






"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Dict							=					(DDIMScheduler,)
   A_		:      Tuple							=					(("eta", 0.0), ("num_inference_steps", 50))


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

     '''simple docstring'''
     __A		     =					{
         '''num_train_timesteps''': 10_00,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''clip_sample''': True,
     }

     config.update(**_lowerCamelCase    )
     return config


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

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config(**_lowerCamelCase    )
     __A		     =					scheduler_class(**_lowerCamelCase    )

     __A     ,       __A		     =					10, 0.0

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter

     scheduler.set_timesteps(_lowerCamelCase    )

     for t in scheduler.timesteps:
       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )
       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    ).prev_sample

     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     for timesteps in [1_00, 5_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=_lowerCamelCase    )

     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config(steps_offset=1    )
     __A		     =					scheduler_class(**_lowerCamelCase    )
     scheduler.set_timesteps(5    )
     assert torch.equal(scheduler.timesteps,			torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''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=_lowerCamelCase,			beta_end=_lowerCamelCase    )


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

     '''simple docstring'''
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     for t in [1, 10, 49]:
       self.check_over_forward(time_step=_lowerCamelCase    )


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

     '''simple docstring'''
     for t, num_inference_steps in zip([1, 10, 50],			[10, 50, 5_00]    ):
       self.check_over_forward(time_step=_lowerCamelCase,			num_inference_steps=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     for t, eta in zip([1, 10, 49],			[0.0, 0.5, 1.0]    ):
       self.check_over_forward(time_step=_lowerCamelCase,			eta=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config()
     __A		     =					scheduler_class(**_lowerCamelCase    )

     assert torch.sum(torch.abs(scheduler._get_variance(0,			0    ) - 0.0    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_20,			4_00    ) - 0.1_47_71    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80,			9_60    ) - 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(4_87,			4_86    ) - 0.0_09_79    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99,			9_98    ) - 0.02    )    ) < 1e-5


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.full_loop()

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
     assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.full_loop(prediction_type='''v_prediction'''    )

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     # We specify different beta, so that the first alpha is 0.99
     __A		     =					self.full_loop(set_alpha_to_one=_lowerCamelCase,			beta_start=0.01    )
     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
     assert abs(result_mean.item() - 0.19_51    ) < 1e-3






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     # We specify different beta, so that the first alpha is 0.99
     __A		     =					self.full_loop(set_alpha_to_one=_lowerCamelCase,			beta_start=0.01    )
     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
     assert abs(result_mean.item() - 0.19_41    ) < 1e-3












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       lowercase_								=	[
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




