


"""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


SCREAMING_SNAKE_CASE :    int									=				logging.get_logger(__name__)

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



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='layoutlmv3'



		def __init__(self				,       a_=5_02_65				,       a_=7_68				,       a_=12				,       a_=12				,       a_=30_72				,       a_="gelu"				,       a_=0.1				,       a_=0.1				,       a_=5_12				,       a_=2				,       a_=0.02				,       a_=1E-5				,       a_=1				,       a_=0				,       a_=2				,       a_=10_24				,       a_=1_28				,       a_=1_28				,       a_=True				,       a_=32				,       a_=1_28				,       a_=64				,       a_=2_56				,       a_=True				,       a_=True				,       a_=True				,       a_=2_24				,       a_=3				,       a_=16				,       a_=None				,       **a_				,       ):





			'''simple docstring'''

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



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=version.parse('1.12'				)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1E-5



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 12



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = -1				,       a_ = -1				,       a_ = False				,       a_ = None				,       a_ = 3				,       a_ = 40				,       a_ = 40				,       ):





			'''simple docstring'''

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

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			__snake_case	:						List[str]						=				compute_effective_axis_dimension(
			    a_				,       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
			__snake_case	:						Any						=				processor.tokenizer.num_special_tokens_to_add(a_					)
			__snake_case	:						Tuple						=				compute_effective_axis_dimension(
			    a_				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=a_					)
			# Generate dummy inputs according to compute batch and sequence
			__snake_case	:						Optional[int]						=				[[''' '''.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

			# Generate dummy bounding boxes
			__snake_case	:						Any						=				[[[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)
			__snake_case	:						int						=				self._generate_dummy_images(a_				,       a_				,       a_				,       a_					)

			__snake_case	:						Tuple						=				dict(
			    processor(
			        a_				,       text=a_				,       boxes=a_				,       return_tensors=a_				,       )					)

			return inputs









"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE :    Dict									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Optional[int]									=				{
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='yolos'



		def __init__(self				,       a_=7_68				,       a_=12				,       a_=12				,       a_=30_72				,       a_="gelu"				,       a_=0.0				,       a_=0.0				,       a_=0.02				,       a_=1E-12				,       a_=[5_12, 8_64]				,       a_=16				,       a_=3				,       a_=True				,       a_=1_00				,       a_=True				,       a_=False				,       a_=1				,       a_=5				,       a_=2				,       a_=5				,       a_=2				,       a_=0.1				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)

			__snake_case	:						Union[str, Any]						=				hidden_size
			__snake_case	:						Union[str, Any]						=				num_hidden_layers
			__snake_case	:						Dict						=				num_attention_heads
			__snake_case	:						str						=				intermediate_size
			__snake_case	:						List[str]						=				hidden_act
			__snake_case	:						Tuple						=				hidden_dropout_prob
			__snake_case	:						Optional[int]						=				attention_probs_dropout_prob
			__snake_case	:						Union[str, Any]						=				initializer_range
			__snake_case	:						Tuple						=				layer_norm_eps
			__snake_case	:						List[Any]						=				image_size
			__snake_case	:						Tuple						=				patch_size
			__snake_case	:						str						=				num_channels
			__snake_case	:						Tuple						=				qkv_bias
			__snake_case	:						Union[str, Any]						=				num_detection_tokens
			__snake_case	:						List[str]						=				use_mid_position_embeddings
			__snake_case	:						Tuple						=				auxiliary_loss
			# Hungarian matcher
			__snake_case	:						List[str]						=				class_cost
			__snake_case	:						int						=				bbox_cost
			__snake_case	:						int						=				giou_cost
			# Loss coefficients
			__snake_case	:						Optional[int]						=				bbox_loss_coefficient
			__snake_case	:						List[str]						=				giou_loss_coefficient
			__snake_case	:						List[Any]						=				eos_coefficient



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=version.parse('1.11'				)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return OrderedDict(
			    [
			        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
			    ]					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1E-4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 12





