


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  		=			{
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ProphetNetDecoder""",
					    """ProphetNetEncoder""",
					    """ProphetNetForCausalLM""",
					    """ProphetNetForConditionalGeneration""",
					    """ProphetNetModel""",
					    """ProphetNetPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
					from .tokenization_prophetnet import ProphetNetTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_prophetnet import (
										    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ProphetNetDecoder,
										    ProphetNetEncoder,
										    ProphetNetForCausalLM,
										    ProphetNetForConditionalGeneration,
										    ProphetNetModel,
										    ProphetNetPreTrainedModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    (LMSDiscreteScheduler,)
     UpperCAmelCase_		:      Any    						=    10


     def   UpperCAmelCase_			(						self					:	str	,     **lowercase_					:	List[str]			)		->      str:
      UpperCAmelCase			:      Optional[Any]		   =					{
          'num_train_timesteps': 1_100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**lowercase_			)
      return config


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      Tuple:
      for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=lowercase_			)


     def   UpperCAmelCase_			(						self					:	int			)		->      Tuple:
      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]			):
       self.check_over_configs(beta_start=lowercase_	,     beta_end=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      for t in [0, 500, 800]:
       self.check_over_forward(time_step=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Tuple		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Any		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

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

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Optional[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Union[str, Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Optional[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Optional[Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3


     def   UpperCAmelCase_			(						self					:	str			)		->      Dict:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Dict		   =					self.get_scheduler_config(prediction_type='v_prediction'			)
      UpperCAmelCase			:      Optional[int]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      Optional[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      List[str]		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      List[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 0.0017			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Dict:
      UpperCAmelCase			:      Dict		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Optional[int]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      List[str]		   =					scheduler_class(**lowercase_			)

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

      UpperCAmelCase			:      Tuple		   =					self.dummy_model()
      UpperCAmelCase			:      Tuple		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase			:      Any		   =					sample.to(lowercase_			)

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Dict		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Tuple		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[Any]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Union[str, Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3




     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Union[str, Any]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Optional[Any]		   =					scheduler_class(**lowercase_	,     use_karras_sigmas=lowercase_			)

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

      UpperCAmelCase			:      List[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      Dict		   =					self.dummy_sample_deter.to(lowercase_			) * scheduler.init_noise_sigma
      UpperCAmelCase			:      Union[str, Any]		   =					sample.to(lowercase_			)

      for t in scheduler.timesteps:
       UpperCAmelCase			:      Optional[int]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[str]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[Any]		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      Any		   =					output.prev_sample

      UpperCAmelCase			:      List[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 3812.9927			) < 2E-2
      assert abs(result_mean.item() - 4.9648			) < 1E-3




