

'''simple docstring'''

from __future__ import annotations

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


class 	lowerCAmelCase  :







       def __init__(				self      :			List[str]    ,   __lowercase      :			Collection[float] | None = None   ):







            """simple docstring"""







            if components is None:
                 __lowercase      		=[]
            __lowercase      		=list(__lowercase   )







       def __len__(				self      :			Optional[int]   ):







            """simple docstring"""







            return len(self.__components   )







       def __str__(				self      :			str   ):







            """simple docstring"""







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







       def __add__(				self      :			Optional[Any]    ,   __lowercase      :			Vector   ):







            """simple docstring"""







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







       def __sub__(				self      :			Optional[Any]    ,   __lowercase      :			Vector   ):







            """simple docstring"""







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







       @overload
       def __mul__(				self      :			Tuple    ,   __lowercase      :			float   ):







            """simple docstring"""







            ...







       @overload
       def __mul__(				self      :			Dict    ,   __lowercase      :			Vector   ):







            """simple docstring"""







            ...







       def __mul__(				self      :			Optional[int]    ,   __lowercase      :			float | Vector   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            return Vector(self.__components   )







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			int   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			int    ,   __lowercase      :			float   ):







            """simple docstring"""







            assert -len(self.__components   ) <= pos < len(self.__components   )
            __lowercase      		=value







       def       snake_case (				self      :			Any   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Any    ,   __lowercase      :			Vector    ,   __lowercase      :			bool = False   ):







            """simple docstring"""







            __lowercase      		=self * other
            __lowercase      		=self.euclidean_length() * other.euclidean_length()
            if deg:
                 return math.degrees(math.acos(num / den   )   )
            else:
                 return math.acos(num / den   )






def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     assert isinstance(lowercase__,       lowercase__			)
     return Vector([0] * dimension			)

def 					__UpperCamelCase				(						lowercase__    :	int,       lowercase__    :	int			):






     '''simple docstring'''





     assert isinstance(lowercase__,       lowercase__			) and (isinstance(lowercase__,       lowercase__			))
     __lowercase      		=[0] * dimension
     __lowercase      		=1
     return Vector(lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	float,       lowercase__    :	Vector,       lowercase__    :	Vector			):






     '''simple docstring'''





     assert (
         isinstance(lowercase__,       lowercase__			)
         and isinstance(lowercase__,       lowercase__			)
         and (isinstance(lowercase__,       (int, float)			))
     )
     return x * scalar + y

def 					__UpperCamelCase				(						lowercase__    :	int,       lowercase__    :	int,       lowercase__    :	int			):






     '''simple docstring'''





     random.seed(lowercase__			)
     __lowercase      		=[random.randint(lowercase__,       lowercase__			) for _ in range(lowercase__			)]
     return Vector(lowercase__			)


class 	lowerCAmelCase  :







       def __init__(				self      :			List[str]    ,   __lowercase      :			list[list[float]]    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            __lowercase      		=matrix
            __lowercase      		=w
            __lowercase      		=h







       def __str__(				self      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=''
            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      :			Dict    ,   __lowercase      :			Matrix   ):







            """simple docstring"""







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







       def __sub__(				self      :			Optional[Any]    ,   __lowercase      :			Matrix   ):







            """simple docstring"""







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







       @overload
       def __mul__(				self      :			List[str]    ,   __lowercase      :			float   ):







            """simple docstring"""







            ...







       @overload
       def __mul__(				self      :			Optional[int]    ,   __lowercase      :			Vector   ):







            """simple docstring"""







            ...







       def __mul__(				self      :			Union[str, Any]    ,   __lowercase      :			float | Vector   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            return self.__height







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            return self.__width







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """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       snake_case (				self      :			List[str]    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			float   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Any    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """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(__lowercase    ,   __lowercase   )
            else:
                 raise Exception('Indices out of bounds'   )







       def       snake_case (				self      :			Tuple   ):







