

"""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


__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class        _lowercase					(     UpperCamelCase__     ):
     _lowerCamelCase     =       """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_=0.0_2 ,						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_ ,						):
      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_ ,						)
      __magic_name__     =      max_ad_position_embeddings
      __magic_name__     =      coordinate_size
      __magic_name__     =      shape_size
      __magic_name__     =      has_relative_attention_bias
      __magic_name__     =      rel_pos_bins
      __magic_name__     =      max_rel_pos
      __magic_name__     =      has_spatial_attention_bias
      __magic_name__     =      rel_ad_pos_bins
      __magic_name__     =      max_rel_ad_pos
      __magic_name__     =      text_embed
      __magic_name__     =      visual_embed
      __magic_name__     =      input_size
      __magic_name__     =      num_channels
      __magic_name__     =      patch_size
      __magic_name__     =      classifier_dropout


class        _lowercase					(     UpperCamelCase__     ):
     _lowerCamelCase     =       version.parse('''1.12'''     )







     @property
     def 	lowerCAmelCase__    (      self       ):
      # 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       ):
      return 1E-5







     @property
     def 	lowerCAmelCase__    (      self       ):
      return 12







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = -1 ,						UpperCamelCase_ = -1 ,						UpperCamelCase_ = False ,						UpperCamelCase_ = None ,						UpperCamelCase_ = 3 ,						UpperCamelCase_ = 40 ,						UpperCamelCase_ = 40 ,						):
      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
      __magic_name__     =      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
      __magic_name__     =      processor.tokenizer.num_special_tokens_to_add(UpperCamelCase_       )
      __magic_name__     =      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
      __magic_name__     =      [[''' '''.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

      # Generate dummy bounding boxes
      __magic_name__     =      [[[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)
      __magic_name__     =      self._generate_dummy_images(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )

      __magic_name__     =      dict(
          processor(
              UpperCamelCase_ ,						text=UpperCamelCase_ ,						boxes=UpperCamelCase_ ,						return_tensors=UpperCamelCase_ ,						)       )

      return inputs




"""simple docstring"""






import pytest

import datasets


# Import fixture modules as plugins
__lowerCamelCase										=		["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
 for item in items:
  if any(marker in item.keywords for marker in ['''integration''', '''unit''']						):
   continue
  item.add_marker(pytest.mark.unit						)





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 config.addinivalue_line('''markers'''      ,   '''torchaudio_latest: mark test to run with torchaudio>=0.12'''						)





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Dict:
 # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
 __magic_name__     =      tmp_path_factory.getbasetemp() / '''cache'''
 __magic_name__     =      test_hf_cache_home / '''datasets'''
 __magic_name__     =      test_hf_cache_home / '''metrics'''
 __magic_name__     =      test_hf_cache_home / '''modules'''
 monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads'''
 monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads''' / '''extracted'''
 monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)





@pytest.fixture(autouse=__UpperCamelCase      ,   scope='''session'''						)
def 	lowercase      (   ) ->      Any:
 datasets.disable_progress_bar()





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase						) ->      str:
 # don't take tests into account when counting downloads
 monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''      ,   __UpperCamelCase						)





@pytest.fixture
def 	lowercase      (   __UpperCamelCase						) ->      int:
 # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
 # To be removed once SQLAlchemy 2.0 supported
 monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''      ,   __UpperCamelCase						)

