

from typing import TYPE_CHECKING

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


__A   :     Dict      =			{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     List[Any]      =			['''BarthezTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     List[str]      =			['''BarthezTokenizerFast''']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_barthez import BarthezTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_barthez_fast import BarthezTokenizerFast

else:
 import sys

 __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class    __A				(							lowerCAmelCase ):
    lowerCAmelCase_							:		List[str]						=							(KDPMaAncestralDiscreteScheduler,)
    lowerCAmelCase_							:		Dict						=							10






    def 	lowercase__ (	self			:    Any    ,				**UpperCAmelCase_			:    List[Any]    ):
        lowerCAmelCase				:			int    			=      {
            'num_train_timesteps': 1100,
            'beta_start': 0.00_01,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**UpperCAmelCase_    )
        return config






    def 	lowercase__ (	self			:    Optional[Any]    ):
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=UpperCAmelCase_    )






    def 	lowercase__ (	self			:    List[str]    ):
        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=UpperCAmelCase_    ,				beta_end=UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Optional[int]    ):
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=UpperCAmelCase_    )






    def 	lowercase__ (	self			:    int    ):
        if torch_device == "mps":
            return
        lowerCAmelCase				:			Any    			=      self.scheduler_classes[0]
        lowerCAmelCase				:			Optional[int]    			=      self.get_scheduler_config()
        lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )

        scheduler.set_timesteps(self.num_inference_steps    )

        lowerCAmelCase				:			Any    			=      torch.manual_seed(0    )

        lowerCAmelCase				:			List[Any]    			=      self.dummy_model()
        lowerCAmelCase				:			List[Any]    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )

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

            lowerCAmelCase				:			Any    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )

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

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

        assert abs(result_sum.item() - 1_38_49.38_77    ) < 1E-2
        assert abs(result_mean.item() - 18.03_31    ) < 5E-3






    def 	lowercase__ (	self			:    Tuple    ):
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=UpperCAmelCase_    )






    def 	lowercase__ (	self			:    List[str]    ):
        if torch_device == "mps":
            return
        lowerCAmelCase				:			Optional[Any]    			=      self.scheduler_classes[0]
        lowerCAmelCase				:			List[Any]    			=      self.get_scheduler_config(prediction_type='v_prediction'    )
        lowerCAmelCase				:			Any    			=      scheduler_class(**UpperCAmelCase_    )

        scheduler.set_timesteps(self.num_inference_steps    )

        lowerCAmelCase				:			Optional[int]    			=      self.dummy_model()
        lowerCAmelCase				:			Any    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )

        lowerCAmelCase				:			Optional[int]    			=      torch.manual_seed(0    )

        for i, t in enumerate(scheduler.timesteps    ):
            lowerCAmelCase				:			Dict    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )

            lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )

            lowerCAmelCase				:			Optional[int]    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
            lowerCAmelCase				:			str    			=      output.prev_sample

        lowerCAmelCase				:			str    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
        lowerCAmelCase				:			Tuple    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )

        assert abs(result_sum.item() - 3_28.99_70    ) < 1E-2
        assert abs(result_mean.item() - 0.42_84    ) < 1E-3






    def 	lowercase__ (	self			:    Tuple    ):
        if torch_device == "mps":
            return
        lowerCAmelCase				:			str    			=      self.scheduler_classes[0]
        lowerCAmelCase				:			Dict    			=      self.get_scheduler_config()
        lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )

        scheduler.set_timesteps(self.num_inference_steps    ,				device=UpperCAmelCase_    )
        lowerCAmelCase				:			int    			=      torch.manual_seed(0    )

        lowerCAmelCase				:			Tuple    			=      self.dummy_model()
        lowerCAmelCase				:			Optional[Any]    			=      self.dummy_sample_deter.to(UpperCAmelCase_    ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
            lowerCAmelCase				:			str    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )

            lowerCAmelCase				:			List[Any]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )

            lowerCAmelCase				:			Dict    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
            lowerCAmelCase				:			Dict    			=      output.prev_sample

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

        assert abs(result_sum.item() - 1_38_49.38_18    ) < 1E-1
        assert abs(result_mean.item() - 18.03_31    ) < 1E-3





