





"""simple docstring"""





import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =SwinConfig.from_pretrained(
							    '''microsoft/swin-tiny-patch4-window7-224'''    ,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']   )
							__lowerCamelCase      :      Tuple         =MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Dict         ='''huggingface/label-files'''
							if "ade20k-full" in model_name:
														# this should be ok
														__lowerCamelCase      :      Any         =847
														__lowerCamelCase      :      Any         ='''maskformer-ade20k-full-id2label.json'''
							elif "ade" in model_name:
														# this should be ok
														__lowerCamelCase      :      Tuple         =150
														__lowerCamelCase      :      Optional[int]         ='''ade20k-id2label.json'''
							elif "coco-stuff" in model_name:
														# this should be ok
														__lowerCamelCase      :      List[Any]         =171
														__lowerCamelCase      :      int         ='''maskformer-coco-stuff-id2label.json'''
							elif "coco" in model_name:
														# TODO
														__lowerCamelCase      :      Dict         =133
														__lowerCamelCase      :      List[Any]         ='''coco-panoptic-id2label.json'''
							elif "cityscapes" in model_name:
														# this should be ok
														__lowerCamelCase      :      str         =19
														__lowerCamelCase      :      Optional[Any]         ='''cityscapes-id2label.json'''
							elif "vistas" in model_name:
														# this should be ok
														__lowerCamelCase      :      str         =65
														__lowerCamelCase      :      List[Any]         ='''mapillary-vistas-id2label.json'''

							__lowerCamelCase      :      str         =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						repo_type='''dataset'''   )    ,						'''r'''   )   )
							__lowerCamelCase      :      Dict         ={int(SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}

							return config



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =[]
							# stem
							# fmt: off
							rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')   )
							rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')   )
							rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')   )
							rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')   )
							# stages
							for i in range(len(config.backbone_config.depths   )   ):
														for j in range(config.backbone_config.depths[i]   ):
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')   )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')   )

														if i < 3:
																					rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')   )
																					rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')   )
																					rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')   )
														rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')   )
														rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')   )

							# FPN
							rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')   )
							rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')   )
							rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')   )
							for source_index, target_index in zip(range(3    ,						0    ,						-1   )    ,						range(0    ,						3   )   ):
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')   )
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')   )
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')   )
														rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')   )
														rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')   )
														rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')   )
							rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')   )
							rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')   )

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers   ):
														# self-attention out projection
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')   )
														# cross-attention out projection
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')   )
														# MLP 1
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')   )
														# MLP 2
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')   )
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')   )
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')   )
														# layernorm 3 (final layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')   )

							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')   )
							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')   )

							# heads on top
							rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')   )

							rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')   )
							rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')   )

							rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')   )
							rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')   )

							for i in range(3   ):
														rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')   )
														rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')   )
							# fmt: on

							return rename_keys



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =dct.pop(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Dict         =val



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
							for i in range(len(backbone_config.depths   )   ):
														__lowerCamelCase      :      List[Any]         =num_features[i]
														for j in range(backbone_config.depths[i]   ):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					__lowerCamelCase      :      Optional[int]         =state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'   )
																					__lowerCamelCase      :      List[str]         =state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'   )
																					# next, add query, keys and values (in that order) to the state dict
																					__lowerCamelCase      :      int         =in_proj_weight[:dim, :]
																					__lowerCamelCase      :      int         =in_proj_bias[: dim]
																					__lowerCamelCase      :      str         =in_proj_weight[
																					    dim : dim * 2, :
																					]
																					__lowerCamelCase      :      Union[str, Any]         =in_proj_bias[
																					    dim : dim * 2
																					]
																					__lowerCamelCase      :      List[str]         =in_proj_weight[
																					    -dim :, :
																					]
																					__lowerCamelCase      :      Union[str, Any]         =in_proj_bias[-dim :]
																					# fmt: on



