





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = (DDPMParallelScheduler,)






  def 				lowercase		(				self :		int					,  **lowerCAmelCase_ :		Tuple     )							->						List[Any]:
     __lowerCAmelCase								=	{
         'num_train_timesteps': 1_0_0_0,
         'beta_start': 0.00_01,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'variance_type': 'fixed_small',
         'clip_sample': True,
     }

     config.update(**lowerCAmelCase_     )
     return config






  def 				lowercase		(				self :		Tuple     )							->						str:
     for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase_     )






  def 				lowercase		(				self :		Tuple     )							->						Dict:
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]					,  [0.0_02, 0.02, 0.2, 2]     ):
        self.check_over_configs(beta_start=lowerCAmelCase_					,  beta_end=lowerCAmelCase_     )






  def 				lowercase		(				self :		List[Any]     )							->						Optional[int]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase_     )






  def 				lowercase		(				self :		Dict     )							->						Dict:
     for variance in ["fixed_small", "fixed_large", "other"]:
        self.check_over_configs(variance_type=lowerCAmelCase_     )






  def 				lowercase		(				self :		List[str]     )							->						List[str]:
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowerCAmelCase_     )






  def 				lowercase		(				self :		int     )							->						List[Any]:
     self.check_over_configs(thresholding=lowerCAmelCase_     )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowerCAmelCase_					,  prediction_type=lowerCAmelCase_					,  sample_max_value=lowerCAmelCase_					,  )






  def 				lowercase		(				self :		Optional[int]     )							->						Dict:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase_     )






  def 				lowercase		(				self :		str     )							->						str:
     for t in [0, 5_0_0, 9_9_9]:
        self.check_over_forward(time_step=lowerCAmelCase_     )






  def 				lowercase		(				self :		List[Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.0_09_79     )     ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.02     )     ) < 1e-5






  def 				lowercase		(				self :		Any     )							->						List[Any]:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase								=	len(lowerCAmelCase_     )

     __lowerCAmelCase								=	self.dummy_model()
     __lowerCAmelCase								=	self.dummy_sample_deter
     __lowerCAmelCase								=	self.dummy_sample_deter + 0.1
     __lowerCAmelCase								=	self.dummy_sample_deter - 0.1

     __lowerCAmelCase								=	samplea.shape[0]
     __lowerCAmelCase								=	torch.stack([samplea, samplea, samplea]					,  dim=0     )
     __lowerCAmelCase								=	torch.arange(lowerCAmelCase_     )[0:3, None].repeat(1					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	model(samples.flatten(0					,  1     )					,  timesteps.flatten(0					,  1     )     )
     __lowerCAmelCase								=	scheduler.batch_step_no_noise(lowerCAmelCase_					,  timesteps.flatten(0					,  1     )					,  samples.flatten(0					,  1     )     )

     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 11_53.18_33     ) < 1e-2
     assert abs(result_mean.item() - 0.50_05     ) < 1e-3






  def 				lowercase		(				self :		Union[str, Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase								=	len(lowerCAmelCase_     )

     __lowerCAmelCase								=	self.dummy_model()
     __lowerCAmelCase								=	self.dummy_sample_deter
     __lowerCAmelCase								=	torch.manual_seed(0     )

     for t in reversed(range(lowerCAmelCase_     )     ):
        # 1. predict noise residual
        __lowerCAmelCase								=	model(lowerCAmelCase_					,  lowerCAmelCase_     )

        # 2. predict previous mean of sample x_t-1
        __lowerCAmelCase								=	scheduler.step(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  generator=lowerCAmelCase_     ).prev_sample

        __lowerCAmelCase								=	pred_prev_sample

     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 2_58.96_06     ) < 1e-2
     assert abs(result_mean.item() - 0.33_72     ) < 1e-3






  def 				lowercase		(				self :		Union[str, Any]     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config(prediction_type='v_prediction'     )
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase								=	len(lowerCAmelCase_     )

     __lowerCAmelCase								=	self.dummy_model()
     __lowerCAmelCase								=	self.dummy_sample_deter
     __lowerCAmelCase								=	torch.manual_seed(0     )

     for t in reversed(range(lowerCAmelCase_     )     ):
        # 1. predict noise residual
        __lowerCAmelCase								=	model(lowerCAmelCase_					,  lowerCAmelCase_     )

        # 2. predict previous mean of sample x_t-1
        __lowerCAmelCase								=	scheduler.step(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  generator=lowerCAmelCase_     ).prev_sample

        __lowerCAmelCase								=	pred_prev_sample

     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 2_02.02_96     ) < 1e-2
     assert abs(result_mean.item() - 0.26_31     ) < 1e-3






  def 				lowercase		(				self :		Union[str, Any]     )							->						Dict:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase								=	[1_0_0, 8_7, 5_0, 1, 0]

     scheduler.set_timesteps(timesteps=lowerCAmelCase_     )

     __lowerCAmelCase								=	scheduler.timesteps

     for i, timestep in enumerate(lowerCAmelCase_     ):
        if i == len(lowerCAmelCase_     ) - 1:
           __lowerCAmelCase								=	-1
        else:
           __lowerCAmelCase								=	timesteps[i + 1]

        __lowerCAmelCase								=	scheduler.previous_timestep(lowerCAmelCase_     )
        __lowerCAmelCase								=	prev_t.item()

        self.assertEqual(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						List[str]:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase								=	[1_0_0, 8_7, 5_0, 5_1, 0]

     with self.assertRaises(lowerCAmelCase_					,  msg='`custom_timesteps` must be in descending order.'     ):
        scheduler.set_timesteps(timesteps=lowerCAmelCase_     )






  def 				lowercase		(				self :		int     )							->						int:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase								=	[1_0_0, 8_7, 5_0, 1, 0]
     __lowerCAmelCase								=	len(lowerCAmelCase_     )

     with self.assertRaises(lowerCAmelCase_					,  msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'     ):
        scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_					,  timesteps=lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]     )							->						int:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase								=	[scheduler.config.num_train_timesteps]

     with self.assertRaises(
         lowerCAmelCase_					,  msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'					,  ):
        scheduler.set_timesteps(timesteps=lowerCAmelCase_     )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case  :    Union[str, Any]						    =  {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Union[str, Any]						    =  ['LayoutLMv3TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    List[str]						    =  [
         'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'LayoutLMv3ForQuestionAnswering',
         'LayoutLMv3ForSequenceClassification',
         'LayoutLMv3ForTokenClassification',
         'LayoutLMv3Model',
         'LayoutLMv3PreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[Any]						    =  [
         'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFLayoutLMv3ForQuestionAnswering',
         'TFLayoutLMv3ForSequenceClassification',
         'TFLayoutLMv3ForTokenClassification',
         'TFLayoutLMv3Model',
         'TFLayoutLMv3PreTrainedModel',
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Tuple						    =  ['LayoutLMv3FeatureExtractor']
     _snake_case  :    str						    =  ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
     from .configuration_layoutlmva import (
         LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LayoutLMvaConfig,
         LayoutLMvaOnnxConfig,
     )
     from .processing_layoutlmva import LayoutLMvaProcessor
     from .tokenization_layoutlmva import LayoutLMvaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlmva import (
              LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMvaForQuestionAnswering,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaModel,
              LayoutLMvaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlmva import (
              TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
              TFLayoutLMvaModel,
              TFLayoutLMvaPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
          from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
     import sys

     _snake_case  :    Any						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

