






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a:     int   					=					logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    MobileViTConfig()

       # size of the architecture
       if "mobilevit_s" in mobilevit_name:
              _UpperCAmelCase							=    [1_4_4, 1_9_2, 2_4_0]
              _UpperCAmelCase							=    [1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
       elif "mobilevit_xs" in mobilevit_name:
              _UpperCAmelCase							=    [9_6, 1_2_0, 1_4_4]
              _UpperCAmelCase							=    [1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
       elif "mobilevit_xxs" in mobilevit_name:
              _UpperCAmelCase							=    [6_4, 8_0, 9_6]
              _UpperCAmelCase							=    [1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
              _UpperCAmelCase							=    0.05
              _UpperCAmelCase							=    2.0

       if mobilevit_name.startswith("""deeplabv3_""" ):
              _UpperCAmelCase							=    5_1_2
              _UpperCAmelCase							=    1_6
              _UpperCAmelCase							=    2_1
              _UpperCAmelCase							=    """pascal-voc-id2label.json"""
       else:
              _UpperCAmelCase							=    1_0_0_0
              _UpperCAmelCase							=    """imagenet-1k-id2label.json"""

       _UpperCAmelCase							=    """huggingface/label-files"""
       _UpperCAmelCase							=    json.load(open(hf_hub_download(__snake_case   ,      __snake_case   ,      repo_type="""dataset""" )   ,      """r""" ) )
       _UpperCAmelCase							=    {int(__snake_case ): v for k, v in idalabel.items()}
       _UpperCAmelCase							=    idalabel
       _UpperCAmelCase							=    {v: k for k, v in idalabel.items()}

       return config




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=False )				->						Dict:
       for i in range(1   ,      6 ):
              if f"""layer_{i}.""" in name:
                     _UpperCAmelCase							=    name.replace(f"""layer_{i}."""   ,      f"""encoder.layer.{i - 1}.""" )

       if "conv_1." in name:
              _UpperCAmelCase							=    name.replace("""conv_1."""   ,      """conv_stem.""" )
       if ".block." in name:
              _UpperCAmelCase							=    name.replace(""".block."""   ,      """.""" )

       if "exp_1x1" in name:
              _UpperCAmelCase							=    name.replace("""exp_1x1"""   ,      """expand_1x1""" )
       if "red_1x1" in name:
              _UpperCAmelCase							=    name.replace("""red_1x1"""   ,      """reduce_1x1""" )
       if ".local_rep.conv_3x3." in name:
              _UpperCAmelCase							=    name.replace(""".local_rep.conv_3x3."""   ,      """.conv_kxk.""" )
       if ".local_rep.conv_1x1." in name:
              _UpperCAmelCase							=    name.replace(""".local_rep.conv_1x1."""   ,      """.conv_1x1.""" )
       if ".norm." in name:
              _UpperCAmelCase							=    name.replace(""".norm."""   ,      """.normalization.""" )
       if ".conv." in name:
              _UpperCAmelCase							=    name.replace(""".conv."""   ,      """.convolution.""" )
       if ".conv_proj." in name:
              _UpperCAmelCase							=    name.replace(""".conv_proj."""   ,      """.conv_projection.""" )

       for i in range(0   ,      2 ):
              for j in range(0   ,      4 ):
                     if f""".{i}.{j}.""" in name:
                            _UpperCAmelCase							=    name.replace(f""".{i}.{j}."""   ,      f""".{i}.layer.{j}.""" )

       for i in range(2   ,      6 ):
              for j in range(0   ,      4 ):
                     if f""".{i}.{j}.""" in name:
                            _UpperCAmelCase							=    name.replace(f""".{i}.{j}."""   ,      f""".{i}.""" )
                            if "expand_1x1" in name:
                                   _UpperCAmelCase							=    name.replace("""expand_1x1"""   ,      """downsampling_layer.expand_1x1""" )
                            if "conv_3x3" in name:
                                   _UpperCAmelCase							=    name.replace("""conv_3x3"""   ,      """downsampling_layer.conv_3x3""" )
                            if "reduce_1x1" in name:
                                   _UpperCAmelCase							=    name.replace("""reduce_1x1"""   ,      """downsampling_layer.reduce_1x1""" )

       for i in range(2   ,      5 ):
              if f""".global_rep.{i}.weight""" in name:
                     _UpperCAmelCase							=    name.replace(f""".global_rep.{i}.weight"""   ,      """.layernorm.weight""" )
              if f""".global_rep.{i}.bias""" in name:
                     _UpperCAmelCase							=    name.replace(f""".global_rep.{i}.bias"""   ,      """.layernorm.bias""" )

