



"""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()
lowerCAmelCase_  		=  logging.get_logger(__name__)



def 	__UpperCAmelCase (       __lowerCamelCase      )				-> Optional[int]:
		lowercase__  :       Any												=     SwinConfig.from_pretrained(
		    '''microsoft/swin-tiny-patch4-window7-224'''		,	out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']      )
		lowercase__  :       str												=     MaskFormerConfig(backbone_config=__lowerCAmelCase      )

		lowercase__  :       Tuple												=     '''huggingface/label-files'''
		if "ade20k-full" in model_name:
				# this should be ok
				lowercase__  :       Any												=     8_47
				lowercase__  :       List[Any]												=     '''maskformer-ade20k-full-id2label.json'''
		elif "ade" in model_name:
				# this should be ok
				lowercase__  :       Tuple												=     1_50
				lowercase__  :       Optional[Any]												=     '''ade20k-id2label.json'''
		elif "coco-stuff" in model_name:
				# this should be ok
				lowercase__  :       Optional[int]												=     1_71
				lowercase__  :       Optional[int]												=     '''maskformer-coco-stuff-id2label.json'''
		elif "coco" in model_name:
				# TODO
				lowercase__  :       Optional[int]												=     1_33
				lowercase__  :       Optional[int]												=     '''coco-panoptic-id2label.json'''
		elif "cityscapes" in model_name:
				# this should be ok
				lowercase__  :       Any												=     19
				lowercase__  :       Union[str, Any]												=     '''cityscapes-id2label.json'''
		elif "vistas" in model_name:
				# this should be ok
				lowercase__  :       Optional[int]												=     65
				lowercase__  :       Optional[Any]												=     '''mapillary-vistas-id2label.json'''

		lowercase__  :       List[str]												=     json.load(open(hf_hub_download(__lowerCAmelCase		,	__lowerCAmelCase		,	repo_type='''dataset'''      )		,	'''r'''      )      )
		lowercase__  :       Optional[Any]												=     {int(__lowerCAmelCase      ): v for k, v in idalabel.items()}

		return config



def 	__UpperCAmelCase (       __lowerCamelCase      )				-> Any:
		lowercase__  :       Optional[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 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase      )				-> Tuple:
		lowercase__  :       Dict												=     dct.pop(__lowerCAmelCase      )
		lowercase__  :       Optional[int]												=     val



def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase      )				-> Tuple:
		lowercase__  :       Union[str, Any]												=     [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
		for i in range(len(backbone_config.depths      )      ):
				lowercase__  :       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)
						lowercase__  :       Optional[Any]												=     state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""      )
						lowercase__  :       Optional[Any]												=     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
						lowercase__  :       Tuple												=     in_proj_weight[:dim, :]
						lowercase__  :       Optional[int]												=     in_proj_bias[: dim]
						lowercase__  :       Dict												=     in_proj_weight[
						    dim : dim * 2, :
						]
						lowercase__  :       Optional[int]												=     in_proj_bias[
						    dim : dim * 2
						]
						lowercase__  :       str												=     in_proj_weight[
						    -dim :, :
						]
						lowercase__  :       List[str]												=     in_proj_bias[-dim :]
						# fmt: on



def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase      )				-> Any:
		# fmt: off
		lowercase__  :       int												=     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)
				lowercase__  :       int												=     state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""      )
				lowercase__  :       Dict												=     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
				lowercase__  :       int												=     in_proj_weight[: hidden_size, :]
				lowercase__  :       List[Any]												=     in_proj_bias[:config.hidden_size]
				lowercase__  :       Any												=     in_proj_weight[hidden_size : hidden_size * 2, :]
				lowercase__  :       Dict												=     in_proj_bias[hidden_size : hidden_size * 2]
				lowercase__  :       int												=     in_proj_weight[-hidden_size :, :]
				lowercase__  :       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)
				lowercase__  :       Any												=     state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""      )
				lowercase__  :       Any												=     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
				lowercase__  :       List[str]												=     in_proj_weight[: hidden_size, :]
				lowercase__  :       str												=     in_proj_bias[:config.hidden_size]
				lowercase__  :       str												=     in_proj_weight[hidden_size : hidden_size * 2, :]
				lowercase__  :       List[Any]												=     in_proj_bias[hidden_size : hidden_size * 2]
				lowercase__  :       Union[str, Any]												=     in_proj_weight[-hidden_size :, :]
				lowercase__  :       Optional[Any]												=     in_proj_bias[-hidden_size :]
		# fmt: on



