






'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A    	=   logging.get_logger(__name__)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> str:







   """simple docstring"""
   __snake_case				:      List[Any]     							= DPTConfig(embedding_type='hybrid'  )

   if "large" in checkpoint_url:
      __snake_case				:      Optional[Any]     							= 10_24
      __snake_case				:      Union[str, Any]     							= 40_96
      __snake_case				:      Any     							= 24
      __snake_case				:      int     							= 16
      __snake_case				:      Dict     							= [5, 11, 17, 23]
      __snake_case				:      List[str]     							= [2_56, 5_12, 10_24, 10_24]
      __snake_case				:      Optional[Any]     							= (1, 3_84, 3_84)

   if "nyu" or "midas" in checkpoint_url:
      __snake_case				:      int     							= 7_68
      __snake_case				:      Tuple     							= [1, 1, 1, 0.5]
      __snake_case				:      Union[str, Any]     							= [2_56, 5_12, 7_68, 7_68]
      __snake_case				:      Optional[int]     							= 1_50
      __snake_case				:      Any     							= 16
      __snake_case				:      Optional[Any]     							= (1, 3_84, 3_84)
      __snake_case				:      int     							= False
      __snake_case				:      List[str]     							= 'project'

   if "ade" in checkpoint_url:
      __snake_case				:      List[str]     							= True
      __snake_case				:      List[str]     							= 7_68
      __snake_case				:      Optional[int]     							= [1, 1, 1, 0.5]
      __snake_case				:      Optional[Any]     							= 1_50
      __snake_case				:      int     							= 16
      __snake_case				:      Any     							= 'huggingface/label-files'
      __snake_case				:      Optional[Any]     							= 'ade20k-id2label.json'
      __snake_case				:      Tuple     							= json.load(open(cached_download(hf_hub_url(A  ,		A  ,		repo_type='dataset'  )  )  ,		'r'  )  )
      __snake_case				:      List[str]     							= {int(A  ): v for k, v in idalabel.items()}
      __snake_case				:      int     							= idalabel
      __snake_case				:      List[Any]     							= {v: k for k, v in idalabel.items()}
      __snake_case				:      Tuple     							= [1, 1_50, 4_80, 4_80]

   return config, expected_shape




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Union[str, Any]     							= ['pretrained.model.head.weight', 'pretrained.model.head.bias']
   for k in ignore_keys:
      state_dict.pop(A  ,		A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  )    -> Any:







