"""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

__A       			=      [[12, 10], [3, 9]]
__A       			=      [[3, 4], [7, 4]]
__A       			=      [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
__A       			=      [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
__A       			=      [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
__A       			=      [1]
__A       			=      [2]

__A       			=      logging.getLogger()
__A       			=      logging.DEBUG
__A       			=      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   :							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   :							int				, _lowerCAmelCase   :							Tuple						):
    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   :							Tuple				, _lowerCAmelCase   :							Tuple						):
    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   :							Dict						):
    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_tokenizers_available, is_torch_available


__A       			=      {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      ["""RealmTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      [
     """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RealmEmbedder""",
     """RealmForOpenQA""",
     """RealmKnowledgeAugEncoder""",
     """RealmPreTrainedModel""",
     """RealmReader""",
     """RealmScorer""",
     """load_tf_weights_in_realm""",
 ]
 __A       			=      ["""RealmRetriever"""]


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 __A       			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
