code: 
  
  
  
  
  
  '''simple docstring'''
  
  import logging
  
  # standard libraries
  import sys
  
  import numpy as np
  import pytest  # type: ignore
  
  # Custom/local libraries
  from matrix import matrix_operation as matop
  
  __lowerCAmelCase							:			List[Any]				=[[12, 10], [3, 9]]
  __lowerCAmelCase							:			List[str]				=[[3, 4], [7, 4]]
  __lowerCAmelCase							:			List[Any]				=[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
  __lowerCAmelCase							:			Any				=[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
  __lowerCAmelCase							:			List[str]				=[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
  __lowerCAmelCase							:			str				=[1]
  __lowerCAmelCase							:			Dict				=[2]
  
  __lowerCAmelCase							:			Any				=logging.getLogger()
  __lowerCAmelCase							:			Union[str, Any]				=logging.DEBUG
  __lowerCAmelCase							:			Tuple				=logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  
  
  
  
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ("mat1", "mat2")		, [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] )
  def 		UpperCamelCase       (							_lowerCamelCase      :      Union[str, Any]		, _lowerCamelCase      :      Optional[Any] ):
      if (np.array(_lowerCamelCase )).shape < (2, 2) or (np.array(_lowerCamelCase )).shape < (2, 2):
          with pytest.raises(_lowerCamelCase ):
              logger.info(F"\n\t{test_addition.__name__} returned integer" )
              matop.add(_lowerCamelCase		, _lowerCamelCase )
      elif (np.array(_lowerCamelCase )).shape == (np.array(_lowerCamelCase )).shape:
          logger.info(F"\n\t{test_addition.__name__} with same matrix dims" )
          A__       =    (np.array(_lowerCamelCase ) + np.array(_lowerCamelCase )).tolist()
          A__       =    matop.add(_lowerCamelCase		, _lowerCamelCase )
          assert theo == act
      else:
          with pytest.raises(_lowerCamelCase ):
              logger.info(F"\n\t{test_addition.__name__} with different matrix dims" )
              matop.add(_lowerCamelCase		, _lowerCamelCase )
  
  
  
  
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ("mat1", "mat2")		, [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] )
  def 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      Optional[int] ):
      if (np.array(_lowerCamelCase )).shape < (2, 2) or (np.array(_lowerCamelCase )).shape < (2, 2):
          with pytest.raises(_lowerCamelCase ):
              logger.info(F"\n\t{test_subtraction.__name__} returned integer" )
              matop.subtract(_lowerCamelCase		, _lowerCamelCase )
      elif (np.array(_lowerCamelCase )).shape == (np.array(_lowerCamelCase )).shape:
          logger.info(F"\n\t{test_subtraction.__name__} with same matrix dims" )
          A__       =    (np.array(_lowerCamelCase ) - np.array(_lowerCamelCase )).tolist()
          A__       =    matop.subtract(_lowerCamelCase		, _lowerCamelCase )
          assert theo == act
      else:
          with pytest.raises(_lowerCamelCase ):
              logger.info(F"\n\t{test_subtraction.__name__} with different matrix dims" )
              assert matop.subtract(_lowerCamelCase		, _lowerCamelCase )
  
  
  
  
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ("mat1", "mat2")		, [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] )
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      str ):
      if (np.array(_lowerCamelCase )).shape < (2, 2) or (np.array(_lowerCamelCase )).shape < (2, 2):
          logger.info(F"\n\t{test_multiplication.__name__} returned integer" )
          with pytest.raises(_lowerCamelCase ):
              matop.add(_lowerCamelCase		, _lowerCamelCase )
      elif (np.array(_lowerCamelCase )).shape == (np.array(_lowerCamelCase )).shape:
          logger.info(F"\n\t{test_multiplication.__name__} meets dim requirements" )
          A__       =    (np.matmul(_lowerCamelCase		, _lowerCamelCase )).tolist()
          A__       =    matop.multiply(_lowerCamelCase		, _lowerCamelCase )
          assert theo == act
      else:
          with pytest.raises(_lowerCamelCase ):
              logger.info(
                  F"\n\t{test_multiplication.__name__} does not meet dim requirements" )
              assert matop.subtract(_lowerCamelCase		, _lowerCamelCase )
  
  
  
  
  
  @pytest.mark.mat_ops
  def 		UpperCamelCase       (							):
      A__       =    (3.5 * np.array(_lowerCamelCase )).tolist()
      A__       =    matop.scalar_multiply(_lowerCamelCase		, 3.5 )
      assert theo == act
  
  
  
  
  
  @pytest.mark.mat_ops
  def 		UpperCamelCase       (							):
      A__       =    (np.identity(5 )).tolist()
      A__       =    matop.identity(5 )
      assert theo == act
  
  
  
  
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize("mat"		, [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f] )
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any] ):
      if (np.array(_lowerCamelCase )).shape < (2, 2):
          with pytest.raises(_lowerCamelCase ):
              logger.info(F"\n\t{test_transpose.__name__} returned integer" )
              matop.transpose(_lowerCamelCase )
      else:
          A__       =    (np.transpose(_lowerCamelCase )).tolist()
          A__       =    matop.transpose(_lowerCamelCase		, return_map=_lowerCamelCase )
          assert theo == act
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  