




from __future__ import annotations

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      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(_SCREAMING_SNAKE_CASE  ) != 0:
              UpperCAmelCase_      :							Union[str, Any]       =      len(rows[0]  )
              if cols == 0:
                   raise error
              for row in rows:
                   if len(_SCREAMING_SNAKE_CASE  ) != cols:
                        raise error
                   for value in row:
                        if not isinstance(_SCREAMING_SNAKE_CASE       ,(int, float)  ):
                             raise error
              UpperCAmelCase_      :							Union[str, Any]       =      rows
         else:
              UpperCAmelCase_      :							Optional[Any]       =      []





    def 							a__				(				self  )			->		list[list[int]]:
         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]  )  )]





    @property
    def 							a__				(				self  )			->		int:
         return len(self.rows  )





    @property
    def 							a__				(				self  )			->		int:
         return len(self.rows[0]  )





    @property
    def 							a__				(				self  )			->		tuple[int, int]:
         return (self.num_rows, self.num_columns)





    @property
    def 							a__				(				self  )			->		bool:
         return self.order[0] == self.order[1]





    def 							a__				(				self  )			->		Matrix:
         UpperCAmelCase_      :							Tuple       =      [
             [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(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		int:
         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 							a__				(				self  )			->		bool:
         return bool(self.determinant()  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							List[Any]       =      [
             [
                 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(_SCREAMING_SNAKE_CASE  ).determinant()





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         if (row + column) % 2 == 0:
              return self.get_minor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         return -1 * self.get_minor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Matrix:
         return Matrix(
             [
                 [self.get_minor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) for column in range(self.num_columns  )]
                 for row in range(self.num_rows  )
             ]  )





    def 							a__				(				self  )			->		Matrix:
         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 							a__				(				self  )			->		Matrix:
         UpperCAmelCase_      :							List[str]       =      [
             [self.cofactors().rows[column][row] for column in range(self.num_columns  )]
             for row in range(self.num_rows  )
         ]
         return Matrix(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Matrix:
         UpperCAmelCase_      :							List[Any]       =      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  )			->		str:
         return str(self.rows  )





    def __str__(				self  )			->		str:
         if self.num_rows == 0:
              return "[]"
         if self.num_rows == 1:
              return "[[" + ". ".join(str(self.rows[0]  )  ) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     '''[''' + '''. '''.join([str(_SCREAMING_SNAKE_CASE  ) for value in row]  ) + '''.]'''
                     for row in self.rows
                 ]  )
             + "]"
         )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		None:
         UpperCAmelCase_      :							List[str]       =      TypeError('''Row must be a list containing all ints and/or floats'''  )
         if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              raise type_error
         for value in row:
              if not isinstance(_SCREAMING_SNAKE_CASE       ,(int, float)  ):
                   raise type_error
         if len(_SCREAMING_SNAKE_CASE  ) != 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(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							Union[str, Any]       =      self.rows[0:position] + [row] + self.rows[position:]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		None:
         UpperCAmelCase_      :							str       =      TypeError(
             '''Column must be a list containing all ints and/or floats'''  )
         if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              raise type_error
         for value in column:
              if not isinstance(_SCREAMING_SNAKE_CASE       ,(int, float)  ):
                   raise type_error
         if len(_SCREAMING_SNAKE_CASE  ) != self.num_rows:
              raise ValueError(
                  '''Column must be equal in length to the other columns in the matrix'''  )
         if position is None:
              UpperCAmelCase_      :							Optional[Any]       =      [self.rows[i] + [column[i]] for i in range(self.num_rows  )]
         else:
              UpperCAmelCase_      :							Union[str, Any]       =      [
                  self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                  for i in range(self.num_rows  )
              ]





    def __eq__(				self       ,_SCREAMING_SNAKE_CASE  )			->		bool:
         if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              return NotImplemented
         return self.rows == other.rows





    def __ne__(				self       ,_SCREAMING_SNAKE_CASE  )			->		bool:
         return not self == other





    def __neg__(				self  )			->		Matrix:
         return self * -1





    def __add__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Matrix:
         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       ,_SCREAMING_SNAKE_CASE  )			->		Matrix:
         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       ,_SCREAMING_SNAKE_CASE  )			->		Matrix:
         if isinstance(_SCREAMING_SNAKE_CASE       ,(int, float)  ):
              return Matrix(
                  [[int(element * other  ) for element in row] for row in self.rows]  )
         elif isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              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(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) 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       ,_SCREAMING_SNAKE_CASE  )			->		Matrix:
         if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              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_      :							List[Any]       =      self
         for _ in range(other - 1  ):
              result *= self
         return result





    @classmethod
    def 							a__				(				cls       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         return sum(row[i] * column[i] for i in range(len(_SCREAMING_SNAKE_CASE  )  )  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

     if model_size == "tiny":
          UpperCAmelCase_      :							Tuple       =      96
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 6, 2)
          UpperCAmelCase_      :							str       =      (3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase_      :							Dict       =      96
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							List[str]       =      (3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase_      :							Tuple       =      128
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Optional[int]       =      (4, 8, 16, 32)
     else:
          UpperCAmelCase_      :							List[str]       =      192
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Union[str, Any]       =      (6, 12, 24, 48)

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if "patch_embed.proj" in name:
          UpperCAmelCase_      :							str       =      name.replace('''patch_embed.proj'''			,						'''embeddings.patch_embeddings.projection'''				)
     if "patch_embed.norm" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''patch_embed.norm'''			,						'''embeddings.norm'''				)
     if "layers" in name:
          UpperCAmelCase_      :							Tuple       =      '''encoder.''' + name
     if "attn.proj" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''attn.proj'''			,						'''attention.output.dense'''				)
     if "attn" in name:
          UpperCAmelCase_      :							int       =      name.replace('''attn'''			,						'''attention.self'''				)
     if "norm1" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''norm1'''			,						'''layernorm_before'''				)
     if "norm2" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''norm2'''			,						'''layernorm_after'''				)
     if "mlp.fc1" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''mlp.fc1'''			,						'''intermediate.dense'''				)
     if "mlp.fc2" in name:
          UpperCAmelCase_      :							Optional[int]       =      name.replace('''mlp.fc2'''			,						'''output.dense'''				)

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               UpperCAmelCase_      :							str       =      key.split('''.'''				)
               UpperCAmelCase_      :							str       =      int(key_split[1]				)
               UpperCAmelCase_      :							Optional[Any]       =      int(key_split[3]				)
               UpperCAmelCase_      :							Optional[int]       =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    UpperCAmelCase_      :							Any       =      val[:dim, :]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_      :							List[str]       =      val[-dim:, :]
               else:
                    UpperCAmelCase_      :							Any       =      val[
                        :dim
                    ]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_      :							Union[str, Any]       =      val[
                        -dim:
                    ]
          else:
               UpperCAmelCase_      :							Optional[Any]       =      val

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_      :							int       =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''			,						'''-'''				)				)				)
     UpperCAmelCase_      :							Optional[Any]       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)
     UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				)

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      model(**_lowercase				).logits

     assert torch.allclose(_lowercase			,						_lowercase			,						atol=1E-3				)

     print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
     image_processor.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     __a          =			parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)