


'''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


__A	:	str     =     logging.get_logger(__name__)

__A	:	Optional[int]     =     {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict   		=  'layoutlmv3'





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:Union[str, Any]=5_0_2_6_5  ,_UpperCamelCase		:Tuple=7_6_8  ,_UpperCamelCase		:Dict=1_2  ,_UpperCamelCase		:str=1_2  ,_UpperCamelCase		:List[Any]=3_0_7_2  ,_UpperCamelCase		:str="gelu"  ,_UpperCamelCase		:str=0.1  ,_UpperCamelCase		:Dict=0.1  ,_UpperCamelCase		:Optional[Any]=5_1_2  ,_UpperCamelCase		:Any=2  ,_UpperCamelCase		:List[Any]=0.02  ,_UpperCamelCase		:Optional[int]=1E-5  ,_UpperCamelCase		:Union[str, Any]=1  ,_UpperCamelCase		:Any=0  ,_UpperCamelCase		:Union[str, Any]=2  ,_UpperCamelCase		:Optional[int]=1_0_2_4  ,_UpperCamelCase		:List[str]=1_2_8  ,_UpperCamelCase		:str=1_2_8  ,_UpperCamelCase		:Union[str, Any]=True  ,_UpperCamelCase		:Union[str, Any]=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,_UpperCamelCase		:Optional[int]=6_4  ,_UpperCamelCase		:Optional[Any]=2_5_6  ,_UpperCamelCase		:Any=True  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:str=True  ,_UpperCamelCase		:Tuple=2_2_4  ,_UpperCamelCase		:List[Any]=3  ,_UpperCamelCase		:Any=1_6  ,_UpperCamelCase		:str=None  ,**_UpperCamelCase		:Dict  ,):
									super().__init__(
									    vocab_size=_UpperCamelCase  ,hidden_size=_UpperCamelCase  ,num_hidden_layers=_UpperCamelCase  ,num_attention_heads=_UpperCamelCase  ,intermediate_size=_UpperCamelCase  ,hidden_act=_UpperCamelCase  ,hidden_dropout_prob=_UpperCamelCase  ,attention_probs_dropout_prob=_UpperCamelCase  ,max_position_embeddings=_UpperCamelCase  ,type_vocab_size=_UpperCamelCase  ,initializer_range=_UpperCamelCase  ,layer_norm_eps=_UpperCamelCase  ,pad_token_id=_UpperCamelCase  ,bos_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase  ,**_UpperCamelCase  ,)
									snake_case_		:							Any			       =						max_ad_position_embeddings
									snake_case_		:							Optional[Any]			       =						coordinate_size
									snake_case_		:							str			       =						shape_size
									snake_case_		:							int			       =						has_relative_attention_bias
									snake_case_		:							Optional[Any]			       =						rel_pos_bins
									snake_case_		:							str			       =						max_rel_pos
									snake_case_		:							Optional[int]			       =						has_spatial_attention_bias
									snake_case_		:							Optional[Any]			       =						rel_ad_pos_bins
									snake_case_		:							Tuple			       =						max_rel_ad_pos
									snake_case_		:							Dict			       =						text_embed
									snake_case_		:							List[str]			       =						visual_embed
									snake_case_		:							List[Any]			       =						input_size
									snake_case_		:							Optional[Any]			       =						num_channels
									snake_case_		:							List[str]			       =						patch_size
									snake_case_		:							Union[str, Any]			       =						classifier_dropout





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Tuple   		=  version.parse('1.12'							)





							@property
							def 		a__   (  self		:Optional[Any]			):
									# The order of inputs is different for question answering and sequence classification
									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		:List[Any]			):
									return 1E-5





							@property
							def 		a__   (  self		:Optional[int]			):
									return 1_2





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:"ProcessorMixin"  ,_UpperCamelCase		:int = -1  ,_UpperCamelCase		:int = -1  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:Optional["TensorType"] = None  ,_UpperCamelCase		:int = 3  ,_UpperCamelCase		:int = 4_0  ,_UpperCamelCase		:int = 4_0  ,):
									setattr(processor.image_processor  ,"""apply_ocr"""  ,_UpperCamelCase			)

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

									# Generate dummy bounding boxes
									snake_case_		:							Union[str, Any]			       =						[[[4_8, 8_4, 7_3, 1_2_8]]] * 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_		:							Dict			       =						self._generate_dummy_images(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)

									snake_case_		:							int			       =						dict(
									    processor(
									        _UpperCamelCase  ,text=_UpperCamelCase  ,boxes=_UpperCamelCase  ,return_tensors=_UpperCamelCase  ,)			)

									return inputs



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))