'''simple docstring'''


from typing import TYPE_CHECKING

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


snake_case__	:     Any      =    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     int      =    [
     '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPTJForCausalLM''',
     '''GPTJForQuestionAnswering''',
     '''GPTJForSequenceClassification''',
     '''GPTJModel''',
     '''GPTJPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     List[Any]      =    [
     '''TFGPTJForCausalLM''',
     '''TFGPTJForQuestionAnswering''',
     '''TFGPTJForSequenceClassification''',
     '''TFGPTJModel''',
     '''TFGPTJPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     int      =    [
     '''FlaxGPTJForCausalLM''',
     '''FlaxGPTJModel''',
     '''FlaxGPTJPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 snake_case__	:     str      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[list[int]]					)  ->							bool:
   # must be
   _UpperCAmelCase 						=       [list(a_					) for row in matrix]
   if matrix and isinstance(a_     ,	a_					):
      if isinstance(matrix[0]     ,	a_					):
         _UpperCAmelCase 						=       0
         for row in matrix:
            if prev_len == 0:
               _UpperCAmelCase 						=       len(a_					)
               _UpperCAmelCase 						=       True
            else:
               _UpperCAmelCase 						=       prev_len == len(a_					)
      else:
         _UpperCAmelCase 						=       True
   else:
      _UpperCAmelCase 						=       False

   return result
def       __UpperCAmelCase		(		a_      :		list[list[int]]					)  ->							None:
   if check_matrix(a_					) and len(a_					) > 0:
      _UpperCAmelCase 						=       [list(a_					) for row in a]
      _UpperCAmelCase 						=       len(a_					)
      if isinstance(a[0]     ,	a_					):
         _UpperCAmelCase 						=       len(a[0]					)
      else:
         for dat in a:
            print(a_					)
         return

      # horizotal printing increasing
      for i in range(0     ,	a_					):
         print(a[0][i]					)
      # vertical printing down
      for i in range(1     ,	a_					):
         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]					)
      _UpperCAmelCase 						=       [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
      if len(a_					) > 0:
         spiral_print_clockwise(a_					)
      else:
         return
   else:
      print("Not a valid matrix"					)
      return
def       __UpperCAmelCase		(		a_      :		list[list]					)  ->							list[int]:
   if matrix:
      return list(matrix.pop(0					)					) + spiral_traversal(list(zip(*a_					)					)[::-1]					)
   else:
      return []


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

  doctest.testmod()

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