       if ".global_rep." in name:
              _UpperCAmelCase							=    name.replace(""".global_rep."""   ,      """.transformer.""" )
       if ".pre_norm_mha.0." in name:
              _UpperCAmelCase							=    name.replace(""".pre_norm_mha.0."""   ,      """.layernorm_before.""" )
       if ".pre_norm_mha.1.out_proj." in name:
              _UpperCAmelCase							=    name.replace(""".pre_norm_mha.1.out_proj."""   ,      """.attention.output.dense.""" )
       if ".pre_norm_ffn.0." in name:
              _UpperCAmelCase							=    name.replace(""".pre_norm_ffn.0."""   ,      """.layernorm_after.""" )
       if ".pre_norm_ffn.1." in name:
              _UpperCAmelCase							=    name.replace(""".pre_norm_ffn.1."""   ,      """.intermediate.dense.""" )
       if ".pre_norm_ffn.4." in name:
              _UpperCAmelCase							=    name.replace(""".pre_norm_ffn.4."""   ,      """.output.dense.""" )
       if ".transformer." in name:
              _UpperCAmelCase							=    name.replace(""".transformer."""   ,      """.transformer.layer.""" )

       if ".aspp_layer." in name:
              _UpperCAmelCase							=    name.replace(""".aspp_layer."""   ,      """.""" )
       if ".aspp_pool." in name:
              _UpperCAmelCase							=    name.replace(""".aspp_pool."""   ,      """.""" )
       if "seg_head." in name:
              _UpperCAmelCase							=    name.replace("""seg_head."""   ,      """segmentation_head.""" )
       if "segmentation_head.classifier.classifier." in name:
              _UpperCAmelCase							=    name.replace("""segmentation_head.classifier.classifier."""   ,      """segmentation_head.classifier.""" )

       if "classifier.fc." in name:
              _UpperCAmelCase							=    name.replace("""classifier.fc."""   ,      """classifier.""" )
       elif (not base_model) and ("segmentation_head." not in name):
              _UpperCAmelCase							=    """mobilevit.""" + name

       return name




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case=False )				->						str:
       if base_model:
              _UpperCAmelCase							=    """"""
       else:
              _UpperCAmelCase							=    """mobilevit."""

       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase							=    orig_state_dict.pop(__snake_case )

              if key[:8] == "encoder.":
                     _UpperCAmelCase							=    key[8:]

              if "qkv" in key:
                     _UpperCAmelCase							=    key.split(""".""" )
                     _UpperCAmelCase							=    int(key_split[0][6:] ) - 1
                     _UpperCAmelCase							=    int(key_split[3] )
                     _UpperCAmelCase							=    model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}""" )
                     _UpperCAmelCase							=    layer.transformer.layer[transformer_num].attention.attention.all_head_size
                     _UpperCAmelCase							=    (
                         f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
                     )
                     if "weight" in key:
                            _UpperCAmelCase							=    val[:dim, :]
                            _UpperCAmelCase							=    val[dim : dim * 2, :]
                            _UpperCAmelCase							=    val[-dim:, :]
                     else:
                            _UpperCAmelCase							=    val[:dim]
                            _UpperCAmelCase							=    val[dim : dim * 2]
                            _UpperCAmelCase							=    val[-dim:]
              else:
                     _UpperCAmelCase							=    val

       return orig_state_dict




def       _SCREAMING_SNAKE_CASE (							)				->						Tuple:
       _UpperCAmelCase							=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase							=    Image.open(requests.get(__snake_case   ,      stream=__snake_case ).raw )
       return im




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=False )				->						str:
       _UpperCAmelCase							=    get_mobilevit_config(__snake_case )

       # load original state_dict
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )

       # load 🤗 model
       if mobilevit_name.startswith("""deeplabv3_""" ):
              _UpperCAmelCase							=    MobileViTForSemanticSegmentation(__snake_case ).eval()
       else:
              _UpperCAmelCase							=    MobileViTForImageClassification(__snake_case ).eval()

       _UpperCAmelCase							=    convert_state_dict(__snake_case   ,      __snake_case )
       model.load_state_dict(__snake_case )

       # Check outputs on an image, prepared by MobileViTImageProcessor
       _UpperCAmelCase							=    MobileViTImageProcessor(crop_size=config.image_size   ,      size=config.image_size + 3_2 )
       _UpperCAmelCase							=    image_processor(images=prepare_img()   ,      return_tensors="""pt""" )
       _UpperCAmelCase							=    model(**__snake_case )
       _UpperCAmelCase							=    outputs.logits

       if mobilevit_name.startswith("""deeplabv3_""" ):
              assert logits.shape == (1, 2_1, 3_2, 3_2)