def 	__UpperCAmelCase (       )				-> torch.Tensor:
		lowercase__  :       Any												=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__  :       str												=     Image.open(requests.get(__lowerCAmelCase		,	stream=__lowerCAmelCase      ).raw      )
		return im



@torch.no_grad()
def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase = False      )				-> List[Any]:
		lowercase__  :       str												=     get_maskformer_config(__lowerCAmelCase      )

		# load original state_dict
		with open(__lowerCAmelCase		,	'''rb'''      ) as f:
				lowercase__  :       Tuple												=     pickle.load(__lowerCAmelCase      )
		lowercase__  :       Optional[Any]												=     data['''model''']

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

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

		# update to torch tensors
		for key, value in state_dict.items():
				lowercase__  :       List[Any]												=     torch.from_numpy(__lowerCAmelCase      )

		# load 🤗 model
		lowercase__  :       List[str]												=     MaskFormerForInstanceSegmentation(__lowerCAmelCase      )
		model.eval()

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

		lowercase__						,     lowercase__  :       Tuple												=     model.load_state_dict(__lowerCAmelCase		,	strict=__lowerCAmelCase      )
		assert missing_keys == [
		    "model.pixel_level_module.encoder.model.layernorm.weight",
		    "model.pixel_level_module.encoder.model.layernorm.bias",
		]
		assert len(__lowerCAmelCase      ) == 0, f"""Unexpected keys: {unexpected_keys}"""

		# verify results
		lowercase__  :       List[str]												=     prepare_img()
		if "vistas" in model_name:
				lowercase__  :       Optional[Any]												=     65
		elif "cityscapes" in model_name:
				lowercase__  :       int												=     6_55_35
		else:
				lowercase__  :       str												=     2_55
		lowercase__  :       int												=     True if '''ade''' in model_name else False
		lowercase__  :       Union[str, Any]												=     MaskFormerImageProcessor(ignore_index=__lowerCAmelCase		,	reduce_labels=__lowerCAmelCase      )

		lowercase__  :       Tuple												=     image_processor(__lowerCAmelCase		,	return_tensors='''pt'''      )

		lowercase__  :       str												=     model(**__lowerCAmelCase      )

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

		if model_name == "maskformer-swin-tiny-ade":
				lowercase__  :       List[Any]												=     torch.tensor(
				    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]      )
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,	__lowerCAmelCase		,	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(__lowerCAmelCase      ).mkdir(exist_ok=__lowerCAmelCase      )
				model.save_pretrained(__lowerCAmelCase      )
				image_processor.save_pretrained(__lowerCAmelCase      )

		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__":
							lowerCAmelCase_  		=  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.'
							)

							lowerCAmelCase_  		=  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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							int		)		->  Tuple:  # noqa: E741
   while r - l > 1:
      snake_case 						=   (l + r) // 2
      if v[m] >= key:
         snake_case 						=   m
      else:
         snake_case 						=   m  # noqa: E741
   return r





def 	__lowerCamelCase			(      __lowerCAmelCase       :							list[int]		)		->  int:
   if len(__lowerCAmelCase		) == 0:
      return 0

   snake_case 						=   [0] * len(__lowerCAmelCase		)
   snake_case 						=   1

   snake_case 						=   v[0]

   for i in range(1      ,    len(__lowerCAmelCase		)		):
      if v[i] < tail[0]:
         snake_case 						=   v[i]
      elif v[i] > tail[length - 1]:
         snake_case 						=   v[i]
         length += 1
      else:
         snake_case 						=   v[i]

   return length


if __name__ == "__main__":
 import doctest

 doctest.testmod()

