperCamelCase					:		Dict  =				torch.sum(torch.abs(__a		)		)
        _UpperCamelCase					:		List[str]  =				torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 10_06.3_88		) < 1e-2
        assert abs(result_mean.item() - 1.31		) < 1e-3

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> List[Any]:
        _UpperCamelCase					:		int  =				self.scheduler_classes[0]
        _UpperCamelCase					:		Dict  =				self.get_scheduler_config(prediction_type="v_prediction"		)
        _UpperCamelCase					:		Union[str, Any]  =				scheduler_class(**__a		)

        scheduler.set_timesteps(self.num_inference_steps		)

        _UpperCamelCase					:		List[Any]  =				self.dummy_model()
        _UpperCamelCase					:		Tuple  =				self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps		):
              _UpperCamelCase					:		List[str]  =				scheduler.scale_model_input(__a    ,      __a		)

              _UpperCamelCase					:		int  =				model(__a    ,      __a		)

              _UpperCamelCase					:		Tuple  =				scheduler.step(__a    ,      __a    ,      __a		)
              _UpperCamelCase					:		Optional[int]  =				output.prev_sample

        _UpperCamelCase					:		Tuple  =				torch.sum(torch.abs(__a		)		)
        _UpperCamelCase					:		Any  =				torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 0.00_17		) < 1e-2
        assert abs(result_mean.item() - 2.2_6_7_6e-0_6		) < 1e-3

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Any:
        _UpperCamelCase					:		List[str]  =				self.scheduler_classes[0]
        _UpperCamelCase					:		int  =				self.get_scheduler_config()
        _UpperCamelCase					:		int  =				scheduler_class(**__a		)

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

        _UpperCamelCase					:		List[Any]  =				self.dummy_model()
        _UpperCamelCase					:		Dict  =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _UpperCamelCase					:		List[Any]  =				sample.to(__a		)

        for i, t in enumerate(scheduler.timesteps		):
              _UpperCamelCase					:		Union[str, Any]  =				scheduler.scale_model_input(__a    ,      __a		)

              _UpperCamelCase					:		List[Any]  =				model(__a    ,      __a		)

              _UpperCamelCase					:		Optional[int]  =				scheduler.step(__a    ,      __a    ,      __a		)
              _UpperCamelCase					:		List[Any]  =				output.prev_sample

        _UpperCamelCase					:		List[Any]  =				torch.sum(torch.abs(__a		)		)
        _UpperCamelCase					:		Optional[Any]  =				torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 10_06.3_88		) < 1e-2
        assert abs(result_mean.item() - 1.31		) < 1e-3


  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Any:
        _UpperCamelCase					:		Tuple  =				self.scheduler_classes[0]
        _UpperCamelCase					:		List[Any]  =				self.get_scheduler_config()
        _UpperCamelCase					:		Union[str, Any]  =				scheduler_class(**__a    ,      use_karras_sigmas=__a		)

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

        _UpperCamelCase					:		str  =				self.dummy_model()
        _UpperCamelCase					:		Optional[Any]  =				self.dummy_sample_deter.to(__a		) * scheduler.init_noise_sigma
        _UpperCamelCase					:		List[str]  =				sample.to(__a		)

        for t in scheduler.timesteps:
              _UpperCamelCase					:		Union[str, Any]  =				scheduler.scale_model_input(__a    ,      __a		)

              _UpperCamelCase					:		Dict  =				model(__a    ,      __a		)

              _UpperCamelCase					:		Tuple  =				scheduler.step(__a    ,      __a    ,      __a		)
              _UpperCamelCase					:		Optional[int]  =				output.prev_sample

        _UpperCamelCase					:		str  =				torch.sum(torch.abs(__a		)		)
        _UpperCamelCase					:		str  =				torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 38_12.99_27		) < 2e-2
        assert abs(result_mean.item() - 4.96_48		) < 1e-3







"""simple docstring"""


from __future__ import annotations




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      assert n > 1 and a > 0 and greatest_common_divisor(lowercase_						,lowercase_	) == 1
      ((_UpperCamelCase),     (_UpperCamelCase),     (_UpperCamelCase))					:		int  =				extended_gcd(lowercase_						,lowercase_	)  # Implemented below
      _UpperCamelCase					:		Any  =				(b * s) % n
      return x




def     lowercase__			(    lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      ((_UpperCamelCase),     (_UpperCamelCase))					:		List[Any]  =				extended_euclid(lowercase_						,lowercase_	)  # Implemented below
      if b < 0:
            _UpperCamelCase					:		Any  =				(b % n + n) % n
      return b




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      _UpperCamelCase					:		Union[str, Any]  =				invert_modulo(lowercase_						,lowercase_	)
      _UpperCamelCase					:		Any  =				(b * s) % n
      return x




def     lowercase__			(    lowercase_						,lowercase_	)    ->						tuple[int, int, int]:




      """simple docstring"""

      assert a >= 0 and b >= 0

      if b == 0:
            _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		str  =				a, 1, 0
      else:
            ((_UpperCamelCase),     (_UpperCamelCase),     (_UpperCamelCase))					:		str  =				extended_gcd(lowercase_						,a % b	)
            _UpperCamelCase					:		Dict  =				q
            _UpperCamelCase					:		Tuple  =				p - q * (a // b)

      assert a % d == 0 and b % d == 0
      assert d == a * x + b * y

      return (d, x, y)




def     lowercase__			(    lowercase_						,lowercase_	)    ->						tuple[int, int]:




      """simple docstring"""

      if b == 0:
            return (1, 0)
      ((_UpperCamelCase),     (_UpperCamelCase))					:		Optional[Any]  =				extended_euclid(lowercase_						,a % b	)
      _UpperCamelCase					:		Dict  =				a // b
      return (y, x - k * y)




def     lowercase__			(    lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      if a < b:
            _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				b, a

      while a % b != 0:
            _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				b, a % b

      return b


if __name__ == "__main__":
  from doctest import testmod

  testmod(name="modular_division", verbose=True)
  testmod(name="modular_division2", verbose=True)
  testmod(name="invert_modulo", verbose=True)
  testmod(name="extended_gcd", verbose=True)
  testmod(name="extended_euclid", verbose=True)
  testmod(name="greatest_common_divisor", verbose=True)
