
"""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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (DPMSolverSDEScheduler,)
	UpperCamelCase				=  10





	def   __lowerCAmelCase (						self    :int		, **lowerCamelCase_    :List[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= {
			    '''num_train_timesteps''': 11_00,
			    '''beta_start''': 0.0_0_0_1,
			    '''beta_end''': 0.0_2,
			    '''beta_schedule''': '''linear''',
			    '''noise_sampler_seed''': 0,
			}

			config.update(**lowerCamelCase_					)
			return config





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any					)							->			Union[str, Any]:







			'''simple docstring'''




			for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		, [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
					self.check_over_configs(beta_start=lowerCamelCase_		, beta_end=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict					)							->			Optional[Any]:







			'''simple docstring'''




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





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







			'''simple docstring'''




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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     int				= scheduler_class(**lowerCamelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			SCREAMING_SNAKE_CASE       :     Tuple				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.dummy_sample_deter * scheduler.init_noise_sigma
			SCREAMING_SNAKE_CASE       :     Tuple				= sample.to(lowerCamelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					SCREAMING_SNAKE_CASE       :     str				= scheduler.scale_model_input(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     int				= model(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     str				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Any				= output.prev_sample

			SCREAMING_SNAKE_CASE       :     int				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.mean(torch.abs(lowerCamelCase_					)					)

			if torch_device in ["mps"]:
					assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5					) < 1E-2
					assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7					) < 1E-3
			elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6					) < 1E-2
					assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2					) < 1E-3
			else:
					assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2					) < 1E-2
					assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6					) < 1E-3





	def   __lowerCAmelCase (						self    :List[str]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     int				= self.get_scheduler_config(prediction_type='''v_prediction'''					)
			SCREAMING_SNAKE_CASE       :     List[str]				= scheduler_class(**lowerCamelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			SCREAMING_SNAKE_CASE       :     List[str]				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     str				= self.dummy_sample_deter * scheduler.init_noise_sigma
			SCREAMING_SNAKE_CASE       :     Tuple				= sample.to(lowerCamelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					SCREAMING_SNAKE_CASE       :     List[str]				= scheduler.scale_model_input(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     Any				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Dict				= output.prev_sample

			SCREAMING_SNAKE_CASE       :     Tuple				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.mean(torch.abs(lowerCamelCase_					)					)

			if torch_device in ["mps"]:
					assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3					) < 1E-2
					assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4					) < 1E-3
			elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3					) < 1E-2
					assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7					) < 1E-3
			else:
					assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5					) < 1E-2
					assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1					) < 1E-3





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= scheduler_class(**lowerCamelCase_					)

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

			SCREAMING_SNAKE_CASE       :     int				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     List[str]				= self.dummy_sample_deter.to(lowerCamelCase_					) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
					SCREAMING_SNAKE_CASE       :     List[str]				= scheduler.scale_model_input(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     int				= model(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     Tuple				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[str]				= output.prev_sample

			SCREAMING_SNAKE_CASE       :     int				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     int				= torch.mean(torch.abs(lowerCamelCase_					)					)

			if torch_device in ["mps"]:
					assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8					) < 1E-2
					assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5					) < 1E-3
			elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2					) < 1E-2
					assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1					) < 1E-3
			else:
					assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2					) < 1E-2
					assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6					) < 1E-3







	def   __lowerCAmelCase (						self    :List[str]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Dict				= scheduler_class(**lowerCamelCase_		, use_karras_sigmas=lowerCamelCase_					)

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

			SCREAMING_SNAKE_CASE       :     int				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Any				= self.dummy_sample_deter.to(lowerCamelCase_					) * scheduler.init_noise_sigma
			SCREAMING_SNAKE_CASE       :     Tuple				= sample.to(lowerCamelCase_					)

			for t in scheduler.timesteps:
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= scheduler.scale_model_input(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     Dict				= model(lowerCamelCase_		, lowerCamelCase_					)

					SCREAMING_SNAKE_CASE       :     Optional[Any]				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[str]				= output.prev_sample

			SCREAMING_SNAKE_CASE       :     Tuple				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Any				= torch.mean(torch.abs(lowerCamelCase_					)					)

			if torch_device in ["mps"]:
					assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8					) < 1E-2
					assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2
			elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5					) < 1E-2
					assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2
			else:
					assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2					) < 1E-2
					assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2







