






from __future__ import annotations


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


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


     def 					_A							(					self							:						Dict   ):
       return [[row[i] for row in self.rows] for i in range(len(self.rows[0]   )   )]


     @property
     def 					_A							(					self							:						List[str]   ):
       return len(self.rows   )


     @property
     def 					_A							(					self							:						List[Any]   ):
       return len(self.rows[0]   )


     @property
     def 					_A							(					self							:						Optional[Any]   ):
       return (self.num_rows, self.num_columns)


     @property
     def 					_A							(					self							:						Optional[int]   ):
       return self.order[0] == self.order[1]


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     [
           [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(UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]   ):
       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							:						List[str]   ):
       return bool(self.determinant()   )


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   Optional[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(UpperCAmelCase_   ).determinant()


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int   ):
       if (row + column) % 2 == 0:
         return self.get_minor(UpperCAmelCase_       ,			UpperCAmelCase_   )
       return -1 * self.get_minor(UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						List[str]   ):
       return Matrix(
           [
               [self.get_minor(UpperCAmelCase_       ,			UpperCAmelCase_   ) for column in range(self.num_columns   )]
               for row in range(self.num_rows   )
           ]   )


     def 					_A							(					self							:						Dict   ):
       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							:						Any   ):
       SCREAMING_SNAKE_CASE							:   int						    =     [
           [self.cofactors().rows[column][row] for column in range(self.num_columns   )]
           for row in range(self.num_rows   )
       ]
       return Matrix(UpperCAmelCase_   )


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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							:						Optional[int]   ):
       return str(self.rows   )


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


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


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


     def __eq__(					self							:						Any       ,			UpperCAmelCase_							:						object   ):
       if not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         return NotImplemented
       return self.rows == other.rows


     def __ne__(					self							:						List[Any]       ,			UpperCAmelCase_							:						object   ):
       return not self == other


     def __neg__(					self							:						Tuple   ):
       return self * -1


     def __add__(					self							:						str       ,			UpperCAmelCase_							:						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							:						Any       ,			UpperCAmelCase_							:						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							:						Any       ,			UpperCAmelCase_							:						Matrix | int | float   ):
       if isinstance(UpperCAmelCase_       ,			(int, float)   ):
         return Matrix(
             [[int(element * other   ) for element in row] for row in self.rows]   )
       elif isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         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(UpperCAmelCase_       ,			UpperCAmelCase_   ) 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       ,			UpperCAmelCase_							:						int   ):
       if not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         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"   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self
       for _ in range(other - 1   ):
         result *= self
       return result






     @classmethod
     def 					_A							(					cls							:						List[Any]       ,			UpperCAmelCase_							:						list[int]       ,			UpperCAmelCase_							:						list[int]   ):
       return sum(row[i] * column[i] for i in range(len(UpperCAmelCase_   )   )   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()














"""simple docstring"""



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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase	:	Optional[Any]				=      logging.get_logger(__name__)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Any=False			,					snake_case       : Any=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""

     UpperCAmelCase__						:    Any							=							[]
     for i in range(config.num_hidden_layers				):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')				)
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')				)
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')				)
          rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')				)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f'{prefix}cls_token', "beit.embeddings.cls_token"),
             (f'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
             (f'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
             (f'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
         ]				)

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ("mask_token", "beit.embeddings.mask_token"),
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
              ]				)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("fc_norm.weight", "beit.pooler.layernorm.weight"),
                  ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]				)

     return rename_keys


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : List[str]			,					snake_case       : List[Any]=False			,					snake_case       : int=False				)->		Any:






     '''simple docstring'''



     for i in range(config.num_hidden_layers				):
          UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""
          # queries, keys and values
          UpperCAmelCase__						:    List[str]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'				)
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'				)
          UpperCAmelCase__						:    Optional[int]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'				)