            """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:
                 __lowercase      		=[
                     self.__matrix[0][y] * self.cofactor(0    ,   __lowercase   ) for y in range(self.__width   )
                 ]
                 return sum(__lowercase   )






def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=[[0] * n for _ in range(lowercase__			)]
     return Matrix(lowercase__,       lowercase__,       lowercase__			)







def 					__UpperCamelCase				(						lowercase__    :	int,       lowercase__    :	int,       lowercase__    :	int,       lowercase__    :	int			):






     '''simple docstring'''





     random.seed(lowercase__			)
     __lowercase      		=[
         [random.randint(lowercase__,       lowercase__			) for _ in range(lowercase__			)] for _ in range(lowercase__			)
     ]
     return Matrix(lowercase__,       lowercase__,       lowercase__			)





'''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()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	Dict			):






     '''simple docstring'''





     __lowercase      		=DPTConfig()

     if "large" in checkpoint_url:
          __lowercase      		=10_24
          __lowercase      		=40_96
          __lowercase      		=24
          __lowercase      		=16
          __lowercase      		=[5, 11, 17, 23]
          __lowercase      		=[2_56, 5_12, 10_24, 10_24]
          __lowercase      		=(1, 3_84, 3_84)

     if "ade" in checkpoint_url:
          __lowercase      		=True

          __lowercase      		=1_50
          __lowercase      		='huggingface/label-files'
          __lowercase      		='ade20k-id2label.json'
          __lowercase      		=json.load(open(cached_download(hf_hub_url(lowercase__,       lowercase__,       repo_type='dataset'			)			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}
          __lowercase      		=[1, 1_50, 4_80, 4_80]

     return config, expected_shape

def 					__UpperCamelCase				(						lowercase__    :	Optional[int]			):






     '''simple docstring'''





     __lowercase      		=['pretrained.model.head.weight', 'pretrained.model.head.bias']
     for k in ignore_keys:
          state_dict.pop(lowercase__,       lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	Union[str, 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
     ):
          __lowercase      		=name.replace('pretrained.model',       'dpt.encoder'			)
     if "pretrained.model" in name:
          __lowercase      		=name.replace('pretrained.model',       'dpt.embeddings'			)
     if "patch_embed" in name:
          __lowercase      		=name.replace('patch_embed',       'patch_embeddings'			)
     if "pos_embed" in name:
          __lowercase      		=name.replace('pos_embed',       'position_embeddings'			)
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "proj" in name and "project" not in name:
          __lowercase      		=name.replace('proj',       'projection'			)
     if "blocks" in name:
          __lowercase      		=name.replace('blocks',       'layer'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "scratch.output_conv" in name:
          __lowercase      		=name.replace('scratch.output_conv',       'head'			)
     if "scratch" in name:
          __lowercase      		=name.replace('scratch',       'neck'			)
     if "layer1_rn" in name:
          __lowercase      		=name.replace('layer1_rn',       'convs.0'			)
     if "layer2_rn" in name:
          __lowercase      		=name.replace('layer2_rn',       'convs.1'			)
     if "layer3_rn" in name:
          __lowercase      		=name.replace('layer3_rn',       'convs.2'			)
     if "layer4_rn" in name:
          __lowercase      		=name.replace('layer4_rn',       'convs.3'			)
     if "refinenet" in name:
          __lowercase      		=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
          __lowercase      		=name.replace(F'''refinenet{layer_idx}''',       F'''fusion_stage.layers.{abs(layer_idx-4			)}'''			)
     if "out_conv" in name:
          __lowercase      		=name.replace('out_conv',       'projection'			)
     if "resConfUnit1" in name:
          __lowercase      		=name.replace('resConfUnit1',       'residual_layer1'			)
     if "resConfUnit2" in name:
          __lowercase      		=name.replace('resConfUnit2',       'residual_layer2'			)
     if "conv1" in name:
          __lowercase      		=name.replace('conv1',       'convolution1'			)
     if "conv2" in name:
          __lowercase      		=name.replace('conv2',       'convolution2'			)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.0.project.0',       'neck.reassemble_stage.readout_projects.0.0'			)
     if "pretrained.act_postprocess2.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.0.project.0',       'neck.reassemble_stage.readout_projects.1.0'			)
     if "pretrained.act_postprocess3.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess3.0.project.0',       'neck.reassemble_stage.readout_projects.2.0'			)
     if "pretrained.act_postprocess4.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.0.project.0',       'neck.reassemble_stage.readout_projects.3.0'			)
     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.3',       'neck.reassemble_stage.layers.0.projection'			)
     if "pretrained.act_postprocess1.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.4',       'neck.reassemble_stage.layers.0.resize'			)
     if "pretrained.act_postprocess2.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.3',       'neck.reassemble_stage.layers.1.projection'			)
     if "pretrained.act_postprocess2.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.4',       'neck.reassemble_stage.layers.1.resize'			)
     if "pretrained.act_postprocess3.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess3.3',       'neck.reassemble_stage.layers.2.projection'			)
     if "pretrained.act_postprocess4.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.3',       'neck.reassemble_stage.layers.3.projection'			)
     if "pretrained.act_postprocess4.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.4',       'neck.reassemble_stage.layers.3.resize'			)
     if "pretrained" in name:
          __lowercase      		=name.replace('pretrained',       'dpt'			)
     if "bn" in name:
          __lowercase      		=name.replace('bn',       'batch_norm'			)
     if "head" in name:
          __lowercase      		=name.replace('head',       'head.head'			)
     if "encoder.norm" in name:
          __lowercase      		=name.replace('encoder.norm',       'layernorm'			)
     if "auxlayer" in name:
          __lowercase      		=name.replace('auxlayer',       'auxiliary_head.head'			)