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers   ):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														__lowerCamelCase      :      int         =state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'   )
														__lowerCamelCase      :      List[str]         =state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'   )
														# next, add query, keys and values (in that order) to the state dict
														__lowerCamelCase      :      List[Any]         =in_proj_weight[: hidden_size, :]
														__lowerCamelCase      :      int         =in_proj_bias[:config.hidden_size]
														__lowerCamelCase      :      Optional[int]         =in_proj_weight[hidden_size : hidden_size * 2, :]
														__lowerCamelCase      :      Optional[int]         =in_proj_bias[hidden_size : hidden_size * 2]
														__lowerCamelCase      :      Dict         =in_proj_weight[-hidden_size :, :]
														__lowerCamelCase      :      Optional[Any]         =in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														__lowerCamelCase      :      Any         =state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'   )
														__lowerCamelCase      :      str         =state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'   )
														# next, add query, keys and values (in that order) to the state dict
														__lowerCamelCase      :      List[Any]         =in_proj_weight[: hidden_size, :]
														__lowerCamelCase      :      Optional[Any]         =in_proj_bias[:config.hidden_size]
														__lowerCamelCase      :      str         =in_proj_weight[hidden_size : hidden_size * 2, :]
														__lowerCamelCase      :      List[str]         =in_proj_bias[hidden_size : hidden_size * 2]
														__lowerCamelCase      :      Dict         =in_proj_weight[-hidden_size :, :]
														__lowerCamelCase      :      List[str]         =in_proj_bias[-hidden_size :]
							# fmt: on



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowerCamelCase      :      List[Any]         =Image.open(requests.get(SCREAMING_SNAKE_CASE    ,						stream=SCREAMING_SNAKE_CASE   ).raw   )
							return im



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							bool = False   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =get_maskformer_config(SCREAMING_SNAKE_CASE   )

							# load original state_dict
							with open(SCREAMING_SNAKE_CASE    ,						'''rb'''   ) as f:
														__lowerCamelCase      :      Any         =pickle.load(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      int         =data['''model''']

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							__lowerCamelCase      :      List[Any]         =create_rename_keys(SCREAMING_SNAKE_CASE   )
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							read_in_swin_q_k_v(SCREAMING_SNAKE_CASE    ,						config.backbone_config   )
							read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# update to torch tensors
							for key, value in state_dict.items():
														__lowerCamelCase      :      Dict         =torch.from_numpy(SCREAMING_SNAKE_CASE   )

							# load 🤗 model
							__lowerCamelCase      :      List[Any]         =MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE   )
							model.eval()

							for name, param in model.named_parameters():
														print(SCREAMING_SNAKE_CASE    ,						param.shape   )

							__lowerCamelCase    ,						__lowerCamelCase      :      List[str]         =model.load_state_dict(SCREAMING_SNAKE_CASE    ,						strict=SCREAMING_SNAKE_CASE   )
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(SCREAMING_SNAKE_CASE   ) == 0, F'Unexpected keys: {unexpected_keys}'

							# verify results
							__lowerCamelCase      :      Tuple         =prepare_img()
							if "vistas" in model_name:
														__lowerCamelCase      :      Tuple         =65
							elif "cityscapes" in model_name:
														__lowerCamelCase      :      Any         =65535
							else:
														__lowerCamelCase      :      Dict         =255
							__lowerCamelCase      :      List[str]         =True if '''ade''' in model_name else False
							__lowerCamelCase      :      List[str]         =MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE    ,						reduce_labels=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      List[Any]         =image_processor(SCREAMING_SNAKE_CASE    ,						return_tensors='''pt'''   )

							__lowerCamelCase      :      int         =model(**SCREAMING_SNAKE_CASE   )

							print('''Logits:'''    ,						outputs.class_queries_logits[0, :3, :3]   )

							if model_name == "maskformer-swin-tiny-ade":
														__lowerCamelCase      :      Dict         =torch.tensor(
														    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]   )
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-4   )
							print('''Looks ok!'''   )

