






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= (EulerDiscreteScheduler,)
	lowerCAmelCase													= 1_0




	def 							_UpperCamelCase			(       self       ,			**a					)       ->    Optional[Any]:
								snake_case_				=							{
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**__lowerCAmelCase					)
								return config




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:






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




	def 							_UpperCamelCase			(       self					)       ->    List[str]:






								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,			[0.0_002, 0.002, 0.02]					):
															self.check_over_configs(beta_start=__lowerCAmelCase       ,			beta_end=__lowerCAmelCase					)




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






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




	def 							_UpperCamelCase			(       self					)       ->    List[str]:






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




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

								scheduler.set_timesteps(self.num_inference_steps					)

								snake_case_				=							torch.manual_seed(0					)

								snake_case_				=							self.dummy_model()
								snake_case_				=							self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_				=							sample.to(__lowerCAmelCase					)

								for i, t in enumerate(scheduler.timesteps					):
															snake_case_				=							scheduler.scale_model_input(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							model(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							scheduler.step(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			generator=__lowerCAmelCase					)
															snake_case_				=							output.prev_sample

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

								assert abs(result_sum.item() - 10.08_07					) < 1E-2
								assert abs(result_mean.item() - 0.0_131					) < 1E-3




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_				=							self.scheduler_classes[0]
								snake_case_				=							self.get_scheduler_config(prediction_type='v_prediction'					)
								snake_case_				=							scheduler_class(**__lowerCAmelCase					)

								scheduler.set_timesteps(self.num_inference_steps					)

								snake_case_				=							torch.manual_seed(0					)

								snake_case_				=							self.dummy_model()
								snake_case_				=							self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_				=							sample.to(__lowerCAmelCase					)

								for i, t in enumerate(scheduler.timesteps					):
															snake_case_				=							scheduler.scale_model_input(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							model(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							scheduler.step(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			generator=__lowerCAmelCase					)
															snake_case_				=							output.prev_sample

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

								assert abs(result_sum.item() - 0.0_002					) < 1E-2
								assert abs(result_mean.item() - 2.2_676E-06					) < 1E-3




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.scheduler_classes[0]
								snake_case_				=							self.get_scheduler_config()
								snake_case_				=							scheduler_class(**__lowerCAmelCase					)

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

								snake_case_				=							torch.manual_seed(0					)

								snake_case_				=							self.dummy_model()
								snake_case_				=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								snake_case_				=							sample.to(__lowerCAmelCase					)

								for t in scheduler.timesteps:
															snake_case_				=							scheduler.scale_model_input(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							model(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							scheduler.step(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			generator=__lowerCAmelCase					)
															snake_case_				=							output.prev_sample

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

								assert abs(result_sum.item() - 10.08_07					) < 1E-2
								assert abs(result_mean.item() - 0.0_131					) < 1E-3






	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.scheduler_classes[0]
								snake_case_				=							self.get_scheduler_config()
								snake_case_				=							scheduler_class(**__lowerCAmelCase       ,			use_karras_sigmas=__lowerCAmelCase					)

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

								snake_case_				=							torch.manual_seed(0					)

								snake_case_				=							self.dummy_model()
								snake_case_				=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								snake_case_				=							sample.to(__lowerCAmelCase					)

								for t in scheduler.timesteps:
															snake_case_				=							scheduler.scale_model_input(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							model(__lowerCAmelCase       ,			__lowerCAmelCase					)

															snake_case_				=							scheduler.step(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			generator=__lowerCAmelCase					)
															snake_case_				=							output.prev_sample

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

								assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19					) < 1E-2
								assert abs(result_mean.item() - 0.16_213_932_633_399_963					) < 1E-3












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


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






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







					"""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							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






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







					"""simple docstring"""






					return 1e-5






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







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__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
					A__        =   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
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   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
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[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)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



