


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     Tuple								=						(EulerAncestralDiscreteScheduler,)
      a__   :     Optional[int]								=						10






      def 	__lowercase(     self					:   int,		**__lowerCamelCase					:   str   )  -> str:
           UpperCamelCase__							:  Union[str, Any]								=     {
               '''num_train_timesteps''': 11_00,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**__lowerCamelCase   )
           return config






      def 	__lowercase(     self					:   int   )  -> Optional[int]:
           for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=__lowerCamelCase   )






      def 	__lowercase(     self					:   List[str]   )  -> int:
           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]   )  -> Union[str, Any]:
           for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=__lowerCamelCase   )






      def 	__lowercase(     self					:   List[str]   )  -> Tuple:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=__lowerCamelCase   )






      def 	__lowercase(     self					:   Tuple   )  -> int:
           UpperCamelCase__							:  Optional[Any]								=     self.scheduler_classes[0]
           UpperCamelCase__							:  str								=     self.get_scheduler_config()
           UpperCamelCase__							:  Optional[Any]								=     scheduler_class(**__lowerCamelCase   )

           scheduler.set_timesteps(self.num_inference_steps   )

           UpperCamelCase__							:  int								=     torch.manual_seed(0   )

           UpperCamelCase__							:  Dict								=     self.dummy_model()
           UpperCamelCase__							:  str								=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           UpperCamelCase__							:  int								=     sample.to(__lowerCamelCase   )

           for i, t in enumerate(scheduler.timesteps   ):
                UpperCamelCase__							:  Any								=     scheduler.scale_model_input(__lowerCamelCase,		__lowerCamelCase   )

                UpperCamelCase__							:  Any								=     model(__lowerCamelCase,		__lowerCamelCase   )

                UpperCamelCase__							:  List[str]								=     scheduler.step(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		generator=__lowerCamelCase   )
                UpperCamelCase__							:  Dict								=     output.prev_sample

           UpperCamelCase__							:  Optional[int]								=     torch.sum(torch.abs(__lowerCamelCase   )   )
           UpperCamelCase__							:  Tuple								=     torch.mean(torch.abs(__lowerCamelCase   )   )

           assert abs(result_sum.item() - 152.3192   ) < 1e-2
           assert abs(result_mean.item() - 0.1983   ) < 1e-3






      def 	__lowercase(     self					:   str   )  -> List[str]:
           UpperCamelCase__							:  Optional[int]								=     self.scheduler_classes[0]
           UpperCamelCase__							:  Tuple								=     self.get_scheduler_config(prediction_type='''v_prediction'''   )
           UpperCamelCase__							:  Tuple								=     scheduler_class(**__lowerCamelCase   )

           scheduler.set_timesteps(self.num_inference_steps   )

           UpperCamelCase__							:  Optional[Any]								=     torch.manual_seed(0   )

           UpperCamelCase__							:  List[Any]								=     self.dummy_model()
           UpperCamelCase__							:  List[str]								=     self.dummy_sample_deter * scheduler.init_noise_sigma
           UpperCamelCase__							:  int								=     sample.to(__lowerCamelCase   )

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

                UpperCamelCase__							:  Tuple								=     model(__lowerCamelCase,		__lowerCamelCase   )

                UpperCamelCase__							:  List[Any]								=     scheduler.step(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		generator=__lowerCamelCase   )
                UpperCamelCase__							:  int								=     output.prev_sample

           UpperCamelCase__							:  str								=     torch.sum(torch.abs(__lowerCamelCase   )   )
           UpperCamelCase__							:  Any								=     torch.mean(torch.abs(__lowerCamelCase   )   )

           assert abs(result_sum.item() - 108.4439   ) < 1e-2
           assert abs(result_mean.item() - 0.1412   ) < 1e-3






      def 	__lowercase(     self					:   int   )  -> Dict:
           UpperCamelCase__							:  List[Any]								=     self.scheduler_classes[0]
           UpperCamelCase__							:  int								=     self.get_scheduler_config()
           UpperCamelCase__							:  Optional[int]								=     scheduler_class(**__lowerCamelCase   )

           scheduler.set_timesteps(self.num_inference_steps,		device=__lowerCamelCase   )
           UpperCamelCase__							:  int								=     torch.manual_seed(0   )

           UpperCamelCase__							:  Tuple								=     self.dummy_model()
           UpperCamelCase__							:  List[Any]								=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           UpperCamelCase__							:  List[Any]								=     sample.to(__lowerCamelCase   )

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

                UpperCamelCase__							:  List[str]								=     model(__lowerCamelCase,		__lowerCamelCase   )

                UpperCamelCase__							:  int								=     scheduler.step(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		generator=__lowerCamelCase   )
                UpperCamelCase__							:  List[Any]								=     output.prev_sample

           UpperCamelCase__							:  List[str]								=     torch.sum(torch.abs(__lowerCamelCase   )   )
           UpperCamelCase__							:  str								=     torch.mean(torch.abs(__lowerCamelCase   )   )

           assert abs(result_sum.item() - 152.3192   ) < 1e-2
           assert abs(result_mean.item() - 0.1983   ) < 1e-3








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					Tuple			=	{
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Any			=	["""MarianTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[Any]			=	[
       """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MarianForCausalLM""",
       """MarianModel""",
       """MarianMTModel""",
       """MarianPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Optional[Any]			=	["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

   _SCREAMING_SNAKE_CASE :					List[Any]			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




