



"""simple docstring"""







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					(KDPMaDiscreteScheduler,)
      _SCREAMING_SNAKE_CASE      =					10



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      **SCREAMING_SNAKE_CASE_    :				List[Any]	):
        lowerCAmelCase_       :    int								=			{
            'num_train_timesteps': 1_1_0_0,
            'beta_start': 0.00_01,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_	)
        return config



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				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=SCREAMING_SNAKE_CASE_   ,      beta_end=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    str								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    Tuple								=			self.get_scheduler_config(prediction_type='v_prediction'	)
        lowerCAmelCase_       :    int								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

        scheduler.set_timesteps(self.num_inference_steps	)

        lowerCAmelCase_       :    Optional[int]								=			self.dummy_model()
        lowerCAmelCase_       :    Dict								=			self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase_       :    Tuple								=			sample.to(SCREAMING_SNAKE_CASE_	)

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

          lowerCAmelCase_       :    Optional[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    str								=			scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Dict								=			output.prev_sample

        lowerCAmelCase_       :    List[Any]								=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        lowerCAmelCase_       :    List[str]								=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.69_34E-07	) < 1E-2
          assert abs(result_mean.item() - 6.11_12E-10	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07	) < 1E-2
          assert abs(result_mean.item() - 0.00_02	) < 1E-3



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        if torch_device == "mps":
          return
        lowerCAmelCase_       :    Optional[int]								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    Dict								=			self.get_scheduler_config()
        lowerCAmelCase_       :    List[str]								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

        scheduler.set_timesteps(self.num_inference_steps	)

        lowerCAmelCase_       :    Optional[Any]								=			self.dummy_model()
        lowerCAmelCase_       :    Tuple								=			self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase_       :    Optional[Any]								=			sample.to(SCREAMING_SNAKE_CASE_	)

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

          lowerCAmelCase_       :    List[str]								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    List[str]								=			scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    List[Any]								=			output.prev_sample

        lowerCAmelCase_       :    List[Any]								=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        lowerCAmelCase_       :    Union[str, Any]								=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 20.41_25	) < 1E-2
          assert abs(result_mean.item() - 0.02_66	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 20.41_25	) < 1E-2
          assert abs(result_mean.item() - 0.02_66	) < 1E-3





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        if torch_device == "mps":
          return
        lowerCAmelCase_       :    Tuple								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    Union[str, Any]								=			self.get_scheduler_config()
        lowerCAmelCase_       :    Tuple								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

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

        lowerCAmelCase_       :    List[str]								=			self.dummy_model()
        lowerCAmelCase_       :    Optional[int]								=			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_	) * scheduler.init_noise_sigma

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

          lowerCAmelCase_       :    Any								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    Any								=			scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Union[str, Any]								=			output.prev_sample

        lowerCAmelCase_       :    Any								=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        lowerCAmelCase_       :    Optional[Any]								=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        if str(SCREAMING_SNAKE_CASE_	).startswith('cpu'	):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 20.41_25	) < 1E-2
          assert abs(result_mean.item() - 0.02_66	) < 1E-3
        else:
          # CUDA
          assert abs(result_sum.item() - 20.41_25	) < 1E-2
          assert abs(result_mean.item() - 0.02_66	) < 1E-3










"""simple docstring"""







from typing import TYPE_CHECKING

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


lowercase__		:    Optional[int]				   =     {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:    Dict				   =     [
         """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TrOCRForCausalLM""",
         """TrOCRPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
     from .processing_trocr import TrOCRProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
     import sys

     lowercase__		:    int				   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