   """simple docstring"""
   if (
       "pretrained.model" in name
       and "cls_token" not in name
       and "pos_embed" not in name
       and "patch_embed" not in name
   ):
      __snake_case				:      Union[str, Any]     							= name.replace('pretrained.model'  ,		'dpt.encoder'  )
   if "pretrained.model" in name:
      __snake_case				:      str     							= name.replace('pretrained.model'  ,		'dpt.embeddings'  )
   if "patch_embed" in name:
      __snake_case				:      Union[str, Any]     							= name.replace('patch_embed'  ,		''  )
   if "pos_embed" in name:
      __snake_case				:      Union[str, Any]     							= name.replace('pos_embed'  ,		'position_embeddings'  )
   if "attn.proj" in name:
      __snake_case				:      Union[str, Any]     							= name.replace('attn.proj'  ,		'attention.output.dense'  )
   if "proj" in name and "project" not in name:
      __snake_case				:      Optional[Any]     							= name.replace('proj'  ,		'projection'  )
   if "blocks" in name:
      __snake_case				:      Optional[int]     							= name.replace('blocks'  ,		'layer'  )
   if "mlp.fc1" in name:
      __snake_case				:      int     							= name.replace('mlp.fc1'  ,		'intermediate.dense'  )
   if "mlp.fc2" in name:
      __snake_case				:      Any     							= name.replace('mlp.fc2'  ,		'output.dense'  )
   if "norm1" in name and "backbone" not in name:
      __snake_case				:      Tuple     							= name.replace('norm1'  ,		'layernorm_before'  )
   if "norm2" in name and "backbone" not in name:
      __snake_case				:      Union[str, Any]     							= name.replace('norm2'  ,		'layernorm_after'  )
   if "scratch.output_conv" in name:
      __snake_case				:      List[str]     							= name.replace('scratch.output_conv'  ,		'head'  )
   if "scratch" in name:
      __snake_case				:      Any     							= name.replace('scratch'  ,		'neck'  )
   if "layer1_rn" in name:
      __snake_case				:      List[Any]     							= name.replace('layer1_rn'  ,		'convs.0'  )
   if "layer2_rn" in name:
      __snake_case				:      Optional[Any]     							= name.replace('layer2_rn'  ,		'convs.1'  )
   if "layer3_rn" in name:
      __snake_case				:      List[str]     							= name.replace('layer3_rn'  ,		'convs.2'  )
   if "layer4_rn" in name:
      __snake_case				:      Dict     							= name.replace('layer4_rn'  ,		'convs.3'  )
   if "refinenet" in name:
      __snake_case				:      List[str]     							= int(name[len('neck.refinenet'  ) : len('neck.refinenet'  ) + 1]  )
      # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
      __snake_case				:      Optional[Any]     							= name.replace(F"""refinenet{layer_idx}"""  ,		F"""fusion_stage.layers.{abs(layer_idx-4  )}"""  )
   if "out_conv" in name:
      __snake_case				:      Dict     							= name.replace('out_conv'  ,		'projection'  )
   if "resConfUnit1" in name:
      __snake_case				:      str     							= name.replace('resConfUnit1'  ,		'residual_layer1'  )
   if "resConfUnit2" in name:
      __snake_case				:      Union[str, Any]     							= name.replace('resConfUnit2'  ,		'residual_layer2'  )
   if "conv1" in name:
      __snake_case				:      Optional[Any]     							= name.replace('conv1'  ,		'convolution1'  )
   if "conv2" in name:
      __snake_case				:      Tuple     							= name.replace('conv2'  ,		'convolution2'  )
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      __snake_case				:      str     							= name.replace('pretrained.act_postprocess1.0.project.0'  ,		'neck.reassemble_stage.readout_projects.0.0'  )
   if "pretrained.act_postprocess2.0.project.0" in name:
      __snake_case				:      Dict     							= name.replace('pretrained.act_postprocess2.0.project.0'  ,		'neck.reassemble_stage.readout_projects.1.0'  )
   if "pretrained.act_postprocess3.0.project.0" in name:
      __snake_case				:      Tuple     							= name.replace('pretrained.act_postprocess3.0.project.0'  ,		'neck.reassemble_stage.readout_projects.2.0'  )
   if "pretrained.act_postprocess4.0.project.0" in name:
      __snake_case				:      Optional[int]     							= name.replace('pretrained.act_postprocess4.0.project.0'  ,		'neck.reassemble_stage.readout_projects.3.0'  )

   # resize blocks
   if "pretrained.act_postprocess1.3" in name:
      __snake_case				:      Any     							= name.replace('pretrained.act_postprocess1.3'  ,		'neck.reassemble_stage.layers.0.projection'  )
   if "pretrained.act_postprocess1.4" in name:
      __snake_case				:      Optional[int]     							= name.replace('pretrained.act_postprocess1.4'  ,		'neck.reassemble_stage.layers.0.resize'  )
   if "pretrained.act_postprocess2.3" in name:
      __snake_case				:      str     							= name.replace('pretrained.act_postprocess2.3'  ,		'neck.reassemble_stage.layers.1.projection'  )
   if "pretrained.act_postprocess2.4" in name:
      __snake_case				:      str     							= name.replace('pretrained.act_postprocess2.4'  ,		'neck.reassemble_stage.layers.1.resize'  )
   if "pretrained.act_postprocess3.3" in name:
      __snake_case				:      Tuple     							= name.replace('pretrained.act_postprocess3.3'  ,		'neck.reassemble_stage.layers.2.projection'  )
   if "pretrained.act_postprocess4.3" in name:
      __snake_case				:      str     							= name.replace('pretrained.act_postprocess4.3'  ,		'neck.reassemble_stage.layers.3.projection'  )
   if "pretrained.act_postprocess4.4" in name:
      __snake_case				:      Optional[int]     							= name.replace('pretrained.act_postprocess4.4'  ,		'neck.reassemble_stage.layers.3.resize'  )
   if "pretrained" in name:
      __snake_case				:      List[str]     							= name.replace('pretrained'  ,		'dpt'  )
   if "bn" in name:
      __snake_case				:      List[str]     							= name.replace('bn'  ,		'batch_norm'  )
   if "head" in name:
      __snake_case				:      Optional[Any]     							= name.replace('head'  ,		'head.head'  )
   if "encoder.norm" in name:
      __snake_case				:      Optional[int]     							= name.replace('encoder.norm'  ,		'layernorm'  )
   if "auxlayer" in name:
      __snake_case				:      List[Any]     							= name.replace('auxlayer'  ,		'auxiliary_head.head'  )
   if "backbone" in name:
      __snake_case				:      List[str]     							= name.replace('backbone'  ,		'backbone.bit.encoder'  )

