





"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class       a							(   a__  ):
 snake_case__             =       (DDIMParallelScheduler,)
 snake_case__             =       (('''eta''', 0.0), ('''num_inference_steps''', 5_0))



 def    UpperCamelCase__  (     self				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= {
      'num_train_timesteps': 10_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
      'clip_sample': True,
  }

  config.update(**_snake_case     )
  return config



 def    UpperCamelCase__  (     self				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config(**_snake_case     )
  lowerCAmelCase    				= scheduler_class(**_snake_case     )

  lowerCAmelCase				,lowerCAmelCase    				= 10, 0.0

  lowerCAmelCase    				= self.dummy_model()
  lowerCAmelCase    				= self.dummy_sample_deter

  scheduler.set_timesteps(_snake_case     )

  for t in scheduler.timesteps:
   lowerCAmelCase    				= model(_snake_case				,       _snake_case     )
   lowerCAmelCase    				= scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ).prev_sample

  return sample



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for timesteps in [1_00, 5_00, 10_00]:
   self.check_over_configs(num_train_timesteps=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for steps_offset in [0, 1]:
   self.check_over_configs(steps_offset=_snake_case     )

  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config(steps_offset=1     )
  lowerCAmelCase    				= scheduler_class(**_snake_case     )
  scheduler.set_timesteps(5     )
  assert torch.equal(scheduler.timesteps				,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,       [0.002, 0.02, 0.2, 2]     ):
   self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for schedule in ["linear", "squaredcos_cap_v2"]:
   self.check_over_configs(beta_schedule=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for clip_sample in [True, False]:
   self.check_over_configs(clip_sample=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for timestep_spacing in ["trailing", "leading"]:
   self.check_over_configs(timestep_spacing=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for rescale_betas_zero_snr in [True, False]:
   self.check_over_configs(rescale_betas_zero_snr=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.check_over_configs(thresholding=_snake_case     )
  for threshold in [0.5, 1.0, 2.0]:
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(
        thresholding=_snake_case				,       prediction_type=_snake_case				,       sample_max_value=_snake_case				,       )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for t in [1, 10, 49]:
   self.check_over_forward(time_step=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for t, num_inference_steps in zip([1, 10, 50]				,       [10, 50, 5_00]     ):
   self.check_over_forward(time_step=_snake_case				,       num_inference_steps=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for t, eta in zip([1, 10, 49]				,       [0.0, 0.5, 1.0]     ):
   self.check_over_forward(time_step=_snake_case				,       eta=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config()
  lowerCAmelCase    				= scheduler_class(**_snake_case     )

  assert torch.sum(torch.abs(scheduler._get_variance(0				,       0     ) - 0.0     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(4_20				,       4_00     ) - 0.14_771     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_80				,       9_60     ) - 0.32_460     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(0				,       0     ) - 0.0     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(4_87				,       4_86     ) - 0.00_979     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_99				,       9_98     ) - 0.02     )     ) < 1E-5



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config()
  lowerCAmelCase    				= scheduler_class(**_snake_case     )

  lowerCAmelCase				,lowerCAmelCase    				= 10, 0.0
  scheduler.set_timesteps(_snake_case     )

  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(_snake_case     )[0:3, None].repeat(1				,       _snake_case     )

  lowerCAmelCase    				= model(samples.flatten(0				,       1     )				,       timesteps.flatten(0				,       1     )     )
  lowerCAmelCase    				= scheduler.batch_step_no_noise(_snake_case				,       timesteps.flatten(0				,       1     )				,       samples.flatten(0				,       1     )				,       _snake_case     )

  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 1_147.7_904     ) < 1E-2
  assert abs(result_mean.item() - 0.4_982     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop()

  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 172.0_067     ) < 1E-2
  assert abs(result_mean.item() - 0.223_967     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop(prediction_type='v_prediction'     )

  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 52.5_302     ) < 1E-2
  assert abs(result_mean.item() - 0.0_684     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop(set_alpha_to_one=_snake_case				,       beta_start=0.01     )
  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 149.8_295     ) < 1E-2
  assert abs(result_mean.item() - 0.1_951     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop(set_alpha_to_one=_snake_case				,       beta_start=0.01     )
  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 149.0_784     ) < 1E-2
  assert abs(result_mean.item() - 0.1_941     ) < 1E-3











'''simple docstring'''


from typing import TYPE_CHECKING

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


_snake_case : str  				=						{
    '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 : List[str]  				=						['LayoutLMv3TokenizerFast']

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

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    '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 : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['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__)

