


'''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											=					{
    '''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											=					['''LayoutLMv3TokenizerFast''']

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

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''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											=					['''LayoutLMv3FeatureExtractor''']
		__snake_case											=					['''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											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    (DDIMParallelScheduler,)
		_a   			=    (('eta', 0.0), ('num_inference_steps', 50))




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Optional[Any]							 =			self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			10, 0.0

							UpperCamelCase__       :List[str]							 =			self.dummy_model()
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCamelCase_							)

							for t in scheduler.timesteps:
												UpperCamelCase__       :Optional[int]							 =			model(UpperCamelCase_   ,       UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							).prev_sample

							return sample




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timesteps in [100, 500, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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

							UpperCamelCase__       :List[str]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config(steps_offset=1							)
							UpperCamelCase__       :str							 =			scheduler_class(**UpperCamelCase_							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps   ,       torch.LongTensor([801, 601, 401, 201, 1]							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]							):
												self.check_over_configs(beta_start=UpperCamelCase_   ,       beta_end=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 10, 50]   ,       [10, 50, 500]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       num_inference_steps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config()
							UpperCamelCase__       :int							 =			scheduler_class(**UpperCamelCase_							)

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400							) - 0.14771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960							) - 0.32460							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ,       486							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998							) - 0.02							)							) < 1e-5




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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

							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			10, 0.0
							scheduler.set_timesteps(UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			self.dummy_model()
							UpperCamelCase__       :List[Any]							 =			self.dummy_sample_deter
							UpperCamelCase__       :int							 =			self.dummy_sample_deter + 0.1
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter - 0.1

							UpperCamelCase__       :str							 =			samplea.shape[0]
							UpperCamelCase__       :Optional[Any]							 =			torch.stack([samplea, samplea, samplea]   ,       dim=0							)
							UpperCamelCase__       :List[Any]							 =			torch.arange(UpperCamelCase_							)[0:3, None].repeat(1   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(samples.flatten(0   ,       1							)   ,       timesteps.flatten(0   ,       1							)							)
							UpperCamelCase__       :Any							 =			scheduler.batch_step_no_noise(UpperCamelCase_   ,       timesteps.flatten(0   ,       1							)   ,       samples.flatten(0   ,       1							)   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 1147.7904							) < 1e-2
							assert abs(result_mean.item() - 0.4982							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.full_loop()

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

							assert abs(result_sum.item() - 172.0067							) < 1e-2
							assert abs(result_mean.item() - 0.223967							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.full_loop(prediction_type='''v_prediction'''							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :str							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 52.5302							) < 1e-2
							assert abs(result_mean.item() - 0.0684							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :str							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.8295							) < 1e-2
							assert abs(result_mean.item() - 0.1951							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :List[str]							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.0784							) < 1e-2
							assert abs(result_mean.item() - 0.1941							) < 1e-3