


'''simple docstring'''







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE__	):



     """simple docstring"""






     __magic_name__    			=    (DPMSolverSDEScheduler,)
     __magic_name__    			=    1_0







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {
                "num_train_timesteps": 1100,
                "beta_start": 0.0001,
                "beta_end": 0.02,
                "beta_schedule": "linear",
                "noise_sampler_seed": 0,
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''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=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Tuple   			=       scheduler_class(**snake_case__   )

            scheduler.set_timesteps(self.num_inference_steps   )

            _lowerCAmelCase    :							Union[str, Any]   			=       self.dummy_model()
            _lowerCAmelCase    :							Union[str, Any]   			=       self.dummy_sample_deter * scheduler.init_noise_sigma
            _lowerCAmelCase    :							List[Any]   			=       sample.to(snake_case__   )

            for i, t in enumerate(scheduler.timesteps   ):
                   _lowerCAmelCase    :							str   			=       scheduler.scale_model_input(snake_case__      ,	snake_case__   )

                   _lowerCAmelCase    :							List[str]   			=       model(snake_case__      ,	snake_case__   )

                   _lowerCAmelCase    :							Dict   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__   )
                   _lowerCAmelCase    :							Tuple   			=       output.prev_sample

            _lowerCAmelCase    :							List[str]   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							int   			=       torch.mean(torch.abs(snake_case__   )   )

            if torch_device in ["mps"]:
                   assert abs(result_sum.item() - 167.47_8210_4492_1875   ) < 1E-2
                   assert abs(result_mean.item() - 0.2178_7059_6456_5277   ) < 1E-3
            elif torch_device in ["cuda"]:
                   assert abs(result_sum.item() - 171.59_3521_1181_6406   ) < 1E-2
                   assert abs(result_mean.item() - 0.2_2342_9068_9229_9652   ) < 1E-3
            else:
                   assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1E-2
                   assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Dict   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							Dict   			=       scheduler_class(**snake_case__   )

            scheduler.set_timesteps(self.num_inference_steps   )

            _lowerCAmelCase    :							Optional[Any]   			=       self.dummy_model()
            _lowerCAmelCase    :							Dict   			=       self.dummy_sample_deter * scheduler.init_noise_sigma
            _lowerCAmelCase    :							Optional[Any]   			=       sample.to(snake_case__   )

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

                   _lowerCAmelCase    :							List[str]   			=       model(snake_case__      ,	snake_case__   )

                   _lowerCAmelCase    :							Optional[int]   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__   )
                   _lowerCAmelCase    :							str   			=       output.prev_sample

            _lowerCAmelCase    :							int   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Optional[Any]   			=       torch.mean(torch.abs(snake_case__   )   )

            if torch_device in ["mps"]:
                   assert abs(result_sum.item() - 124.77_1492_0043_9453   ) < 1E-2
                   assert abs(result_mean.item() - 0.1_6226_2890_1481_6284   ) < 1E-3
            elif torch_device in ["cuda"]:
                   assert abs(result_sum.item() - 128.1_6633_6059_5703   ) < 1E-2
                   assert abs(result_mean.item() - 0.1_6688_3260_0116_7297   ) < 1E-3
            else:
                   assert abs(result_sum.item() - 119.8_4875_4882_8125   ) < 1E-2
                   assert abs(result_mean.item() - 0.1560_5306_6253_6621   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							str   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

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

            _lowerCAmelCase    :							Optional[Any]   			=       self.dummy_model()
            _lowerCAmelCase    :							List[Any]   			=       self.dummy_sample_deter.to(snake_case__   ) * scheduler.init_noise_sigma

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

                   _lowerCAmelCase    :							str   			=       model(snake_case__      ,	snake_case__   )

                   _lowerCAmelCase    :							Any   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__   )
                   _lowerCAmelCase    :							Tuple   			=       output.prev_sample

            _lowerCAmelCase    :							Tuple   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Optional[Any]   			=       torch.mean(torch.abs(snake_case__   )   )

            if torch_device in ["mps"]:
                   assert abs(result_sum.item() - 167.46_9573_9746_0938   ) < 1E-2
                   assert abs(result_mean.item() - 0.2_1805_9346_0798_2635   ) < 1E-3
            elif torch_device in ["cuda"]:
                   assert abs(result_sum.item() - 171.59_3536_3769_5312   ) < 1E-2
                   assert abs(result_mean.item() - 0.2_2342_9083_8241_5771   ) < 1E-3
            else:
                   assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1E-2
                   assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Optional[int]   			=       scheduler_class(**snake_case__      ,	use_karras_sigmas=snake_case__   )

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

            _lowerCAmelCase    :							Optional[Any]   			=       self.dummy_model()
            _lowerCAmelCase    :							Any   			=       self.dummy_sample_deter.to(snake_case__   ) * scheduler.init_noise_sigma
            _lowerCAmelCase    :							List[Any]   			=       sample.to(snake_case__   )

            for t in scheduler.timesteps:
                   _lowerCAmelCase    :							str   			=       scheduler.scale_model_input(snake_case__      ,	snake_case__   )

                   _lowerCAmelCase    :							List[str]   			=       model(snake_case__      ,	snake_case__   )

                   _lowerCAmelCase    :							str   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__   )
                   _lowerCAmelCase    :							str   			=       output.prev_sample

            _lowerCAmelCase    :							List[str]   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

            if torch_device in ["mps"]:
                   assert abs(result_sum.item() - 176.66_9741_3574_2188   ) < 1E-2
                   assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2
            elif torch_device in ["cuda"]:
                   assert abs(result_sum.item() - 177.63_6535_6445_3125   ) < 1E-2
                   assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2
            else:
                   assert abs(result_sum.item() - 170.3_1352_2338_8672   ) < 1E-2
                   assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2





'''simple docstring'''







from typing import TYPE_CHECKING

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


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


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

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

