




'''simple docstring'''





def UpperCAmelCase    (       UpperCAmelCase_						):
    # must be
    _a				:							Optional[Any]							       =      [list(UpperCAmelCase_						) for row in matrix]
    if matrix and isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        if isinstance(matrix[0]   ,	UpperCAmelCase_						):
            _a				:							int							       =      0
            for row in matrix:
                if prev_len == 0:
                    _a				:							Any							       =      len(UpperCAmelCase_						)
                    _a				:							Optional[Any]							       =      True
                else:
                    _a				:							List[Any]							       =      prev_len == len(UpperCAmelCase_						)
        else:
            _a				:							Dict							       =      True
    else:
        _a				:							Dict							       =      False

    return result





def UpperCAmelCase    (       UpperCAmelCase_						):
    if check_matrix(UpperCAmelCase_						) and len(UpperCAmelCase_						) > 0:
        _a				:							Tuple							       =      [list(UpperCAmelCase_						) for row in a]
        _a				:							Union[str, Any]							       =      len(UpperCAmelCase_						)
        if isinstance(a[0]   ,	UpperCAmelCase_						):
            _a				:							Union[str, Any]							       =      len(a[0]						)
        else:
            for dat in a:
                print(UpperCAmelCase_						)
            return

        # horizotal printing increasing
        for i in range(0   ,	UpperCAmelCase_						):
            print(a[0][i]						)
        # vertical printing down
        for i in range(1   ,	UpperCAmelCase_						):
            print(a[i][mat_col - 1]						)
        # horizotal printing decreasing
        if mat_row > 1:
            for i in range(mat_col - 2   ,	-1   ,	-1						):
                print(a[mat_row - 1][i]						)
        # vertical printing up
        for i in range(mat_row - 2   ,	0   ,	-1						):
            print(a[i][0]						)
        _a				:							List[str]							       =      [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
        if len(UpperCAmelCase_						) > 0:
            spiral_print_clockwise(UpperCAmelCase_						)
        else:
            return
    else:
        print("""Not a valid matrix"""						)
        return





def UpperCAmelCase    (       UpperCAmelCase_						):
    if matrix:
        return list(matrix.pop(0						)						) + spiral_traversal(list(zip(*UpperCAmelCase_						)						)[::-1]						)
    else:
        return []


# driver code
if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a		:	str       =      [[1, 2, 3, 4], [5, 6, 7, 8], [9, 1_0, 1_1, 1_2]]
  spiral_print_clockwise(a)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       			=				{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__       			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)

