




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__			: Optional[int]    =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__			: List[Any]    =  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}




class 						UpperCamelCase__      (_lowerCAmelCase	):

    '''simple docstring'''



    lowerCamelCase_ :      int      	= "layoutlmv3"







    def __init__(       self	,    UpperCamelCase__=5_0265	,    UpperCamelCase__=768	,    UpperCamelCase__=12	,    UpperCamelCase__=12	,    UpperCamelCase__=3072	,    UpperCamelCase__="gelu"	,    UpperCamelCase__=0.1	,    UpperCamelCase__=0.1	,    UpperCamelCase__=512	,    UpperCamelCase__=2	,    UpperCamelCase__=0.02	,    UpperCamelCase__=1e-5	,    UpperCamelCase__=1	,    UpperCamelCase__=0	,    UpperCamelCase__=2	,    UpperCamelCase__=1024	,    UpperCamelCase__=128	,    UpperCamelCase__=128	,    UpperCamelCase__=True	,    UpperCamelCase__=32	,    UpperCamelCase__=128	,    UpperCamelCase__=64	,    UpperCamelCase__=256	,    UpperCamelCase__=True	,    UpperCamelCase__=True	,    UpperCamelCase__=True	,    UpperCamelCase__=224	,    UpperCamelCase__=3	,    UpperCamelCase__=16	,    UpperCamelCase__=None	,    **UpperCamelCase__	,    )	-> Any:
           super().__init__(
               vocab_size=SCREAMING_SNAKE_CASE__	,    hidden_size=SCREAMING_SNAKE_CASE__	,    num_hidden_layers=SCREAMING_SNAKE_CASE__	,    num_attention_heads=SCREAMING_SNAKE_CASE__	,    intermediate_size=SCREAMING_SNAKE_CASE__	,    hidden_act=SCREAMING_SNAKE_CASE__	,    hidden_dropout_prob=SCREAMING_SNAKE_CASE__	,    attention_probs_dropout_prob=SCREAMING_SNAKE_CASE__	,    max_position_embeddings=SCREAMING_SNAKE_CASE__	,    type_vocab_size=SCREAMING_SNAKE_CASE__	,    initializer_range=SCREAMING_SNAKE_CASE__	,    layer_norm_eps=SCREAMING_SNAKE_CASE__	,    pad_token_id=SCREAMING_SNAKE_CASE__	,    bos_token_id=SCREAMING_SNAKE_CASE__	,    eos_token_id=SCREAMING_SNAKE_CASE__	,    **SCREAMING_SNAKE_CASE__	,    )
           lowerCamelCase  : Tuple     					=   max_ad_position_embeddings
           lowerCamelCase  : Optional[Any]     					=   coordinate_size
           lowerCamelCase  : Tuple     					=   shape_size
           lowerCamelCase  : Tuple     					=   has_relative_attention_bias
           lowerCamelCase  : List[str]     					=   rel_pos_bins
           lowerCamelCase  : str     					=   max_rel_pos
           lowerCamelCase  : Union[str, Any]     					=   has_spatial_attention_bias
           lowerCamelCase  : Optional[int]     					=   rel_ad_pos_bins
           lowerCamelCase  : Optional[int]     					=   max_rel_ad_pos
           lowerCamelCase  : str     					=   text_embed
           lowerCamelCase  : Union[str, Any]     					=   visual_embed
           lowerCamelCase  : Optional[Any]     					=   input_size
           lowerCamelCase  : Any     					=   num_channels
           lowerCamelCase  : Any     					=   patch_size
           lowerCamelCase  : List[Any]     					=   classifier_dropout




class 						UpperCamelCase__      (_lowerCAmelCase	):

    '''simple docstring'''



    lowerCamelCase_ :      Union[str, Any]      	= version.parse("""1.12"""	)







    @property
    def        _lowercase	(       self	)	-> Mapping[str, Mapping[int, str]]:
           # 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        _lowercase	(       self	)	-> float:
           return 1e-5







    @property
    def        _lowercase	(       self	)	-> int:
           return 12







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__ = -1	,    UpperCamelCase__ = -1	,    UpperCamelCase__ = False	,    UpperCamelCase__ = None	,    UpperCamelCase__ = 3	,    UpperCamelCase__ = 40	,    UpperCamelCase__ = 40	,    )	-> Mapping[str, Any]:
           setattr(processor.image_processor	,    "apply_ocr"	,    SCREAMING_SNAKE_CASE__	)

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

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

           lowerCamelCase  : Union[str, Any]     					=   dict(
               processor(
                   SCREAMING_SNAKE_CASE__	,    text=SCREAMING_SNAKE_CASE__	,    boxes=SCREAMING_SNAKE_CASE__	,    return_tensors=SCREAMING_SNAKE_CASE__	,    )	)

           return inputs











"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       bytes     )	-> str:






	"""simple docstring"""





	return "".join([hex(UpperCAmelCase_     )[2:].zfill(2     ).upper() for byte in list(UpperCAmelCase_     )]     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> bytes:






	"""simple docstring"""





	if (len(UpperCAmelCase_     ) % 2) != 0:
		raise ValueError(
		    "Base16 encoded data is invalid:\nData does not have an even number of hex digits."     )
	# Check the character set - the standard base16 alphabet
	# is uppercase according to RFC3548 section 6
	if not set(UpperCAmelCase_     ) <= set("0123456789ABCDEF"     ):
		raise ValueError(
		    "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."     )
	# For every two hexadecimal digits (= a byte), turn it into an integer.
	# Then, string the result together into bytes, and return it.
	return bytes(int(data[i] + data[i + 1],	16     ) for i in range(0,	len(UpperCAmelCase_     ),	2     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()




