
def        lowerCamelCase_      (			UpperCAmelCase_							:				list[list[int]]       ):
						# must be
						lowercase       :    Union[str, Any]											=  [list(UpperCAmelCase_       ) for row in matrix]
						if matrix and isinstance(UpperCAmelCase_							,			UpperCAmelCase_       ):
												if isinstance(matrix[0]							,			UpperCAmelCase_       ):
																		lowercase       :    List[str]											=  0
																		for row in matrix:
																								if prev_len == 0:
																														lowercase       :    List[str]											=  len(UpperCAmelCase_       )
																														lowercase       :    Optional[int]											=  True
																								else:
																														lowercase       :    List[Any]											=  prev_len == len(UpperCAmelCase_       )
												else:
																		lowercase       :    Tuple											=  True
						else:
												lowercase       :    int											=  False

						return result

def        lowerCamelCase_      (			UpperCAmelCase_							:				list[list[int]]       ):
						if check_matrix(UpperCAmelCase_       ) and len(UpperCAmelCase_       ) > 0:
												lowercase       :    Tuple											=  [list(UpperCAmelCase_       ) for row in a]
												lowercase       :    List[Any]											=  len(UpperCAmelCase_       )
												if isinstance(a[0]							,			UpperCAmelCase_       ):
																		lowercase       :    Dict											=  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]       )
												lowercase       :    int											=  [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        lowerCamelCase_      (			UpperCAmelCase_							:				list[list]       ):
						if matrix:
												return list(matrix.pop(0       )       ) + spiral_traversal(list(zip(*UpperCAmelCase_       )       )[::-1]       )
						else:
												return []


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

						doctest.testmod()

						snake_case__  							=							[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
						spiral_print_clockwise(a)




from typing import TYPE_CHECKING

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


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

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


