



"""simple docstring"""




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					(LMSDiscreteScheduler,)
   _lowerCamelCase       				=					10


   def 							lowercase					(				self   :				Optional[int],  **lowerCAmelCase   :				Any      )     ->							Dict:
         lowercase	:   Dict											= {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase      )
         return config


   def 							lowercase					(				self   :				Optional[Any]      )     ->							Dict:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase      )


   def 							lowercase					(				self   :				List[str]      )     ->							Dict:
         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=lowerCAmelCase,  beta_end=lowerCAmelCase      )


   def 							lowercase					(				self   :				List[Any]      )     ->							Tuple:
         for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase      )


   def 							lowercase					(				self   :				Union[str, Any]      )     ->							List[Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCAmelCase      )


   def 							lowercase					(				self   :				List[Any]      )     ->							Optional[Any]:
         for t in [0, 500, 800]:
               self.check_over_forward(time_step=lowerCAmelCase      )


   def 							lowercase					(				self   :				Optional[int]      )     ->							Any:
         lowercase	:   List[Any]											= self.scheduler_classes[0]
         lowercase	:   str											= self.get_scheduler_config()
         lowercase	:   Tuple											= scheduler_class(**lowerCAmelCase      )

         scheduler.set_timesteps(self.num_inference_steps      )

         lowercase	:   Union[str, Any]											= self.dummy_model()
         lowercase	:   List[str]											= self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps      ):
               lowercase	:   str											= scheduler.scale_model_input(lowerCAmelCase,  lowerCAmelCase      )

               lowercase	:   Any											= model(lowerCAmelCase,  lowerCAmelCase      )

               lowercase	:   Dict											= scheduler.step(lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   Optional[int]											= output.prev_sample

         lowercase	:   str											= torch.sum(torch.abs(lowerCAmelCase      )      )
         lowercase	:   Dict											= torch.mean(torch.abs(lowerCAmelCase      )      )

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


   def 							lowercase					(				self   :				List[Any]      )     ->							Any:
         lowercase	:   List[str]											= self.scheduler_classes[0]
         lowercase	:   List[str]											= self.get_scheduler_config(prediction_type='v_prediction'      )
         lowercase	:   List[str]											= scheduler_class(**lowerCAmelCase      )

         scheduler.set_timesteps(self.num_inference_steps      )

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

         for i, t in enumerate(scheduler.timesteps      ):
               lowercase	:   str											= scheduler.scale_model_input(lowerCAmelCase,  lowerCAmelCase      )

               lowercase	:   Union[str, Any]											= model(lowerCAmelCase,  lowerCAmelCase      )

               lowercase	:   Optional[int]											= scheduler.step(lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   Optional[int]											= output.prev_sample

         lowercase	:   List[Any]											= torch.sum(torch.abs(lowerCAmelCase      )      )
         lowercase	:   Any											= torch.mean(torch.abs(lowerCAmelCase      )      )

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


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

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

         lowercase	:   List[Any]											= self.dummy_model()
         lowercase	:   Union[str, Any]											= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         lowercase	:   int											= sample.to(lowerCAmelCase      )

         for i, t in enumerate(scheduler.timesteps      ):
               lowercase	:   Any											= scheduler.scale_model_input(lowerCAmelCase,  lowerCAmelCase      )

               lowercase	:   Any											= model(lowerCAmelCase,  lowerCAmelCase      )

               lowercase	:   Union[str, Any]											= scheduler.step(lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   Any											= output.prev_sample

         lowercase	:   Union[str, Any]											= torch.sum(torch.abs(lowerCAmelCase      )      )
         lowercase	:   Optional[int]											= torch.mean(torch.abs(lowerCAmelCase      )      )

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






   def 							lowercase					(				self   :				Tuple      )     ->							int:
         lowercase	:   Dict											= self.scheduler_classes[0]
         lowercase	:   Optional[int]											= self.get_scheduler_config()
         lowercase	:   str											= scheduler_class(**lowerCAmelCase,  use_karras_sigmas=lowerCAmelCase      )

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

         lowercase	:   List[str]											= self.dummy_model()
         lowercase	:   Tuple											= self.dummy_sample_deter.to(lowerCAmelCase      ) * scheduler.init_noise_sigma
         lowercase	:   Tuple											= sample.to(lowerCAmelCase      )

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

               lowercase	:   Tuple											= model(lowerCAmelCase,  lowerCAmelCase      )

               lowercase	:   Any											= scheduler.step(lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   List[Any]											= output.prev_sample

         lowercase	:   Optional[int]											= torch.sum(torch.abs(lowerCAmelCase      )      )
         lowercase	:   Any											= torch.mean(torch.abs(lowerCAmelCase      )      )

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





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase:					Optional[Any]     				=	{
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					Union[str, Any]     				=	['CodeGenTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					str     				=	[
         'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CodeGenForCausalLM',
         'CodeGenModel',
         'CodeGenPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
     from .tokenization_codegen import CodeGenTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_codegen_fast import CodeGenTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_codegen import (
              CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
              CodeGenForCausalLM,
              CodeGenModel,
              CodeGenPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase:					int     				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