"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


lowerCamelCase__ :       Dict						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Dict						     =			{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """layoutlmv3"""





	def __init__(						self    :str		, lowerCamelCase_    :Optional[Any]=5_02_65		, lowerCamelCase_    :Dict=7_68		, lowerCamelCase_    :Union[str, Any]=12		, lowerCamelCase_    :Optional[Any]=12		, lowerCamelCase_    :Union[str, Any]=30_72		, lowerCamelCase_    :Any="gelu"		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :str=0.1		, lowerCamelCase_    :Any=5_12		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Optional[Any]=0.0_2		, lowerCamelCase_    :Optional[int]=1E-5		, lowerCamelCase_    :Dict=1		, lowerCamelCase_    :int=0		, lowerCamelCase_    :Tuple=2		, lowerCamelCase_    :List[str]=10_24		, lowerCamelCase_    :Tuple=1_28		, lowerCamelCase_    :Any=1_28		, lowerCamelCase_    :Optional[Any]=True		, lowerCamelCase_    :str=32		, lowerCamelCase_    :int=1_28		, lowerCamelCase_    :int=64		, lowerCamelCase_    :List[Any]=2_56		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :str=True		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :List[str]=2_24		, lowerCamelCase_    :Dict=3		, lowerCamelCase_    :Union[str, Any]=16		, lowerCamelCase_    :Any=None		, **lowerCamelCase_    :Optional[Any]		, )							->			int:







			'''simple docstring'''




			super().__init__(
			    vocab_size=lowerCamelCase_		, hidden_size=lowerCamelCase_		, num_hidden_layers=lowerCamelCase_		, num_attention_heads=lowerCamelCase_		, intermediate_size=lowerCamelCase_		, hidden_act=lowerCamelCase_		, hidden_dropout_prob=lowerCamelCase_		, attention_probs_dropout_prob=lowerCamelCase_		, max_position_embeddings=lowerCamelCase_		, type_vocab_size=lowerCamelCase_		, initializer_range=lowerCamelCase_		, layer_norm_eps=lowerCamelCase_		, pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= max_ad_position_embeddings
			SCREAMING_SNAKE_CASE       :     List[Any]				= coordinate_size
			SCREAMING_SNAKE_CASE       :     Tuple				= shape_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= has_relative_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_pos_bins
			SCREAMING_SNAKE_CASE       :     int				= max_rel_pos
			SCREAMING_SNAKE_CASE       :     Any				= has_spatial_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_ad_pos_bins
			SCREAMING_SNAKE_CASE       :     Dict				= max_rel_ad_pos
			SCREAMING_SNAKE_CASE       :     Optional[int]				= text_embed
			SCREAMING_SNAKE_CASE       :     Any				= visual_embed
			SCREAMING_SNAKE_CASE       :     Any				= input_size
			SCREAMING_SNAKE_CASE       :     Tuple				= num_channels
			SCREAMING_SNAKE_CASE       :     List[str]				= patch_size
			SCREAMING_SNAKE_CASE       :     str				= classifier_dropout





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  version.parse("""1.12"""   )





	@property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			if self.task in ["question-answering", "sequence-classification"]:
					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
					    ]					)
			else:
					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
					    ]					)





	@property
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			float:







			'''simple docstring'''




			return 1E-5





	@property
	def   __lowerCAmelCase (						self    :Tuple					)							->			int:







			'''simple docstring'''




			return 12







	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :"ProcessorMixin"		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional["TensorType"] = None		, lowerCamelCase_    :int = 3		, lowerCamelCase_    :int = 40		, lowerCamelCase_    :int = 40		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			setattr(processor.image_processor		, '''apply_ocr'''		, lowerCamelCase_					)

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			SCREAMING_SNAKE_CASE       :     Dict				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_batch		, num_token_to_add=0					)
			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= processor.tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=lowerCamelCase_					)
			# Generate dummy inputs according to compute batch and sequence
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [[''' '''.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

			# Generate dummy bounding boxes
			SCREAMING_SNAKE_CASE       :     int				= [[[48, 84, 73, 1_28]]] * batch_size

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
			SCREAMING_SNAKE_CASE       :     List[Any]				= self._generate_dummy_images(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= dict(
			    processor(
			        lowerCamelCase_		, text=lowerCamelCase_		, boxes=lowerCamelCase_		, return_tensors=lowerCamelCase_		, )					)

			return inputs