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     _UpperCAmelCase							=    torch.tensor(
                         [
                             [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                             [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                             [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                         ] )
              elif mobilevit_name == "deeplabv3_mobilevit_xs":
                     _UpperCAmelCase							=    torch.tensor(
                         [
                             [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                             [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                             [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                         ] )
              elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                     _UpperCAmelCase							=    torch.tensor(
                         [
                             [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                             [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                             [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                         ] )
              else:
                     raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" )

              assert torch.allclose(logits[0, :3, :3, :3]   ,      __snake_case   ,      atol=1E-4 )
       else:
              assert logits.shape == (1, 1_0_0_0)

              if mobilevit_name == "mobilevit_s":
                     _UpperCAmelCase							=    torch.tensor([-0.9866, 0.2392, -1.1241] )
              elif mobilevit_name == "mobilevit_xs":
                     _UpperCAmelCase							=    torch.tensor([-2.4761, -0.9399, -1.9587] )
              elif mobilevit_name == "mobilevit_xxs":
                     _UpperCAmelCase							=    torch.tensor([-1.9364, -1.2327, -0.4653] )
              else:
                     raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" )

              assert torch.allclose(logits[0, :3]   ,      __snake_case   ,      atol=1E-4 )

       Path(__snake_case ).mkdir(exist_ok=__snake_case )
       print(f"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
       model.save_pretrained(__snake_case )
       print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
       image_processor.save_pretrained(__snake_case )

       if push_to_hub:
              _UpperCAmelCase							=    {
                  """mobilevit_s""": """mobilevit-small""",
                  """mobilevit_xs""": """mobilevit-x-small""",
                  """mobilevit_xxs""": """mobilevit-xx-small""",
                  """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
                  """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
                  """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
              }

              print("""Pushing to the hub...""" )
              _UpperCAmelCase							=    model_mapping[mobilevit_name]
              image_processor.push_to_hub(__snake_case   ,      organization="""apple""" )
              model.push_to_hub(__snake_case   ,      organization="""apple""" )


if __name__ == "__main__":
  __a:     int   					=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--mobilevit_name''',
      default='''mobilevit_s''',
      type=str,
      help=(
          '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
          ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
      ),
  )
  parser.add_argument(
      '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
  )
  parser.add_argument(
      '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
  )

  __a:     Any   					=					parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )







from __future__ import annotations
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			list[list[int]]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    TypeError(
             """Matrices must be formed from a list of zero or more lists containing at """
             """least one and the same number of values, each of which must be of type """
             """int or float."""							)
         if len(lowerCamelCase							) != 0:
                _UpperCAmelCase							=    len(rows[0]							)
                if cols == 0:
                       raise error
                for row in rows:
                       if len(lowerCamelCase							) != cols:
                              raise error
                       for value in row:
                              if not isinstance(lowerCamelCase       ,				(int, float)							):
                                     raise error
                _UpperCAmelCase							=    rows
         else:
                _UpperCAmelCase							=    []


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	list[list[int]]:


         """simple docstring"""
         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]


  @property
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         return len(self.rows							)


  @property
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         return len(self.rows[0]							)


  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	tuple[int, int]:


         """simple docstring"""
         return (self.num_rows, self.num_columns)


  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	bool:


         """simple docstring"""
         return self.order[0] == self.order[1]


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
             for row_num in range(self.num_rows							)
         ]
         return Matrix(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	int:


         """simple docstring"""
         if not self.is_square:
                return 0
         if self.order == (0, 0):
                return 1
         if self.order == (1, 1):
                return int(self.rows[0][0]							)
         if self.order == (2, 2):
                return int(
                    (self.rows[0][0] * self.rows[1][1])
                    - (self.rows[0][1] * self.rows[1][0])							)
         else:
                return sum(
                    self.rows[0][column] * self.cofactors().rows[0][column]
                    for column in range(self.num_columns							)							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	bool:


         """simple docstring"""
         return bool(self.determinant()							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns							)
                 if other_column != column
             ]
             for other_row in range(self.num_rows							)
             if other_row != row
         ]
         return Matrix(lowerCamelCase							).determinant()


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	int:


         """simple docstring"""
         if (row + column) % 2 == 0:
                return self.get_minor(lowerCamelCase       ,				lowerCamelCase							)
         return -1 * self.get_minor(lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	Matrix:


         """simple docstring"""
         return Matrix(
             [
                 [self.get_minor(lowerCamelCase       ,				lowerCamelCase							) for column in range(self.num_columns							)]
                 for row in range(self.num_rows							)
             ]							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Matrix:


