





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()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			int											=	SwinConfig.from_pretrained(
	    'microsoft/swin-tiny-patch4-window7-224'   , out_features=['stage1', 'stage2', 'stage3', 'stage4']   )
	snake_case_		:			Any											=	MaskFormerConfig(backbone_config=__a   )

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

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

	return config


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			str											=	[]
	# 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__						(    __a   , __a   , __a   ):
	snake_case_		:			Tuple											=	dct.pop(__a   )
	snake_case_		:			Dict											=	val


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


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


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Tuple											=	'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_		:			List[str]											=	Image.open(requests.get(__a   , stream=__a   ).raw   )
	return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a = False   ):
	snake_case_		:			List[str]											=	get_maskformer_config(__a   )

	# load original state_dict
	with open(__a   , 'rb'   ) as f:
		snake_case_		:			Any											=	pickle.load(__a   )
	snake_case_		:			List[str]											=	data['model']

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

	# rename keys
	snake_case_		:			Optional[int]											=	create_rename_keys(__a   )
	for src, dest in rename_keys:
		rename_key(__a   , __a   , __a   )
	read_in_swin_q_k_v(__a   , config.backbone_config   )
	read_in_decoder_q_k_v(__a   , __a   )

	# update to torch tensors
	for key, value in state_dict.items():
		snake_case_		:			Optional[int]											=	torch.from_numpy(__a   )

	# load 🤗 model
	snake_case_		:			Union[str, Any]											=	MaskFormerForInstanceSegmentation(__a   )
	model.eval()

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

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

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

	snake_case_		:			Optional[int]											=	image_processor(__a   , return_tensors='pt'   )

	snake_case_		:			Union[str, Any]											=	model(**__a   )

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

	if model_name == "maskformer-swin-tiny-ade":
		snake_case_		:			int											=	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]   , __a   , 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(__a   ).mkdir(exist_ok=__a   )
		model.save_pretrained(__a   )
		image_processor.save_pretrained(__a   )

	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__":
						_SCREAMING_SNAKE_CASE      	=		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."""
						)

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








def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ):
		raise ValueError('Input must be an integer'   )
	if input_num <= 0:
		raise ValueError('Input must be positive'   )
	return sum(
	    divisor for divisor in range(1   , input_num // 2 + 1   ) if input_num % divisor == 0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

