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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							SwinConfig.from_pretrained(
				    'microsoft/swin-tiny-patch4-window7-224'					,			out_features=['stage1', 'stage2', 'stage3', 'stage4']       )
				UpperCAmelCase__             =							MaskFormerConfig(backbone_config=lowerCamelCase       )

				UpperCAmelCase__             =							'huggingface/label-files'
				if "ade20k-full" in model_name:
								# this should be ok
								UpperCAmelCase__             =							8_4_7
								UpperCAmelCase__             =							'maskformer-ade20k-full-id2label.json'
				elif "ade" in model_name:
								# this should be ok
								UpperCAmelCase__             =							1_5_0
								UpperCAmelCase__             =							'ade20k-id2label.json'
				elif "coco-stuff" in model_name:
								# this should be ok
								UpperCAmelCase__             =							1_7_1
								UpperCAmelCase__             =							'maskformer-coco-stuff-id2label.json'
				elif "coco" in model_name:
								# TODO
								UpperCAmelCase__             =							1_3_3
								UpperCAmelCase__             =							'coco-panoptic-id2label.json'
				elif "cityscapes" in model_name:
								# this should be ok
								UpperCAmelCase__             =							1_9
								UpperCAmelCase__             =							'cityscapes-id2label.json'
				elif "vistas" in model_name:
								# this should be ok
								UpperCAmelCase__             =							6_5
								UpperCAmelCase__             =							'mapillary-vistas-id2label.json'

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

				return config



def   a_						(					lowerCamelCase       ):
				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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							dct.pop(lowerCamelCase       )
				UpperCAmelCase__             =							val



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				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   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				# fmt: off
				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   a_						(					):
				UpperCAmelCase__             =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__             =							Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase       ).raw       )
				return im



@torch.no_grad()
def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase = False       ):
				UpperCAmelCase__             =							get_maskformer_config(lowerCamelCase       )

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

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

				# rename keys
				UpperCAmelCase__             =							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():
								UpperCAmelCase__             =							torch.from_numpy(lowerCamelCase       )

				# load 🤗 model
				UpperCAmelCase__             =							MaskFormerForInstanceSegmentation(lowerCamelCase       )
				model.eval()

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

				UpperCAmelCase__      ,	UpperCAmelCase__             =							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
				UpperCAmelCase__             =							prepare_img()
				if "vistas" in model_name:
								UpperCAmelCase__             =							6_5
				elif "cityscapes" in model_name:
								UpperCAmelCase__             =							6_5_5_3_5
				else:
								UpperCAmelCase__             =							2_5_5
				UpperCAmelCase__             =							True if 'ade' in model_name else False
				UpperCAmelCase__             =							MaskFormerImageProcessor(ignore_index=lowerCamelCase					,			reduce_labels=lowerCamelCase       )

				UpperCAmelCase__             =							image_processor(lowerCamelCase					,			return_tensors='pt'       )

				UpperCAmelCase__             =							model(**lowerCamelCase       )

				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]					,			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__  :  Optional[Any]						     =	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__  :  Optional[int]						     =	parser.parse_args()
						convert_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)




"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if a < 0 or b < 0:
								raise ValueError('the value of both inputs must be positive'       )

				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"
				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"

				UpperCAmelCase__             =							max(len(lowerCamelCase       )					,			len(lowerCamelCase       )       )

				return "0b" + "".join(
				    str(int(char_a == '1' and char_b == '1'       )       )
				    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase       )					,			b_binary.zfill(lowerCamelCase       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



