


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

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

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

					return config

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
					__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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
					__UpperCamelCase   							=dct.pop(UpperCAmelCase__    )
					__UpperCamelCase   							=val

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
					__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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Tuple    ):
					# 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 						_UpperCAmelCase    (      ):
					__UpperCamelCase   							='http://images.cocodataset.org/val2017/000000039769.jpg'
					__UpperCamelCase   							=Image.open(requests.get(UpperCAmelCase__      ,					stream=UpperCAmelCase__    ).raw    )
					return im

@torch.no_grad()
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : List[Any] = False    ):
					__UpperCamelCase   							=get_maskformer_config(UpperCAmelCase__    )

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

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

					# rename keys
					__UpperCamelCase   							=create_rename_keys(UpperCAmelCase__    )
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__    )
					read_in_swin_q_k_v(UpperCAmelCase__      ,					config.backbone_config    )
					read_in_decoder_q_k_v(UpperCAmelCase__      ,					UpperCAmelCase__    )

					# update to torch tensors
					for key, value in state_dict.items():
										__UpperCamelCase   							=torch.from_numpy(UpperCAmelCase__    )

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

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

					__UpperCamelCase							,				__UpperCamelCase   							=model.load_state_dict(UpperCAmelCase__      ,					strict=UpperCAmelCase__    )
					assert missing_keys == [
					    "model.pixel_level_module.encoder.model.layernorm.weight",
					    "model.pixel_level_module.encoder.model.layernorm.bias",
					]
					assert len(UpperCAmelCase__    ) == 0, F'Unexpected keys: {unexpected_keys}'

					# verify results
					__UpperCamelCase   							=prepare_img()
					if "vistas" in model_name:
										__UpperCamelCase   							=65
					elif "cityscapes" in model_name:
										__UpperCamelCase   							=6_55_35
					else:
										__UpperCamelCase   							=2_55
					__UpperCamelCase   							=True if 'ade' in model_name else False
					__UpperCamelCase   							=MaskFormerImageProcessor(ignore_index=UpperCAmelCase__      ,					reduce_labels=UpperCAmelCase__    )

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

					__UpperCamelCase   							=model(**UpperCAmelCase__    )

					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]      ,					UpperCAmelCase__      ,					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(UpperCAmelCase__    ).mkdir(exist_ok=UpperCAmelCase__    )
										model.save_pretrained(UpperCAmelCase__    )
										image_processor.save_pretrained(UpperCAmelCase__    )

					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

from typing import Any

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]    ):
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					[]      ,					0    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
   _A			=						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['A', 'B', 'C'])
   generate_all_subsequences(seq)