   if ".." in name:
      __snake_case				:      List[str]     							= name.replace('..'  ,		'.'  )

   if "stem.conv" in name:
      __snake_case				:      Any     							= name.replace('stem.conv'  ,		'bit.embedder.convolution'  )
   if "blocks" in name:
      __snake_case				:      List[str]     							= name.replace('blocks'  ,		'layers'  )
   if "convolution" in name and "backbone" in name:
      __snake_case				:      int     							= name.replace('convolution'  ,		'conv'  )
   if "layer" in name and "backbone" in name:
      __snake_case				:      Optional[int]     							= name.replace('layer'  ,		'layers'  )
   if "backbone.bit.encoder.bit" in name:
      __snake_case				:      Union[str, Any]     							= name.replace('backbone.bit.encoder.bit'  ,		'backbone.bit'  )
   if "embedder.conv" in name:
      __snake_case				:      int     							= name.replace('embedder.conv'  ,		'embedder.convolution'  )
   if "backbone.bit.encoder.stem.norm" in name:
      __snake_case				:      str     							= name.replace('backbone.bit.encoder.stem.norm'  ,		'backbone.bit.embedder.norm'  )
   return name




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       Tuple  )    -> Union[str, Any]:







   """simple docstring"""
   for i in range(config.num_hidden_layers  ):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      __snake_case				:      Any     							= state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
      __snake_case				:      Optional[int]     							= state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""  )
      # next, add query, keys and values (in that order) to the state dict
      __snake_case				:      Any     							= in_proj_weight[: config.hidden_size, :]
      __snake_case				:      Dict     							= in_proj_bias[: config.hidden_size]
      __snake_case				:      Union[str, Any]     							= in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __snake_case				:      List[str]     							= in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __snake_case				:      Tuple     							= in_proj_weight[
          -config.hidden_size :, :
      ]
      __snake_case				:      str     							= in_proj_bias[-config.hidden_size :]




def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Tuple     							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
   __snake_case				:      Tuple     							= Image.open(requests.get(A  ,		stream=A  ).raw  )
   return im




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  ,		A  :       str  ,		A  :       int  ,		A  :       List[Any]  ,		A  :       List[Any]  )    -> List[str]:







   """simple docstring"""
   __snake_case							,__snake_case				:      int     							= get_dpt_config(A  )
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   __snake_case				:      Any     							= torch.load(A  ,		map_location='cpu'  )
   # remove certain keys
   remove_ignore_keys_(A  )
   # rename keys
   for key in state_dict.copy().keys():
      __snake_case				:      int     							= state_dict.pop(A  )
      __snake_case				:      List[Any]     							= val
   # read in qkv matrices
   read_in_q_k_v(A  ,		A  )

   # load HuggingFace model
   __snake_case				:      Optional[Any]     							= DPTForSemanticSegmentation(A  ) if 'ade' in checkpoint_url else DPTForDepthEstimation(A  )
   model.load_state_dict(A  )
   model.eval()

   # Check outputs on an image
   __snake_case				:      str     							= 4_80 if 'ade' in checkpoint_url else 3_84
   __snake_case				:      Dict     							= DPTImageProcessor(size=A  )

   __snake_case				:      int     							= prepare_img()
   __snake_case				:      Optional[int]     							= image_processor(A  ,		return_tensors='pt'  )

   # forward pass
   __snake_case				:      List[Any]     							= model(**A  ).logits if 'ade' in checkpoint_url else model(**A  ).predicted_depth

   if show_prediction:
      __snake_case				:      str     							= (
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1  )  ,		size=(image.size[1], image.size[0])  ,		mode='bicubic'  ,		align_corners=A  ,		)
          .squeeze()
          .cpu()
          .numpy()
      )

      Image.fromarray((prediction / prediction.max()) * 2_55  ).show()

   if pytorch_dump_folder_path is not None:
      Path(A  ).mkdir(exist_ok=A  )
      print(F"""Saving model to {pytorch_dump_folder_path}"""  )
      model.save_pretrained(A  )
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
      image_processor.save_pretrained(A  )

   if push_to_hub:
      model.push_to_hub('ybelkada/dpt-hybrid-midas'  )
      image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'  )


