
'''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()
lowercase    :							str      =					logging.get_logger(__name__)







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

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

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

	return config







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Union[str, Any]:
	_snake_case   						=		[]
	# 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	)       ->	Dict:
	_snake_case   						=		dct.pop(__A	)
	_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	int:
	_snake_case   						=		[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   						=		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   						=		state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'	)
			_snake_case   						=		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   						=		in_proj_weight[:dim, :]
			_snake_case   						=		in_proj_bias[: dim]
			_snake_case   						=		in_proj_weight[
			    dim : dim * 2, :
			]
			_snake_case   						=		in_proj_bias[
			    dim : dim * 2
			]
			_snake_case   						=		in_proj_weight[
			    -dim :, :
			]
			_snake_case   						=		in_proj_bias[-dim :]
			# fmt: on







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	# fmt: off
	_snake_case   						=		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   						=		state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'	)
		_snake_case   						=		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   						=		in_proj_weight[: hidden_size, :]
		_snake_case   						=		in_proj_bias[:config.hidden_size]
		_snake_case   						=		in_proj_weight[hidden_size : hidden_size * 2, :]
		_snake_case   						=		in_proj_bias[hidden_size : hidden_size * 2]
		_snake_case   						=		in_proj_weight[-hidden_size :, :]
		_snake_case   						=		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   						=		state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'	)
		_snake_case   						=		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   						=		in_proj_weight[: hidden_size, :]
		_snake_case   						=		in_proj_bias[:config.hidden_size]
		_snake_case   						=		in_proj_weight[hidden_size : hidden_size * 2, :]
		_snake_case   						=		in_proj_bias[hidden_size : hidden_size * 2]
		_snake_case   						=		in_proj_weight[-hidden_size :, :]
		_snake_case   						=		in_proj_bias[-hidden_size :]
	# fmt: on







def 				SCREAMING_SNAKE_CASE__	(   )       ->	torch.Tensor:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)
	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A = False	)       ->	Any:
	_snake_case   						=		get_maskformer_config(__A	)

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

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

	# rename keys
	_snake_case   						=		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   						=		torch.from_numpy(__A	)

	# load 🤗 model
	_snake_case   						=		MaskFormerForInstanceSegmentation(__A	)
	model.eval()

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

	_snake_case						, _snake_case   						=		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   						=		prepare_img()
	if "vistas" in model_name:
		_snake_case   						=		65
	elif "cityscapes" in model_name:
		_snake_case   						=		65_535
	else:
		_snake_case   						=		255
	_snake_case   						=		True if 'ade' in model_name else False
	_snake_case   						=		MaskFormerImageProcessor(ignore_index=__A			,				reduce_labels=__A	)

	_snake_case   						=		image_processor(__A			,				return_tensors='pt'	)

	_snake_case   						=		model(**__A	)

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

	if model_name == "maskformer-swin-tiny-ade":
		_snake_case   						=		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]			,				__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__":
					lowercase    :							Optional[int]      =					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."
					)

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








lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
