





'''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










'''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__)