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
          type=str,
          help='''URL of the original DPT checkpoint you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default=None,
          type=str,
          required=False,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument(
          '''--push_to_hub''',
          action='''store_true''',
      )
      parser.add_argument(
          '''--model_name''',
          default='''dpt-large''',
          type=str,
          help='''Name of the model, in case you\'re pushing to the hub.''',
      )
      parser.add_argument(
          '''--show_prediction''',
          action='''store_true''',
      )

      __A    	=   parser.parse_args()
      convert_dpt_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
      )







'''simple docstring'''






from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload




class a_    :







   def __init__(self					,   __a = None)			->      None:
      """simple docstring"""






      if components is None:
         __snake_case				:      List[str]     							= []
      __snake_case				:      Optional[int]     							= list(__a)







   def __len__(self)			->      int:
      """simple docstring"""






      return len(self.__components)







   def __str__(self)			->      str:
      """simple docstring"""






      return "(" + ",".join(map(__a					,   self.__components)) + ")"







   def __add__(self					,   __a)			->      Vector:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= len(self)
      if size == len(__a):
         __snake_case				:      Optional[int]     							= [self.__components[i] + other.component(__a) for i in range(__a)]
         return Vector(__a)
      else:
         raise Exception('must have the same size')







   def __sub__(self					,   __a)			->      Vector:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= len(self)
      if size == len(__a):
         __snake_case				:      Optional[int]     							= [self.__components[i] - other.component(__a) for i in range(__a)]
         return Vector(__a)
      else:  # error case
         raise Exception('must have the same size')







   @overload
   def __mul__(self					,   __a)			->      Vector:
      """simple docstring"""






      ...







   @overload
   def __mul__(self					,   __a)			->      float:
      """simple docstring"""






      ...







   def __mul__(self					,   __a)			->      float | Vector:
      """simple docstring"""






      if isinstance(__a					,   (float, int)):
         __snake_case				:      str     							= [c * other for c in self.__components]
         return Vector(__a)
      elif isinstance(__a					,   __a) and len(self) == len(__a):
         __snake_case				:      List[Any]     							= len(self)
         __snake_case				:      Dict     							= [self.__components[i] * other.component(__a) for i in range(__a)]
         return sum(__a)
      else:  # error case
         raise Exception('invalid operand!')







   def     SCREAMING_SNAKE_CASE__					(self)			->      Vector:
      """simple docstring"""






      return Vector(self.__components)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      float:
      """simple docstring"""






      if isinstance(__a					,   __a) and -len(self.__components) <= i < len(self.__components):
         return self.__components[i]
      else:
         raise Exception('index out of range')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      None:
      """simple docstring"""






      assert -len(self.__components) <= pos < len(self.__components)
      __snake_case				:      int     							= value







   def     SCREAMING_SNAKE_CASE__					(self)			->      float:
      """simple docstring"""






      if len(self.__components) == 0:
         raise Exception('Vector is empty')
      __snake_case				:      Tuple     							= [c**2 for c in self.__components]
      return math.sqrt(sum(__a))







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = False)			->      float:
      """simple docstring"""






      __snake_case				:      Tuple     							= self * other
      __snake_case				:      Optional[int]     							= self.euclidean_length() * other.euclidean_length()
      if deg:
         return math.degrees(math.acos(num / den))
      else:
         return math.acos(num / den)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Vector:







   """simple docstring"""
   assert isinstance(A  ,		A  )
   return Vector([0] * dimension  )




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  )    -> Vector:







   """simple docstring"""
   assert isinstance(A  ,		A  ) and (isinstance(A  ,		A  ))
   __snake_case				:      Any     							= [0] * dimension
   __snake_case				:      int     							= 1
   return Vector(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       Vector  ,		A  :       Vector  )    -> Vector:







   """simple docstring"""
   assert (
       isinstance(A  ,		A  )
       and isinstance(A  ,		A  )
       and (isinstance(A  ,		(int, float)  ))
   )
   return x * scalar + y




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  ,		A  :       int  )    -> Vector:







