





'''simple docstring'''






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   _lowercase						(							a				):
   _UpperCamelCase       		=   (EulerDiscreteScheduler,)
   _UpperCamelCase       		=   10


   def 					snake_case				( self    ,   **_UpperCAmelCase					):
       A       :       List[Any]							=						{
           '''num_train_timesteps''': 1_100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_UpperCAmelCase					)
       return config


   def 					snake_case				( self					):
       for timesteps in [10, 50, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=_UpperCAmelCase					)


   def 					snake_case				( self					):
       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,   [0.0002, 0.002, 0.02]					):
           self.check_over_configs(beta_start=_UpperCAmelCase    ,   beta_end=_UpperCAmelCase					)


   def 					snake_case				( self					):
       for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=_UpperCAmelCase					)


   def 					snake_case				( self					):
       for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       int							=						self.scheduler_classes[0]
       A       :       Optional[Any]							=						self.get_scheduler_config()
       A       :       Optional[Any]							=						scheduler_class(**_UpperCAmelCase					)

       scheduler.set_timesteps(self.num_inference_steps					)

       A       :       int							=						torch.manual_seed(0					)

       A       :       Optional[Any]							=						self.dummy_model()
       A       :       str							=						self.dummy_sample_deter * scheduler.init_noise_sigma
       A       :       Union[str, Any]							=						sample.to(_UpperCAmelCase					)

       for i, t in enumerate(scheduler.timesteps					):
           A       :       Any							=						scheduler.scale_model_input(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       Tuple							=						model(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       Any							=						scheduler.step(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   generator=_UpperCAmelCase					)
           A       :       List[str]							=						output.prev_sample

       A       :       Union[str, Any]							=						torch.sum(torch.abs(_UpperCAmelCase					)					)
       A       :       Optional[Any]							=						torch.mean(torch.abs(_UpperCAmelCase					)					)

       assert abs(result_sum.item() - 10.0807					) < 1E-2
       assert abs(result_mean.item() - 0.0131					) < 1E-3


   def 					snake_case				( self					):
       A       :       Any							=						self.scheduler_classes[0]
       A       :       Optional[Any]							=						self.get_scheduler_config(prediction_type='''v_prediction'''					)
       A       :       Union[str, Any]							=						scheduler_class(**_UpperCAmelCase					)

       scheduler.set_timesteps(self.num_inference_steps					)

       A       :       Dict							=						torch.manual_seed(0					)

       A       :       Optional[int]							=						self.dummy_model()
       A       :       Union[str, Any]							=						self.dummy_sample_deter * scheduler.init_noise_sigma
       A       :       Tuple							=						sample.to(_UpperCAmelCase					)

       for i, t in enumerate(scheduler.timesteps					):
           A       :       Any							=						scheduler.scale_model_input(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       int							=						model(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       str							=						scheduler.step(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   generator=_UpperCAmelCase					)
           A       :       Tuple							=						output.prev_sample

       A       :       List[str]							=						torch.sum(torch.abs(_UpperCAmelCase					)					)
       A       :       int							=						torch.mean(torch.abs(_UpperCAmelCase					)					)

       assert abs(result_sum.item() - 0.0002					) < 1E-2
       assert abs(result_mean.item() - 2.2_6_7_6E-0_6					) < 1E-3


   def 					snake_case				( self					):
       A       :       Optional[int]							=						self.scheduler_classes[0]
       A       :       Union[str, Any]							=						self.get_scheduler_config()
       A       :       Dict							=						scheduler_class(**_UpperCAmelCase					)

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

       A       :       Dict							=						torch.manual_seed(0					)

       A       :       Optional[Any]							=						self.dummy_model()
       A       :       Any							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       A       :       Union[str, Any]							=						sample.to(_UpperCAmelCase					)

       for t in scheduler.timesteps:
           A       :       Any							=						scheduler.scale_model_input(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       Optional[int]							=						model(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       Dict							=						scheduler.step(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   generator=_UpperCAmelCase					)
           A       :       Optional[Any]							=						output.prev_sample

       A       :       Optional[Any]							=						torch.sum(torch.abs(_UpperCAmelCase					)					)
       A       :       Tuple							=						torch.mean(torch.abs(_UpperCAmelCase					)					)

       assert abs(result_sum.item() - 10.0807					) < 1E-2
       assert abs(result_mean.item() - 0.0131					) < 1E-3


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.scheduler_classes[0]
       A       :       int							=						self.get_scheduler_config()
       A       :       Union[str, Any]							=						scheduler_class(**_UpperCAmelCase    ,   use_karras_sigmas=_UpperCAmelCase					)

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

       A       :       Tuple							=						torch.manual_seed(0					)

       A       :       List[str]							=						self.dummy_model()
       A       :       Union[str, Any]							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       A       :       Tuple							=						sample.to(_UpperCAmelCase					)

       for t in scheduler.timesteps:
           A       :       Dict							=						scheduler.scale_model_input(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       Dict							=						model(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       Optional[int]							=						scheduler.step(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   generator=_UpperCAmelCase					)
           A       :       Union[str, Any]							=						output.prev_sample

       A       :       Tuple							=						torch.sum(torch.abs(_UpperCAmelCase					)					)
       A       :       Tuple							=						torch.mean(torch.abs(_UpperCAmelCase					)					)

       assert abs(result_sum.item() - 124.52299499511719					) < 1E-2
       assert abs(result_mean.item() - 0.16213932633399963					) < 1E-3







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


snake_case_     =	{
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	["""SpeechT5Tokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SpeechT5ForSpeechToText""",
         """SpeechT5ForSpeechToSpeech""",
         """SpeechT5ForTextToSpeech""",
         """SpeechT5Model""",
         """SpeechT5PreTrainedModel""",
         """SpeechT5HifiGan""",
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

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