          UpperCAmelCase__						:    List[Any]							=							in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__						:    Dict							=							q_bias
          UpperCAmelCase__						:    Optional[int]							=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__						:    Any							=							in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__						:    int							=							v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_1'				)
          UpperCAmelCase__						:    Tuple							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_2'				)

          UpperCAmelCase__						:    Dict							=							gamma_a
          UpperCAmelCase__						:    Tuple							=							gamma_a


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Dict			,					snake_case       : Tuple				)->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							dct.pop(snake_case				)
     UpperCAmelCase__						:    Tuple							=							val


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__						:    List[str]							=							Image.open(requests.get(snake_case			,					stream=snake_case				).raw				)
     return im


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : str			,					snake_case       : Optional[Any]=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							False if "rvlcdip" in checkpoint_url else True
     UpperCAmelCase__						:    int							=							BeitConfig(use_absolute_position_embeddings=snake_case			,					use_mask_token=snake_case				)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          UpperCAmelCase__						:    Optional[int]							=							1024
          UpperCAmelCase__						:    Optional[Any]							=							4096
          UpperCAmelCase__						:    List[str]							=							24
          UpperCAmelCase__						:    List[str]							=							16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCAmelCase__						:    List[Any]							=							16
          UpperCAmelCase__						:    Optional[int]							=							"huggingface/label-files"
          UpperCAmelCase__						:    List[Any]							=							"rvlcdip-id2label.json"
          UpperCAmelCase__						:    List[str]							=							json.load(open(hf_hub_download(snake_case			,					snake_case			,					repo_type="dataset"				)			,					"r"				)				)
          UpperCAmelCase__						:    Dict							=							{int(snake_case				): v for k, v in idalabel.items()}
          UpperCAmelCase__						:    List[Any]							=							idalabel
          UpperCAmelCase__						:    Optional[Any]							=							{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase__						:    List[Any]							=							torch.hub.load_state_dict_from_url(snake_case			,					map_location="cpu"				)["model"]

     UpperCAmelCase__						:    str							=							create_rename_keys(snake_case			,					has_lm_head=snake_case				)
     for src, dest in rename_keys:
          rename_key(snake_case			,					snake_case			,					snake_case				)
     read_in_q_k_v(snake_case			,					snake_case			,					has_lm_head=snake_case				)

     # load HuggingFace model
     UpperCAmelCase__						:    int							=							BeitForMaskedImageModeling(snake_case				) if has_lm_head else BeitForImageClassification(snake_case				)
     model.eval()
     model.load_state_dict(snake_case				)

     # Check outputs on an image
     UpperCAmelCase__						:    List[Any]							=							BeitImageProcessor(
         size=config.image_size			,					resample=PILImageResampling.BILINEAR			,					do_center_crop=snake_case				)
     UpperCAmelCase__						:    Any							=							prepare_img()

     UpperCAmelCase__						:    Any							=							image_processor(images=snake_case			,					return_tensors="pt"				)
     UpperCAmelCase__						:    List[str]							=							encoding["pixel_values"]

     UpperCAmelCase__						:    Optional[Any]							=							model(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							outputs.logits

     # verify logits
     UpperCAmelCase__						:    Union[str, Any]							=							[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(snake_case				), "Shape of logits not as expected"

     Path(snake_case				).mkdir(exist_ok=snake_case				)
     print(f'Saving model 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:
          if has_lm_head:
               UpperCAmelCase__						:    Any							=							"dit-base" if "base" in checkpoint_url else "dit-large"
          else:
               UpperCAmelCase__						:    int							=							"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
          image_processor.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add image processor"			,					use_temp_dir=snake_case			,					)
          model.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add model"			,					use_temp_dir=snake_case			,					)


if __name__ == "__main__":
 _lowerCAmelCase	:	Tuple				=      argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
     type=str,
     help="""URL to the original PyTorch checkpoint (.pth file).""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
 )
 _lowerCAmelCase	:	str				=      parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


