



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


UpperCamelCase__ =						logging.get_logger(__name__)

UpperCamelCase__ =						{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class 			_lowerCAmelCase   ( UpperCamelCase__			):






			"""simple docstring"""





			lowercase__      :  Any      =			"""layoutlmv3"""





			def __init__(				self	:       Dict			,     lowercase	:       Optional[Any]=50_265			,     lowercase	:       Dict=768			,     lowercase	:       str=12			,     lowercase	:       Union[str, Any]=12			,     lowercase	:       int=3_072			,     lowercase	:       Dict="gelu"			,     lowercase	:       List[Any]=0.1			,     lowercase	:       Optional[Any]=0.1			,     lowercase	:       Tuple=512			,     lowercase	:       str=2			,     lowercase	:       Dict=0.02			,     lowercase	:       List[Any]=1E-5			,     lowercase	:       List[str]=1			,     lowercase	:       Optional[Any]=0			,     lowercase	:       Any=2			,     lowercase	:       Optional[int]=1_024			,     lowercase	:       Any=128			,     lowercase	:       List[str]=128			,     lowercase	:       Dict=True			,     lowercase	:       Tuple=32			,     lowercase	:       Dict=128			,     lowercase	:       Optional[Any]=64			,     lowercase	:       Optional[int]=256			,     lowercase	:       List[Any]=True			,     lowercase	:       Tuple=True			,     lowercase	:       List[Any]=True			,     lowercase	:       List[Any]=224			,     lowercase	:       str=3			,     lowercase	:       Optional[Any]=16			,     lowercase	:       Tuple=None			,     **lowercase	:       Any			,     )						->					Optional[int]:
									"""simple docstring"""







									super().__init__(
									    vocab_size=lowercase			,     hidden_size=lowercase			,     num_hidden_layers=lowercase			,     num_attention_heads=lowercase			,     intermediate_size=lowercase			,     hidden_act=lowercase			,     hidden_dropout_prob=lowercase			,     attention_probs_dropout_prob=lowercase			,     max_position_embeddings=lowercase			,     type_vocab_size=lowercase			,     initializer_range=lowercase			,     layer_norm_eps=lowercase			,     pad_token_id=lowercase			,     bos_token_id=lowercase			,     eos_token_id=lowercase			,     **lowercase			,     )
									__lowercase					=				max_ad_position_embeddings
									__lowercase					=				coordinate_size
									__lowercase					=				shape_size
									__lowercase					=				has_relative_attention_bias
									__lowercase					=				rel_pos_bins
									__lowercase					=				max_rel_pos
									__lowercase					=				has_spatial_attention_bias
									__lowercase					=				rel_ad_pos_bins
									__lowercase					=				max_rel_ad_pos
									__lowercase					=				text_embed
									__lowercase					=				visual_embed
									__lowercase					=				input_size
									__lowercase					=				num_channels
									__lowercase					=				patch_size
									__lowercase					=				classifier_dropout





class 			_lowerCAmelCase   ( UpperCamelCase__			):






			"""simple docstring"""





			lowercase__      :  List[Any]      =			version.parse("""1.12"""			)





			@property
			def 		snake_case__			(				self	:       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 		snake_case__			(				self	:       Optional[int]   )						->					float:
									"""simple docstring"""







									return 1E-5





			@property
			def 		snake_case__			(				self	:       str   )						->					int:
									"""simple docstring"""







									return 12







			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       int			,     lowercase	:       Dict = -1			,     lowercase	:       Union[str, Any] = -1			,     lowercase	:       List[str] = False			,     lowercase	:       Any = None			,     lowercase	:       Any = 3			,     lowercase	:       List[str] = 40			,     lowercase	:       Optional[Any] = 40			,     )						->					Mapping[str, Any]:
									"""simple docstring"""







									setattr(processor.image_processor			,     """apply_ocr"""			,     lowercase   )

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									__lowercase					=				compute_effective_axis_dimension(
									    lowercase			,     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
									__lowercase					=				processor.tokenizer.num_special_tokens_to_add(lowercase   )
									__lowercase					=				compute_effective_axis_dimension(
									    lowercase			,     fixed_dimension=OnnxConfig.default_fixed_sequence			,     num_token_to_add=lowercase   )
									# Generate dummy inputs according to compute batch and sequence
									__lowercase					=				[[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

									# Generate dummy bounding boxes
									__lowercase					=				[[[48, 84, 73, 128]]] * 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)
									__lowercase					=				self._generate_dummy_images(lowercase			,     lowercase			,     lowercase			,     lowercase   )

									__lowercase					=				dict(
									    processor(
									        lowercase			,     text=lowercase			,     boxes=lowercase			,     return_tensors=lowercase			,     )   )

									return inputs










def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				sum(lowercase__   )

						__lowercase					=				[[False for x in range(s + 1   )] for y in range(n + 1   )]

						for i in range(1						,       n + 1   ):
												__lowercase					=				True

						for i in range(1						,       s + 1   ):
												__lowercase					=				False

						for i in range(1						,       n + 1   ):
												for j in range(1						,       s + 1   ):
																		__lowercase					=				dp[i][j - 1]

																		if arr[i - 1] <= j:
																								__lowercase					=				dp[i][j] or dp[i - 1][j - arr[i - 1]]

						for j in range(int(s / 2   )						,       -1						,       -1   ):
												if dp[n][j] is True:
																		__lowercase					=				s - 2 * j
																		break

						return diff





