





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()
__A				:    Optional[int]       =   logging.get_logger(__name__)





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				SwinConfig.from_pretrained(
			    '''microsoft/swin-tiny-patch4-window7-224'''			,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
			UpperCAmelCase   		=				MaskFormerConfig(backbone_config=UpperCamelCase__ )

			UpperCAmelCase   		=				'''huggingface/label-files'''
			if "ade20k-full" in model_name:
						# this should be ok
						UpperCAmelCase   		=				847
						UpperCAmelCase   		=				'''maskformer-ade20k-full-id2label.json'''
			elif "ade" in model_name:
						# this should be ok
						UpperCAmelCase   		=				150
						UpperCAmelCase   		=				'''ade20k-id2label.json'''
			elif "coco-stuff" in model_name:
						# this should be ok
						UpperCAmelCase   		=				171
						UpperCAmelCase   		=				'''maskformer-coco-stuff-id2label.json'''
			elif "coco" in model_name:
						# TODO
						UpperCAmelCase   		=				133
						UpperCAmelCase   		=				'''coco-panoptic-id2label.json'''
			elif "cityscapes" in model_name:
						# this should be ok
						UpperCAmelCase   		=				19
						UpperCAmelCase   		=				'''cityscapes-id2label.json'''
			elif "vistas" in model_name:
						# this should be ok
						UpperCAmelCase   		=				65
						UpperCAmelCase   		=				'''mapillary-vistas-id2label.json'''

			UpperCAmelCase   		=				json.load(open(hf_hub_download(UpperCamelCase__			,			UpperCamelCase__			,			repo_type='''dataset''' )			,			'''r''' ) )
			UpperCAmelCase   		=				{int(UpperCamelCase__ ): v for k, v in idalabel.items()}

			return config





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				[]
			# 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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				dct.pop(UpperCamelCase__ )
			UpperCAmelCase   		=				val





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
			for i in range(len(backbone_config.depths ) ):
						UpperCAmelCase   		=				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)
									UpperCAmelCase   		=				state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
									UpperCAmelCase   		=				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
									UpperCAmelCase   		=				in_proj_weight[:dim, :]
									UpperCAmelCase   		=				in_proj_bias[: dim]
									UpperCAmelCase   		=				in_proj_weight[
									    dim : dim * 2, :
									]
									UpperCAmelCase   		=				in_proj_bias[
									    dim : dim * 2
									]
									UpperCAmelCase   		=				in_proj_weight[
									    -dim :, :
									]
									UpperCAmelCase   		=				in_proj_bias[-dim :]
									# fmt: on





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				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)
						UpperCAmelCase   		=				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
						UpperCAmelCase   		=				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
						UpperCAmelCase   		=				in_proj_weight[: hidden_size, :]
						UpperCAmelCase   		=				in_proj_bias[:config.hidden_size]
						UpperCAmelCase   		=				in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCAmelCase   		=				in_proj_bias[hidden_size : hidden_size * 2]
						UpperCAmelCase   		=				in_proj_weight[-hidden_size :, :]
						UpperCAmelCase   		=				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)
						UpperCAmelCase   		=				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
						UpperCAmelCase   		=				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
						UpperCAmelCase   		=				in_proj_weight[: hidden_size, :]
						UpperCAmelCase   		=				in_proj_bias[:config.hidden_size]
						UpperCAmelCase   		=				in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCAmelCase   		=				in_proj_bias[hidden_size : hidden_size * 2]
						UpperCAmelCase   		=				in_proj_weight[-hidden_size :, :]
						UpperCAmelCase   		=				in_proj_bias[-hidden_size :]
			# fmt: on





def 						__SCREAMING_SNAKE_CASE  (				)  ->				torch.Tensor:







			'''simple docstring'''

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





@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = False )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				get_maskformer_config(UpperCamelCase__ )

			# load original state_dict
			with open(UpperCamelCase__			,			'''rb''' ) as f:
						UpperCAmelCase   		=				pickle.load(UpperCamelCase__ )
			UpperCAmelCase   		=				data['''model''']

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

			# rename keys
			UpperCAmelCase   		=				create_rename_keys(UpperCamelCase__ )
			for src, dest in rename_keys:
						rename_key(UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )
			read_in_swin_q_k_v(UpperCamelCase__			,			config.backbone_config )
			read_in_decoder_q_k_v(UpperCamelCase__			,			UpperCamelCase__ )

			# update to torch tensors
			for key, value in state_dict.items():
						UpperCAmelCase   		=				torch.from_numpy(UpperCamelCase__ )

			# load 🤗 model
			UpperCAmelCase   		=				MaskFormerForInstanceSegmentation(UpperCamelCase__ )
			model.eval()

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

			UpperCAmelCase		,			UpperCAmelCase   		=				model.load_state_dict(UpperCamelCase__			,			strict=UpperCamelCase__ )
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(UpperCamelCase__ ) == 0, F"""Unexpected keys: {unexpected_keys}"""

			# verify results
			UpperCAmelCase   		=				prepare_img()
			if "vistas" in model_name:
						UpperCAmelCase   		=				65
			elif "cityscapes" in model_name:
						UpperCAmelCase   		=				6_5535
			else:
						UpperCAmelCase   		=				255
			UpperCAmelCase   		=				True if '''ade''' in model_name else False
			UpperCAmelCase   		=				MaskFormerImageProcessor(ignore_index=UpperCamelCase__			,			reduce_labels=UpperCamelCase__ )

			UpperCAmelCase   		=				image_processor(UpperCamelCase__			,			return_tensors='''pt''' )

			UpperCAmelCase   		=				model(**UpperCamelCase__ )

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

			if model_name == "maskformer-swin-tiny-ade":
						UpperCAmelCase   		=				torch.tensor(
						    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,			UpperCamelCase__			,			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(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
						model.save_pretrained(UpperCamelCase__ )
						image_processor.save_pretrained(UpperCamelCase__ )

			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__":
				__A				:    Tuple       =   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."
				)

				__A				:    str       =   parser.parse_args()
				convert_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)










import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cva.imread(_A							,					0		)
				UpperCAmelCase		,			UpperCAmelCase   		=				img.shape
				UpperCAmelCase   		=				[]
				UpperCAmelCase   		=				img.copy()
				UpperCAmelCase   		=				cva.cvtColor(_A							,					cva.COLOR_GRAY2RGB		)
				UpperCAmelCase		,			UpperCAmelCase   		=				np.gradient(_A		)
				UpperCAmelCase   		=				dx**2
				UpperCAmelCase   		=				dy**2
				UpperCAmelCase   		=				dx * dy
				UpperCAmelCase   		=				0.04
				UpperCAmelCase   		=				self.window_size // 2
				for y in range(_A							,					h - offset		):
							for x in range(_A							,					w - offset		):
										UpperCAmelCase   		=				ixx[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				iyy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				ixy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				det - k * (trace**2)
										# Can change the value
										if r > 0.5:
													corner_list.append([x, y, r]		)
													color_img.itemset((y, x, 0)							,					0		)
													color_img.itemset((y, x, 1)							,					0		)
													color_img.itemset((y, x, 2)							,					2_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