         """simple docstring"""
         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns							)
                 ]
                 for row in range(self.minors().num_rows							)
             ]							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
             for row in range(self.num_rows							)
         ]
         return Matrix(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    self.determinant()
         if not determinant:
                raise TypeError("""Only matrices with a non-zero determinant have an inverse"""							)
         return self.adjugate() * (1 / determinant)


  def __repr__(      self		:			List[str]							)						->	str:


         """simple docstring"""
         return str(self.rows							)


  def __str__(      self		:			Optional[Any]							)						->	str:


         """simple docstring"""
         if self.num_rows == 0:
                return "[]"
         if self.num_rows == 1:
                return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     """[""" + """. """.join([str(lowerCamelCase							) for value in row]							) + """.]"""
                     for row in self.rows
                 ]							)
             + "]"
         )


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			int | None = None							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    TypeError("""Row must be a list containing all ints and/or floats"""							)
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                raise type_error
         for value in row:
                if not isinstance(lowerCamelCase       ,				(int, float)							):
                       raise type_error
         if len(lowerCamelCase							) != self.num_columns:
                raise ValueError(
                    """Row must be equal in length to the other rows in the matrix"""							)
         if position is None:
                self.rows.append(lowerCamelCase							)
         else:
                _UpperCAmelCase							=    self.rows[0:position] + [row] + self.rows[position:]


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			int | None = None							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    TypeError(
             """Column must be a list containing all ints and/or floats"""							)
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                raise type_error
         for value in column:
                if not isinstance(lowerCamelCase       ,				(int, float)							):
                       raise type_error
         if len(lowerCamelCase							) != self.num_rows:
                raise ValueError(
                    """Column must be equal in length to the other columns in the matrix"""							)
         if position is None:
                _UpperCAmelCase							=    [self.rows[i] + [column[i]] for i in range(self.num_rows							)]
         else:
                _UpperCAmelCase							=    [
                    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                    for i in range(self.num_rows							)
                ]


  def __eq__(      self		:			Dict       ,				lowerCamelCase		:			object							)						->	bool:


         """simple docstring"""
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                return NotImplemented
         return self.rows == other.rows


  def __ne__(      self		:			List[str]       ,				lowerCamelCase		:			object							)						->	bool:


         """simple docstring"""
         return not self == other


  def __neg__(      self		:			Union[str, Any]							)						->	Matrix:


         """simple docstring"""
         return self * -1


  def __add__(      self		:			Any       ,				lowerCamelCase		:			Matrix							)						->	Matrix:


         """simple docstring"""
         if self.order != other.order:
                raise ValueError("""Addition requires matrices of the same order"""							)
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
                 for i in range(self.num_rows							)
             ]							)


  def __sub__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Matrix							)						->	Matrix:


         """simple docstring"""
         if self.order != other.order:
                raise ValueError("""Subtraction requires matrices of the same order"""							)
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
                 for i in range(self.num_rows							)
             ]							)


  def __mul__(      self		:			List[Any]       ,				lowerCamelCase		:			Matrix | int | float							)						->	Matrix:


         """simple docstring"""
         if isinstance(lowerCamelCase       ,				(int, float)							):
                return Matrix(
                    [[int(element * other							) for element in row] for row in self.rows]							)
         elif isinstance(lowerCamelCase       ,				lowerCamelCase							):
                if self.num_columns != other.num_rows:
                       raise ValueError(
                           """The number of columns in the first matrix must """
                           """be equal to the number of rows in the second"""							)
                return Matrix(
                    [
                        [Matrix.dot_product(lowerCamelCase       ,				lowerCamelCase							) for column in other.columns()]
                        for row in self.rows
                    ]							)
         else:
                raise TypeError(
                    """A Matrix can only be multiplied by an int, float, or another matrix"""							)


  def __pow__(      self		:			Any       ,				lowerCamelCase		:			int							)						->	Matrix:


         """simple docstring"""
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                raise TypeError("""A Matrix can only be raised to the power of an int"""							)
         if not self.is_square:
                raise ValueError("""Only square matrices can be raised to a power"""							)
         if other == 0:
                return self.identity()
         if other < 0:
                if self.is_invertable():
                       return self.inverse() ** (-other)
                raise ValueError(
                    """Only invertable matrices can be raised to a negative power"""							)
         _UpperCAmelCase							=    self
         for _ in range(other - 1							):
                result *= self
         return result





  @classmethod
  def  lowerCamelCase       (      cls		:			Tuple       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			list[int]							)						->	int:


         """simple docstring"""
         return sum(row[i] * column[i] for i in range(len(lowerCamelCase							)							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()