




'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			["""image_processor""", """tokenizer"""]
			__SCREAMING_SNAKE_CASE    =			"""LayoutLMv3ImageProcessor"""
			__SCREAMING_SNAKE_CASE    =			("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")





			def __init__(      self						:    str      ,		a_						:    Dict=None      ,		a_						:    Dict=None      ,		**a_						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
									    " instead."      ,		a_      ,		)
									__snake_case			=		kwargs.pop("feature_extractor"							)

						__snake_case			=		image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("You need to specify an `image_processor`."							)
						if tokenizer is None:
									raise ValueError("You need to specify a `tokenizer`."							)

						super().__init__(a_      ,		a_							)





			def __call__(      self						:    Any      ,		a_						:    Union[str, Any]      ,		a_						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		a_						:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		a_						:    Union[List[List[int]], List[List[List[int]]]] = None      ,		a_						:    Optional[Union[List[int], List[List[int]]]] = None      ,		a_						:    bool = True      ,		a_						:    Union[bool, str, PaddingStrategy] = False      ,		a_						:    Union[bool, str, TruncationStrategy] = None      ,		a_						:    Optional[int] = None      ,		a_						:    int = 0      ,		a_						:    Optional[int] = None      ,		a_						:    Optional[bool] = None      ,		a_						:    Optional[bool] = None      ,		a_						:    bool = False      ,		a_						:    bool = False      ,		a_						:    bool = False      ,		a_						:    bool = False      ,		a_						:    bool = True      ,		a_						:    Optional[Union[str, TensorType]] = None      ,		**a_						:    Tuple      ,		):



						"""simple docstring"""




						if self.image_processor.apply_ocr and (boxes is not None):
									raise ValueError(
									    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."							)

						if self.image_processor.apply_ocr and (word_labels is not None):
									raise ValueError(
									    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."							)

						# first, apply the image processor
						__snake_case			=		self.image_processor(images=a_      ,		return_tensors=a_							)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(a_      ,		a_							):
												__snake_case			=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
									__snake_case			=		features["words"]

						__snake_case			=		self.tokenizer(
						    text=text if text is not None else features["words"]      ,		text_pair=text_pair if text_pair is not None else None      ,		boxes=boxes if boxes is not None else features["boxes"]      ,		word_labels=a_      ,		add_special_tokens=a_      ,		padding=a_      ,		truncation=a_      ,		max_length=a_      ,		stride=a_      ,		pad_to_multiple_of=a_      ,		return_token_type_ids=a_      ,		return_attention_mask=a_      ,		return_overflowing_tokens=a_      ,		return_special_tokens_mask=a_      ,		return_offsets_mapping=a_      ,		return_length=a_      ,		verbose=a_      ,		return_tensors=a_      ,		**a_      ,		)

						# add pixel values
						__snake_case			=		features.pop("pixel_values"							)
						if return_overflowing_tokens is True:
									__snake_case			=		self.get_overflowing_images(a_      ,		encoded_inputs["overflow_to_sample_mapping"]							)
						__snake_case			=		images

						return encoded_inputs





			def 							A (      self						:    List[Any]      ,		a_						:    int      ,		a_						:    int							):



						"""simple docstring"""




						__snake_case			=		[]
						for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]							)

						if len(a_							) != len(a_							):
									raise ValueError(
									    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
									    f''' {len(a_							)} and {len(a_							)}'''							)

						return images_with_overflow





			def 							A (      self						:    str      ,		*a_						:    str      ,		**a_						:    List[str]							):



						"""simple docstring"""




						return self.tokenizer.batch_decode(*a_      ,		**a_							)





			def 							A (      self						:    str      ,		*a_						:    int      ,		**a_						:    Tuple							):



						"""simple docstring"""




						return self.tokenizer.decode(*a_      ,		**a_							)





			@property
			def 							A (      self						:    Dict							):



						"""simple docstring"""




						return ["input_ids", "bbox", "attention_mask", "pixel_values"]





			@property
			def 							A (      self						:    int							):



						"""simple docstring"""




						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,		a_      ,		)
						return self.image_processor_class






			@property
			def 							A (      self						:    Dict							):



						"""simple docstring"""




						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,		a_      ,		)
						return self.image_processor










'''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  			=						logging.get_logger(__name__)

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







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"layoutlmv3"




    def __init__(       self   :	Any					,	snake_case_   :	Dict=50_265					,	snake_case_   :	Union[str, Any]=768					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	Optional[int]=12					,	snake_case_   :	Union[str, Any]=3_072					,	snake_case_   :	List[str]="gelu"					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=512					,	snake_case_   :	List[str]=2					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	List[Any]=1E-5					,	snake_case_   :	Any=1					,	snake_case_   :	Any=0					,	snake_case_   :	int=2					,	snake_case_   :	Optional[Any]=1_024					,	snake_case_   :	int=128					,	snake_case_   :	Union[str, Any]=128					,	snake_case_   :	int=True					,	snake_case_   :	Tuple=32					,	snake_case_   :	str=128					,	snake_case_   :	Optional[int]=64					,	snake_case_   :	Optional[Any]=256					,	snake_case_   :	List[str]=True					,	snake_case_   :	str=True					,	snake_case_   :	Dict=True					,	snake_case_   :	int=224					,	snake_case_   :	Any=3					,	snake_case_   :	int=16					,	snake_case_   :	Tuple=None					,	**snake_case_   :	Optional[Any]					,	):
          super().__init__(
              vocab_size=snake_case_					,	hidden_size=snake_case_					,	num_hidden_layers=snake_case_					,	num_attention_heads=snake_case_					,	intermediate_size=snake_case_					,	hidden_act=snake_case_					,	hidden_dropout_prob=snake_case_					,	attention_probs_dropout_prob=snake_case_					,	max_position_embeddings=snake_case_					,	type_vocab_size=snake_case_					,	initializer_range=snake_case_					,	layer_norm_eps=snake_case_					,	pad_token_id=snake_case_					,	bos_token_id=snake_case_					,	eos_token_id=snake_case_					,	**snake_case_					,	)
          snake_case__    :       List[Any]       		= max_ad_position_embeddings
          snake_case__    :       Optional[int]       		= coordinate_size
          snake_case__    :       int       		= shape_size
          snake_case__    :       Optional[int]       		= has_relative_attention_bias
          snake_case__    :       Dict       		= rel_pos_bins
          snake_case__    :       List[Any]       		= max_rel_pos
          snake_case__    :       str       		= has_spatial_attention_bias
          snake_case__    :       Optional[Any]       		= rel_ad_pos_bins
          snake_case__    :       Optional[int]       		= max_rel_ad_pos
          snake_case__    :       Union[str, Any]       		= text_embed
          snake_case__    :       List[Any]       		= visual_embed
          snake_case__    :       Union[str, Any]       		= input_size
          snake_case__    :       int       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Dict       		= classifier_dropout







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          # 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 lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	"ProcessorMixin"					,	snake_case_   :	int = -1					,	snake_case_   :	int = -1					,	snake_case_   :	bool = False					,	snake_case_   :	Optional["TensorType"] = None					,	snake_case_   :	int = 3					,	snake_case_   :	int = 40					,	snake_case_   :	int = 40					,	):
          setattr(processor.image_processor					,	"""apply_ocr"""					,	snake_case_		)

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

          # Generate dummy bounding boxes
          snake_case__    :       int       		= [[[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)
          snake_case__    :       Optional[int]       		= self._generate_dummy_images(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= dict(
              processor(
                  snake_case_					,	text=snake_case_					,	boxes=snake_case_					,	return_tensors=snake_case_					,	)		)

          return inputs
