




def       lowerCAmelCase_      (	lowerCamelCase:							list[list[int]]    )       ->      bool:


      '''simple docstring'''




      lowerCamelCase__   	=					[list(lowerCamelCase    ) for row in matrix]
      if matrix and isinstance(lowerCamelCase  ,					lowerCamelCase    ):
            if isinstance(matrix[0]  ,					lowerCamelCase    ):
                  lowerCamelCase__   	=					0
                  for row in matrix:
                        if prev_len == 0:
                              lowerCamelCase__   	=					len(lowerCamelCase    )
                              lowerCamelCase__   	=					True
                        else:
                              lowerCamelCase__   	=					prev_len == len(lowerCamelCase    )
            else:
                  lowerCamelCase__   	=					True
      else:
            lowerCamelCase__   	=					False

      return result

def       lowerCAmelCase_      (	lowerCamelCase:							list[list[int]]    )       ->      None:


      '''simple docstring'''




      if check_matrix(lowerCamelCase    ) and len(lowerCamelCase    ) > 0:
            lowerCamelCase__   	=					[list(lowerCamelCase    ) for row in a]
            lowerCamelCase__   	=					len(lowerCamelCase    )
            if isinstance(a[0]  ,					lowerCamelCase    ):
                  lowerCamelCase__   	=					len(a[0]    )
            else:
                  for dat in a:
                        print(lowerCamelCase    )
                  return

            # horizotal printing increasing
            for i in range(0  ,					lowerCamelCase    ):
                  print(a[0][i]    )
            # vertical printing down
            for i in range(1  ,					lowerCamelCase    ):
                  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]    )
            lowerCamelCase__   	=					[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
            if len(lowerCamelCase    ) > 0:
                  spiral_print_clockwise(lowerCamelCase    )
            else:
                  return
      else:
            print('Not a valid matrix'    )
            return

def       lowerCAmelCase_      (	lowerCamelCase:							list[list]    )       ->      list[int]:


      '''simple docstring'''




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


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

      doctest.testmod()

      __SCREAMING_SNAKE_CASE     : Optional[int]      =[[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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)