

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()
A__						=							logging.get_logger(__name__)




def 					_lowercase     (				a_				:					str				)				->	Any:




					'''simple docstring'''







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

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

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

					return config




def 					_lowercase     (				a_				:					str				)				->	Dict:




					'''simple docstring'''







					__magic_name__							=    []
					# 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 					_lowercase     (				a_				:					Tuple					,a_				:					List[Any]					,a_				:					Optional[Any]				)				->	str:




					'''simple docstring'''







					__magic_name__							=    dct.pop(a_				)
					__magic_name__							=    val




def 					_lowercase     (				a_				:					Optional[int]					,a_				:					Union[str, Any]				)				->	str:




					'''simple docstring'''







					__magic_name__							=    [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
					for i in range(len(backbone_config.depths				)				):
										__magic_name__							=    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)
															__magic_name__							=    state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'				)
															__magic_name__							=    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
															__magic_name__							=    in_proj_weight[:dim, :]
															__magic_name__							=    in_proj_bias[: dim]
															__magic_name__							=    in_proj_weight[
															    dim : dim * 2, :
															]
															__magic_name__							=    in_proj_bias[
															    dim : dim * 2
															]
															__magic_name__							=    in_proj_weight[
															    -dim :, :
															]
															__magic_name__							=    in_proj_bias[-dim :]
															# fmt: on




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					Any				)				->	int:




					'''simple docstring'''







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




def 					_lowercase     (				)				->	torch.Tensor:




					'''simple docstring'''







					__magic_name__							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
					__magic_name__							=    Image.open(requests.get(a_					,stream=a_				).raw				)
					return im




@torch.no_grad()
def 					_lowercase     (				a_				:					str					,a_				:					str					,a_				:					str					,a_				:					bool = False				)				->	Tuple:




					'''simple docstring'''







					__magic_name__							=    get_maskformer_config(a_				)

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

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

					# rename keys
					__magic_name__							=    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():
										__magic_name__							=    torch.from_numpy(a_				)

					# load 🤗 model
					__magic_name__							=    MaskFormerForInstanceSegmentation(a_				)
					model.eval()

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

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

					__magic_name__							=    image_processor(a_					,return_tensors='pt'				)

					__magic_name__							=    model(**a_				)

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

					if model_name == "maskformer-swin-tiny-ade":
										__magic_name__							=    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__":
						A__						=							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."
						)

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







from __future__ import annotations




def 					_lowercase     (				a_				:					int				)				->	bool:




					'''simple docstring'''







					__magic_name__							=    str(a_				)
					return len(a_				) == 9 and set(a_				) == set('123456789'				)




def 					_lowercase     (				)				->	int | None:




					'''simple docstring'''







					for base_num in range(9_9_9_9					,4_9_9_9					,-1				):
										__magic_name__							=    1_0_0_0_0_2 * base_num
										if is_9_pandigital(a_				):
															return candidate

					for base_num in range(3_3_3					,9_9					,-1				):
										__magic_name__							=    1_0_0_2_0_0_3 * base_num
										if is_9_pandigital(a_				):
															return candidate

					return None


if __name__ == "__main__":
						print(f'''{solution() = }''')




