





def A_     (		A__    )  ->		list[list]:
       a__						:     List[Any]			=    current_set.copy()
       for row_index, row in enumerate(A__    ):
              a__						:     int			=    row[0]
              for column_index, column in enumerate(A__    ):
                     if magnitude == 0:
                            a__						:     Tuple			=    column
                            continue
                     a__						:     int			=    column / magnitude
    # Subtract to cancel term
       a__						:     Optional[Any]			=    current_set[0]
       a__						:     Union[str, Any]			=    [first_row]
       a__						:     List[str]			=    current_set[1::]
       for row in current_set:
              a__						:     Dict			=    []
              # If first term is 0, it is already in form we want, so we preserve it
              if row[0] == 0:
                     final_set.append(A__    )
                     continue
              for column_index in range(len(A__    )    ):
                     temp_row.append(first_row[column_index] - row[column_index]    )
              final_set.append(A__    )
       # Create next recursion iteration set
       if len(final_set[0]    ) != 3:
              a__						:     str			=    final_set[0]
              a__						:     List[str]			=    []
              a__						:     Optional[int]			=    []
              for row in final_set[1::]:
                     current_first_column.append(row[0]    )
                     next_iteration.append(row[1::]    )
              a__						:     Optional[int]			=    simplify(A__    )
              for i in range(len(A__    )    ):
                     resultant[i].insert(0		,				current_first_column[i]    )
              resultant.insert(0		,				A__    )
              a__						:     Optional[int]			=    resultant
       return final_set



def A_     (		A__    )  ->		list:
       if len(A__    ) == 0:
              raise IndexError('solve_simultaneous() requires n lists of length n+1'    )
       a__						:     str			=    len(A__    ) + 1
       if any(len(A__    ) != _length for item in equations    ):
              raise IndexError('solve_simultaneous() requires n lists of length n+1'    )
       for row in equations:
              if any(not isinstance(A__		,				(int, float)    ) for column in row    ):
                     raise ValueError('solve_simultaneous() requires lists of integers'    )
       if len(A__    ) == 1:
              return [equations[0][-1] / equations[0][0]]
       a__						:     Tuple			=    equations.copy()
       if any(0 in row for row in data_set    ):
              a__						:     Dict			=    data_set.copy()
              a__						:     Dict			=    []
              for row_index, row in enumerate(A__    ):
                     if 0 not in row:
                            a__						:     str			=    data_set.pop(A__    )
                            break
              if not full_row:
                     raise ValueError('solve_simultaneous() requires at least 1 full equation'    )
              data_set.insert(0		,				A__    )
       a__						:     List[str]			=    data_set.copy()
       a__						:     Any			=    simplify(A__    )
       a__						:     Optional[Any]			=    simplified[::-1]
       a__						:     list			=    []
       for row in simplified:
              a__						:     int			=    row[-1]
              if not solutions:
                     if row[-2] == 0:
                            solutions.append(0    )
                            continue
                     solutions.append(current_solution / row[-2]    )
                     continue
              a__						:     Tuple			=    row.copy()[: len(A__    ) - 1 :]
              while temp_row[0] == 0:
                     temp_row.pop(0    )
              if len(A__    ) == 0:
                     solutions.append(0    )
                     continue
              a__						:     str			=    temp_row[1::]
              a__						:     Any			=    temp_row[::-1]
              for column_index, column in enumerate(A__    ):
                     current_solution -= column * solutions[column_index]
              solutions.append(A__    )
       a__						:     Optional[Any]			=    []
       for item in solutions:
              final.append(float(round(A__		,				5    )    )    )
       return final[::-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    lowercase				:	List[Any]      						=  [
        [2, 1, 1, 1, 1, 4],
        [1, 2, 1, 1, 1, 5],
        [1, 1, 2, 1, 1, 6],
        [1, 1, 1, 2, 1, 7],
        [1, 1, 1, 1, 2, 8],
    ]
    print(solve_simultaneous(eq))
    print(solve_simultaneous([[4, 2]]))









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase				:	List[str]      						=  {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Tuple      						=  ["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""CLIPFeatureExtractor"""]
    lowercase				:	Union[str, Any]      						=  ["""CLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Dict      						=  [
        """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CLIPModel""",
        """CLIPPreTrainedModel""",
        """CLIPTextModel""",
        """CLIPTextModelWithProjection""",
        """CLIPVisionModel""",
        """CLIPVisionModelWithProjection""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  [
        """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCLIPModel""",
        """TFCLIPPreTrainedModel""",
        """TFCLIPTextModel""",
        """TFCLIPVisionModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Any      						=  [
        """FlaxCLIPModel""",
        """FlaxCLIPPreTrainedModel""",
        """FlaxCLIPTextModel""",
        """FlaxCLIPTextPreTrainedModel""",
        """FlaxCLIPVisionModel""",
        """FlaxCLIPVisionPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

    lowercase				:	List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


