





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


__lowerCamelCase			:	int					    =	logging.get_logger(__name__)

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



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[str]		       =  'layoutlmv3'





      def __init__(					self			,				A_=5_0265			,				A_=768			,				A_=12			,				A_=12			,				A_=3072			,				A_="gelu"			,				A_=0.1			,				A_=0.1			,				A_=512			,				A_=2			,				A_=0.02			,				A_=1e-5			,				A_=1			,				A_=0			,				A_=2			,				A_=1024			,				A_=128			,				A_=128			,				A_=True			,				A_=32			,				A_=128			,				A_=64			,				A_=256			,				A_=True			,				A_=True			,				A_=True			,				A_=224			,				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_			,				)
       UpperCamelCase      :   Tuple			      =  max_ad_position_embeddings
       UpperCamelCase      :   Any			      =  coordinate_size
       UpperCamelCase      :   Union[str, Any]			      =  shape_size
       UpperCamelCase      :   Optional[Any]			      =  has_relative_attention_bias
       UpperCamelCase      :   str			      =  rel_pos_bins
       UpperCamelCase      :   Dict			      =  max_rel_pos
       UpperCamelCase      :   List[str]			      =  has_spatial_attention_bias
       UpperCamelCase      :   Dict			      =  rel_ad_pos_bins
       UpperCamelCase      :   List[Any]			      =  max_rel_ad_pos
       UpperCamelCase      :   str			      =  text_embed
       UpperCamelCase      :   List[str]			      =  visual_embed
       UpperCamelCase      :   Dict			      =  input_size
       UpperCamelCase      :   Optional[Any]			      =  num_channels
       UpperCamelCase      :   str			      =  patch_size
       UpperCamelCase      :   str			      =  classifier_dropout



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Tuple		       =  version.parse('1.12'     )





      @property
      def 			__UpperCamelCase(					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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return 1e-5





      @property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return 12





      def 			__UpperCamelCase(					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
       UpperCamelCase      :   Optional[Any]			      =  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
       UpperCamelCase      :   Optional[Any]			      =  processor.tokenizer.num_special_tokens_to_add(A_  )
       UpperCamelCase      :   Union[str, Any]			      =  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
       UpperCamelCase      :   List[Any]			      =  [[" ".join([processor.tokenizer.unk_token]  ) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       UpperCamelCase      :   Optional[Any]			      =  [[[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)
       UpperCamelCase      :   Any			      =  self._generate_dummy_images(A_			,				A_			,				A_			,				A_  )

       UpperCamelCase      :   Any			      =  dict(
           processor(
               A_			,				text=A_			,				boxes=A_			,				return_tensors=A_			,				)  )

       return inputs







from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCamelCase			:	str					    =	{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_byta import ByTaTokenizer
else:
    import sys

    __lowerCamelCase			:	List[str]					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