     return name

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	Optional[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)
          __lowercase      		=state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[
              -config.hidden_size :, :
          ]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		='http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowercase      		=Image.open(requests.get(lowercase__,       stream=lowercase__			).raw			)
     return im

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	Optional[Any],       lowercase__    :	str,       lowercase__    :	List[str],       lowercase__    :	List[str]			):






     '''simple docstring'''





     __lowercase		,  __lowercase      		=get_dpt_config(lowercase__			)
     # load original state_dict from URL
     __lowercase      		=torch.hub.load_state_dict_from_url(lowercase__,       map_location='cpu'			)
     # remove certain keys
     remove_ignore_keys_(lowercase__			)
     # rename keys
     for key in state_dict.copy().keys():
          __lowercase      		=state_dict.pop(lowercase__			)
          __lowercase      		=val
     # read in qkv matrices
     read_in_q_k_v(lowercase__,       lowercase__			)

     # load HuggingFace model
     __lowercase      		=DPTForSemanticSegmentation(lowercase__			) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase__			)
     model.load_state_dict(lowercase__			)
     model.eval()

     # Check outputs on an image
     __lowercase      		=4_80 if 'ade' in checkpoint_url else 3_84
     __lowercase      		=DPTImageProcessor(size=lowercase__			)

     __lowercase      		=prepare_img()
     __lowercase      		=image_processor(lowercase__,       return_tensors='pt'			)

     # forward pass
     __lowercase      		=model(**lowercase__			).logits if 'ade' in checkpoint_url else model(**lowercase__			).predicted_depth

     # Assert logits
     __lowercase      		=torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]			)
     if "ade" in checkpoint_url:
          __lowercase      		=torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]			)
     assert outputs.shape == torch.Size(lowercase__			)
     assert (
         torch.allclose(outputs[0, 0, :3, :3],       lowercase__,       atol=1E-4			)
         if "ade" in checkpoint_url
         else torch.allclose(outputs[0, :3, :3],       lowercase__			)
     )

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(F'''Saving model 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 push_to_hub:
          print('Pushing model to hub...'			)
          model.push_to_hub(
              repo_path_or_name=Path(lowercase__,       lowercase__			),       organization='nielsr',       commit_message='Add model',       use_temp_dir=lowercase__,       )
          image_processor.push_to_hub(
              repo_path_or_name=Path(lowercase__,       lowercase__			),       organization='nielsr',       commit_message='Add image processor',       use_temp_dir=lowercase__,       )


if __name__ == "__main__":
       UpperCAmelCase					 =	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=True,
           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.''',
       )

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


