


'''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


lowercase__				     =		logging.get_logger(__name__)

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





class 						__SCREAMING_SNAKE_CASE					(      __UpperCAmelCase ):
     UpperCAmelCase 					=				'''layoutlmv3'''






     def __init__(			self     ,							__UpperCamelCase=50_265     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__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=1_024     ,							__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     ,							)       ->    List[str]:
          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     ,							)
          _a						=		max_ad_position_embeddings
          _a						=		coordinate_size
          _a						=		shape_size
          _a						=		has_relative_attention_bias
          _a						=		rel_pos_bins
          _a						=		max_rel_pos
          _a						=		has_spatial_attention_bias
          _a						=		rel_ad_pos_bins
          _a						=		max_rel_ad_pos
          _a						=		text_embed
          _a						=		visual_embed
          _a						=		input_size
          _a						=		num_channels
          _a						=		patch_size
          _a						=		classifier_dropout







class 						__SCREAMING_SNAKE_CASE					(      __UpperCAmelCase ):
     UpperCAmelCase 					=				version.parse('''1.12''' )






     @property
     def  a_					(			self		)       ->    Optional[int]:

          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		)       ->    Any:
          return 1e-5






     @property
     def  a_					(			self		)       ->    Any:
          return 12






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = -1     ,							__UpperCamelCase = -1     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 3     ,							__UpperCamelCase = 40     ,							__UpperCamelCase = 40     ,							)       ->    Optional[int]:
          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
          _a						=		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
          _a						=		processor.tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE		)
          _a						=		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
          _a						=		[[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          _a						=		[[[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)
          _a						=		self._generate_dummy_images(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE		)

          _a						=		dict(
              processor(
                  __SCREAMING_SNAKE_CASE     ,							text=__SCREAMING_SNAKE_CASE     ,							boxes=__SCREAMING_SNAKE_CASE     ,							return_tensors=__SCREAMING_SNAKE_CASE     ,							)		)

          return inputs









'''simple docstring'''

lowercase__				     =		65_521






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     int:







     '''simple docstring'''






     _a						=		1
     _a						=		0
     for plain_chr in plain_text:
          _a						=		(a + ord(__lowerCamelCase				)) % MOD_ADLER
          _a						=		(b + a) % MOD_ADLER
     return (b << 16) | a