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

							if push_to_hub:
														print('''Pushing model and image processor to the hub...'''   )
														model.push_to_hub(F'nielsr/{model_name}'   )
														image_processor.push_to_hub(F'nielsr/{model_name}'   )


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='maskformer-swin-tiny-ade',
							    type=str,
							    help=('Name of the MaskFormer model you\'d like to convert',),
							)
							parser.add_argument(
							    '--checkpoint_path',
							    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
							    type=str,
							    help='Path to the original state dict (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							_UpperCamelCase							=      parser.parse_args()
							convert_maskformer_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)











"""simple docstring"""





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Optional[Any]						,    __lowercase  :Collection[float] | None = None			):
											if components is None:
																		__lowerCamelCase      :      str         =[]
											__lowerCamelCase      :      int         =list(__lowercase			)




				def __len__(     self  :Union[str, Any]			):
											return len(self.__components			)




				def __str__(     self  :List[Any]			):
											return "(" + ",".join(map(__lowercase						,    self.__components			)			) + ")"




				def __add__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											__lowerCamelCase      :      Tuple         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      int         =[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  :int						,    __lowercase  :Vector			):
											__lowerCamelCase      :      int         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      Union[str, Any]         =[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  :List[str]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											...




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




				def 					__lowercase  (     self  :Optional[Any]			):
											return Vector(self.__components			)




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




				def 					__lowercase  (     self  :int						,    __lowercase  :int						,    __lowercase  :float			):
											assert -len(self.__components			) <= pos < len(self.__components			)
											__lowerCamelCase      :      int         =value




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




				def 					__lowercase  (     self  :Any						,    __lowercase  :Vector						,    __lowercase  :bool = False			):
											__lowerCamelCase      :      Tuple         =self * other
											__lowerCamelCase      :      str         =self.euclidean_length() * other.euclidean_length()
											if deg:
																		return math.degrees(math.acos(num / den			)			)
											else:
																		return math.acos(num / den			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) and (isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ))
							__lowerCamelCase      :      List[Any]         =[0] * dimension
							__lowerCamelCase      :      Union[str, Any]         =1
							return Vector(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							Vector    ,						SCREAMING_SNAKE_CASE       :							Vector   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =[random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )]
							return Vector(SCREAMING_SNAKE_CASE   )
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :list[list[float]]						,    __lowercase  :int						,    __lowercase  :int			):
											__lowerCamelCase      :      Optional[Any]         =matrix
											__lowerCamelCase      :      int         =w
											__lowerCamelCase      :      str         =h




				def __str__(     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =''''''
											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  :Any						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      Optional[int]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Optional[int]         =[
																									    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  :Tuple						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      List[str]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Dict         =[
																									    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[Any]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :Union[str, Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    __lowercase			):  # matrix-vector
																		if len(__lowercase			) == self.__width:
																									__lowerCamelCase      :      int         =zero_vector(self.__height			)
																									for i in range(self.__height			):
																																__lowerCamelCase      :      Union[str, Any]         =[
																																    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
																		__lowerCamelCase      :      Optional[Any]         =[
																		    [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 					__lowercase  (     self  :Union[str, Any]			):
											return self.__height




				def 					__lowercase  (     self  :Tuple			):
											return self.__width




				def 					__lowercase  (     self  :Any						,    __lowercase  :int						,    __lowercase  :int			):
											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 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :float			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		__lowerCamelCase      :      int         =value
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




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




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int			):
											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 					__lowercase  (     self  :Optional[Any]			):
											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:
																		__lowerCamelCase      :      int         =[
																		    self.__matrix[0][y] * self.cofactor(0						,    __lowercase			) for y in range(self.__width			)
																		]
																		return sum(__lowercase			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      list[list[float]]         =[[0] * n for _ in range(SCREAMING_SNAKE_CASE   )]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      list[list[float]]         =[
							    [random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )] for _ in range(SCREAMING_SNAKE_CASE   )
							]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )




