





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

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

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

	return config


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     int     =		[]
	# 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_						:     str     =		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_						:     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)
			snake_case_						:     str     =		state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""				)
			snake_case_						:     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
			snake_case_						:     Union[str, Any]     =		in_proj_weight[:dim, :]
			snake_case_						:     Optional[Any]     =		in_proj_bias[: dim]
			snake_case_						:     Union[str, Any]     =		in_proj_weight[
			    dim : dim * 2, :
			]
			snake_case_						:     Any     =		in_proj_bias[
			    dim : dim * 2
			]
			snake_case_						:     Union[str, Any]     =		in_proj_weight[
			    -dim :, :
			]
			snake_case_						:     Union[str, Any]     =		in_proj_bias[-dim :]
			# fmt: on


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


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


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

	# load original state_dict
	with open(__a				,	'rb'				) as f:
		snake_case_						:     Optional[int]     =		pickle.load(__a				)
	snake_case_						:     Optional[int]     =		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_						:     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_						:     List[str]     =		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_						:     Tuple     =		prepare_img()
	if "vistas" in model_name:
		snake_case_						:     Dict     =		65
	elif "cityscapes" in model_name:
		snake_case_						:     Dict     =		6_55_35
	else:
		snake_case_						:     Tuple     =		2_55
	snake_case_						:     str     =		True if '''ade''' in model_name else False
	snake_case_						:     Dict     =		MaskFormerImageProcessor(ignore_index=__a				,	reduce_labels=__a				)

	snake_case_						:     Any     =		image_processor(__a				,	return_tensors='pt'				)

	snake_case_						:     List[Any]     =		model(**__a				)

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

	if model_name == "maskformer-swin-tiny-ade":
		snake_case_						:     List[str]     =		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
			)








_SCREAMING_SNAKE_CASE 		=						{
    """Pillow""": """Pillow""",
    """accelerate""": """accelerate>=0.11.0""",
    """compel""": """compel==0.1.8""",
    """black""": """black~=23.1""",
    """datasets""": """datasets""",
    """filelock""": """filelock""",
    """flax""": """flax>=0.4.1""",
    """hf-doc-builder""": """hf-doc-builder>=0.3.0""",
    """huggingface-hub""": """huggingface-hub>=0.13.2""",
    """requests-mock""": """requests-mock==1.10.0""",
    """importlib_metadata""": """importlib_metadata""",
    """invisible-watermark""": """invisible-watermark""",
    """isort""": """isort>=5.5.4""",
    """jax""": """jax>=0.2.8,!=0.3.2""",
    """jaxlib""": """jaxlib>=0.1.65""",
    """Jinja2""": """Jinja2""",
    """k-diffusion""": """k-diffusion>=0.0.12""",
    """torchsde""": """torchsde""",
    """note_seq""": """note_seq""",
    """librosa""": """librosa""",
    """numpy""": """numpy""",
    """omegaconf""": """omegaconf""",
    """parameterized""": """parameterized""",
    """protobuf""": """protobuf>=3.20.3,<4""",
    """pytest""": """pytest""",
    """pytest-timeout""": """pytest-timeout""",
    """pytest-xdist""": """pytest-xdist""",
    """ruff""": """ruff>=0.0.241""",
    """safetensors""": """safetensors""",
    """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
    """scipy""": """scipy""",
    """onnx""": """onnx""",
    """regex""": """regex!=2019.12.17""",
    """requests""": """requests""",
    """tensorboard""": """tensorboard""",
    """torch""": """torch>=1.4""",
    """torchvision""": """torchvision""",
    """transformers""": """transformers>=4.25.1""",
    """urllib3""": """urllib3<=2.0.0""",
}