   """simple docstring"""
   random.seed(A  )
   __snake_case				:      List[Any]     							= [random.randint(A  ,		A  ) for _ in range(A  )]
   return Vector(A  )




class a_    :







   def __init__(self					,   __a					,   __a					,   __a)			->      None:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= matrix
      __snake_case				:      int     							= w
      __snake_case				:      str     							= h







   def __str__(self)			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= ''
      for i in range(self.__height):
         ans += "|"
         for j in range(self.__width):
            if j < self.__width - 1:
               ans += str(self.__matrix[i][j]) + ","
            else:
               ans += str(self.__matrix[i][j]) + "|\n"
      return ans







   def __add__(self					,   __a)			->      Matrix:
      """simple docstring"""






      if self.__width == other.width() and self.__height == other.height():
         __snake_case				:      Tuple     							= []
         for i in range(self.__height):
            __snake_case				:      List[Any]     							= [
                self.__matrix[i][j] + other.component(__a					,   __a)
                for j in range(self.__width)
            ]
            matrix.append(__a)
         return Matrix(__a					,   self.__width					,   self.__height)
      else:
         raise Exception('matrix must have the same dimension!')







   def __sub__(self					,   __a)			->      Matrix:
      """simple docstring"""






      if self.__width == other.width() and self.__height == other.height():
         __snake_case				:      str     							= []
         for i in range(self.__height):
            __snake_case				:      List[str]     							= [
                self.__matrix[i][j] - other.component(__a					,   __a)
                for j in range(self.__width)
            ]
            matrix.append(__a)
         return Matrix(__a					,   self.__width					,   self.__height)
      else:
         raise Exception('matrices must have the same dimension!')







   @overload
   def __mul__(self					,   __a)			->      Matrix:
      """simple docstring"""






      ...







   @overload
   def __mul__(self					,   __a)			->      Vector:
      """simple docstring"""






      ...







   def __mul__(self					,   __a)			->      Vector | Matrix:
      """simple docstring"""






      if isinstance(__a					,   __a):  # matrix-vector
         if len(__a) == self.__width:
            __snake_case				:      Tuple     							= zero_vector(self.__height)
            for i in range(self.__height):
               __snake_case				:      Union[str, Any]     							= [
                   self.__matrix[i][j] * other.component(__a)
                   for j in range(self.__width)
               ]
               ans.change_component(__a					,   sum(__a))
            return ans
         else:
            raise Exception(
                'vector must have the same size as the '
                'number of columns of the matrix!')
      elif isinstance(__a					,   (int, float)):  # matrix-scalar
         __snake_case				:      str     							= [
             [self.__matrix[i][j] * other for j in range(self.__width)]
             for i in range(self.__height)
         ]
         return Matrix(__a					,   self.__width					,   self.__height)
      return None







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return self.__height







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return self.__width







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if 0 <= x < self.__height and 0 <= y < self.__width:
         return self.__matrix[x][y]
      else:
         raise Exception('change_component: indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      None:
      """simple docstring"""






      if 0 <= x < self.__height and 0 <= y < self.__width:
         __snake_case				:      List[Any]     							= value
      else:
         raise Exception('change_component: indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      __snake_case				:      List[Any]     							= self.__matrix[:x] + self.__matrix[x + 1 :]
      for i in range(len(__a)):
         __snake_case				:      Tuple     							= minor[i][:y] + minor[i][y + 1 :]
      return Matrix(__a					,   self.__width - 1					,   self.__height - 1).determinant()







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      if 0 <= x < self.__height and 0 <= y < self.__width:
         return (-1) ** (x + y) * self.minor(__a					,   __a)
      else:
         raise Exception('Indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      if self.__height < 1:
         raise Exception('Matrix has no element')
      elif self.__height == 1:
         return self.__matrix[0][0]
      elif self.__height == 2:
         return (
             self.__matrix[0][0] * self.__matrix[1][1]
             - self.__matrix[0][1] * self.__matrix[1][0]
         )
      else:
         __snake_case				:      Any     							= [
             self.__matrix[0][y] * self.cofactor(0					,   __a) for y in range(self.__width)
         ]
         return sum(__a)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Matrix:







   """simple docstring"""
   __snake_case				:      list[list[float]]     							= [[0] * n for _ in range(A  )]
   return Matrix(A  ,		A  ,		A  )





def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  ,		A  :       int  ,		A  :       int  )    -> Matrix:







   """simple docstring"""
   random.seed(A  )
   __snake_case				:      list[list[float]]     							= [
       [random.randint(A  ,		A  ) for _ in range(A  )] for _ in range(A  )
   ]
   return Matrix(A  ,		A  ,		A  )