
from __future__ import annotations

from typing import Any




def 						A_			(				*snake_case				: list[list[int]]			)   ->     list[list[int]]:



						'''simple docstring'''

						if all(_check_not_integer(snake_case			) for m in matrix_s			):
												for i in matrix_s[1:]:
																		_verify_matrix_sizes(matrix_s[0]			,   snake_case			)
												return [[sum(snake_case			) for t in zip(*snake_case			)] for m in zip(*snake_case			)]
						raise TypeError('''Expected a matrix, got int/list instead'''			)




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: list[list[int]]			)   ->     list[list[int]]:



						'''simple docstring'''

						if (
						    _check_not_integer(snake_case			)
						    and _check_not_integer(snake_case			)
						    and _verify_matrix_sizes(snake_case			,   snake_case			)
						):
												return [[i - j for i, j in zip(*snake_case			)] for m in zip(snake_case			,   snake_case			)]
						raise TypeError('''Expected a matrix, got int/list instead'''			)




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: int | float			)   ->     list[list[float]]:



						'''simple docstring'''

						return [[x * n for x in row] for row in matrix]




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: list[list[int]]			)   ->     list[list[int]]:



						'''simple docstring'''

						if _check_not_integer(snake_case			) and _check_not_integer(snake_case			):
												__UpperCamelCase	,				__UpperCamelCase  	=      _verify_matrix_sizes(snake_case			,   snake_case			)

						if cols[0] != rows[1]:
												__UpperCamelCase  	=      (
												    '''Cannot multiply matrix of dimensions '''
												    f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
												)
												raise ValueError(snake_case			)
						return [
						    [sum(m * n for m, n in zip(snake_case			,   snake_case			)			) for j in zip(*snake_case			)] for i in matrix_a
						]




def 						A_			(				snake_case				: int			)   ->     list[list[int]]:



						'''simple docstring'''

						__UpperCamelCase  	=      int(snake_case			)
						return [[int(row == column			) for column in range(snake_case			)] for row in range(snake_case			)]




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: bool = True			)   ->     list[list[int]] | map[list[int]]:



						'''simple docstring'''

						if _check_not_integer(snake_case			):
												if return_map:
																		return map(snake_case			,   zip(*snake_case			)			)
												else:
																		return list(map(snake_case			,   zip(*snake_case			)			)			)
						raise TypeError('''Expected a matrix, got int/list instead'''			)




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: int			,   snake_case				: int			)   ->     list[list[int]]:



						'''simple docstring'''

						__UpperCamelCase  	=      matrix[:row] + matrix[row + 1 :]
						return [row[:column] + row[column + 1 :] for row in minor]




def 						A_			(				snake_case				: list[list[int]]			)   ->     Any:



						'''simple docstring'''

						if len(snake_case			) == 1:
												return matrix[0][0]

						return sum(
						    x * determinant(minor(snake_case			,   0			,   snake_case			)			) * (-1) ** i
						    for i, x in enumerate(matrix[0]			)			)




def 						A_			(				snake_case				: list[list[int]]			)   ->     list[list[float]] | None:



						'''simple docstring'''

						__UpperCamelCase  	=      determinant(snake_case			)
						if det == 0:
												return None

						__UpperCamelCase  	=      [
						    [determinant(minor(snake_case			,   snake_case			,   snake_case			)			) for j in range(len(snake_case			)			)]
						    for i in range(len(snake_case			)			)
						]

						__UpperCamelCase  	=      [
						    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
						    for row in range(len(snake_case			)			)
						]
						__UpperCamelCase  	=      list(transpose(snake_case			)			)
						return scalar_multiply(snake_case			,   1 / det			)




def 						A_			(				snake_case				: list[list[int]]			)   ->     bool:



						'''simple docstring'''

						return not isinstance(snake_case			,   snake_case			) and not isinstance(matrix[0]			,   snake_case			)




def 						A_			(				snake_case				: list[list[int]]			)   ->     tuple[int, int]:



						'''simple docstring'''

						return len(snake_case			), len(matrix[0]			)




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: list[list[int]]			)   ->     tuple[tuple[int, int], tuple[int, int]]:



						'''simple docstring'''

						__UpperCamelCase  	=      _shape(snake_case			) + _shape(snake_case			)
						if shape[0] != shape[3] or shape[1] != shape[2]:
												__UpperCamelCase  	=      (
												    '''operands could not be broadcast together with shape '''
												    f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
												)
												raise ValueError(snake_case			)
						return (shape[0], shape[2]), (shape[1], shape[3])




def 						A_			(				)   ->     None:



						'''simple docstring'''

						__UpperCamelCase  	=      [[12, 10], [3, 9]]
						__UpperCamelCase  	=      [[3, 4], [7, 4]]
						__UpperCamelCase  	=      [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
						__UpperCamelCase  	=      [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
						print(f"Add Operation, {add(snake_case			,   snake_case			) = } \n"			)
						print(
						    f"Multiply Operation, {multiply(snake_case			,   snake_case			) = } \n"			,   )
						print(f"Identity: {identity(5			)}\n"			)
						print(f"Minor of {matrix_c} = {minor(snake_case			,   1			,   2			)} \n"			)
						print(f"Determinant of {matrix_b} = {determinant(snake_case			)} \n"			)
						print(f"Inverse of {matrix_d} = {inverse(snake_case			)}\n"			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





from typing import TYPE_CHECKING

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


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

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''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         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
