


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[int]			=      (EulerDiscreteScheduler,)
     A:		List[Any]			=      1_0


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	**lowerCamelCase__							:     Any			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
      }

      config.update(**lowerCamelCase__			)
      return config


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Any:





      '''simple docstring'''






      for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      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      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     int			)  ->      List[Any]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     int			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.scheduler_classes[0]
      UpperCamelCase__					:    List[Any]          =   self.get_scheduler_config()
      UpperCamelCase__					:    Tuple          =   scheduler_class(**lowerCamelCase__			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCamelCase__					:    str          =   torch.manual_seed(0			)

      UpperCamelCase__					:    Optional[int]          =   self.dummy_model()
      UpperCamelCase__					:    Tuple          =   self.dummy_sample_deter * scheduler.init_noise_sigma
      UpperCamelCase__					:    Optional[int]          =   sample.to(lowerCamelCase__			)

      for i, t in enumerate(scheduler.timesteps			):
       UpperCamelCase__					:    Optional[int]          =   scheduler.scale_model_input(lowerCamelCase__							,	lowerCamelCase__			)

       UpperCamelCase__					:    int          =   model(lowerCamelCase__							,	lowerCamelCase__			)

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

      UpperCamelCase__					:    int          =   torch.sum(torch.abs(lowerCamelCase__			)			)
      UpperCamelCase__					:    Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__			)			)

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


     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.scheduler_classes[0]
      UpperCamelCase__					:    List[Any]          =   self.get_scheduler_config(prediction_type='''v_prediction'''			)
      UpperCamelCase__					:    Optional[int]          =   scheduler_class(**lowerCamelCase__			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCamelCase__					:    Dict          =   torch.manual_seed(0			)

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

      for i, t in enumerate(scheduler.timesteps			):
       UpperCamelCase__					:    List[Any]          =   scheduler.scale_model_input(lowerCamelCase__							,	lowerCamelCase__			)

       UpperCamelCase__					:    Tuple          =   model(lowerCamelCase__							,	lowerCamelCase__			)

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

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

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


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   self.scheduler_classes[0]
      UpperCamelCase__					:    Tuple          =   self.get_scheduler_config()
      UpperCamelCase__					:    List[Any]          =   scheduler_class(**lowerCamelCase__			)

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

      UpperCamelCase__					:    int          =   torch.manual_seed(0			)

      UpperCamelCase__					:    List[str]          =   self.dummy_model()
      UpperCamelCase__					:    Optional[int]          =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCamelCase__					:    str          =   sample.to(lowerCamelCase__			)

      for t in scheduler.timesteps:
       UpperCamelCase__					:    int          =   scheduler.scale_model_input(lowerCamelCase__							,	lowerCamelCase__			)

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

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

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

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


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   self.scheduler_classes[0]
      UpperCamelCase__					:    Dict          =   self.get_scheduler_config()
      UpperCamelCase__					:    List[str]          =   scheduler_class(**lowerCamelCase__							,	use_karras_sigmas=lowerCamelCase__			)

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

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

      UpperCamelCase__					:    List[Any]          =   self.dummy_model()
      UpperCamelCase__					:    int          =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCamelCase__					:    Optional[Any]          =   sample.to(lowerCamelCase__			)

      for t in scheduler.timesteps:
       UpperCamelCase__					:    str          =   scheduler.scale_model_input(lowerCamelCase__							,	lowerCamelCase__			)

       UpperCamelCase__					:    Any          =   model(lowerCamelCase__							,	lowerCamelCase__			)

       UpperCamelCase__					:    int          =   scheduler.step(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__							,	generator=lowerCamelCase__			)
       UpperCamelCase__					:    str          =   output.prev_sample

      UpperCamelCase__					:    Optional[int]          =   torch.sum(torch.abs(lowerCamelCase__			)			)
      UpperCamelCase__					:    Any          =   torch.mean(torch.abs(lowerCamelCase__			)			)

      assert abs(result_sum.item() - 124.52_2994_9951_1719			) < 1E-2
      assert abs(result_mean.item() - 0.1_6213_9326_3339_9963			) < 1E-3






from typing import TYPE_CHECKING

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


__UpperCamelCase       :       Dict						       =				{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "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

       __UpperCamelCase       :       Tuple						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


