



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 A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			A__	  =     SwinConfig.from_pretrained(
			    'microsoft/swin-tiny-patch4-window7-224'			,						out_features=['stage1', 'stage2', 'stage3', 'stage4']       )
			A__	  =     MaskFormerConfig(backbone_config=__UpperCamelCase       )

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

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

			return config


def A    (							__UpperCamelCase       )    ->   Dict:
			A__	  =     []
			# 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    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


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


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			# fmt: off
			A__	  =     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)
						A__	  =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''       )
						A__	  =     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
						A__	  =     in_proj_weight[: hidden_size, :]
						A__	  =     in_proj_bias[:config.hidden_size]
						A__	  =     in_proj_weight[hidden_size : hidden_size * 2, :]
						A__	  =     in_proj_bias[hidden_size : hidden_size * 2]
						A__	  =     in_proj_weight[-hidden_size :, :]
						A__	  =     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)
						A__	  =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''       )
						A__	  =     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
						A__	  =     in_proj_weight[: hidden_size, :]
						A__	  =     in_proj_bias[:config.hidden_size]
						A__	  =     in_proj_weight[hidden_size : hidden_size * 2, :]
						A__	  =     in_proj_bias[hidden_size : hidden_size * 2]
						A__	  =     in_proj_weight[-hidden_size :, :]
						A__	  =     in_proj_bias[-hidden_size :]
			# fmt: on


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


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase = False       )    ->   List[Any]:
			A__	  =     get_maskformer_config(__UpperCamelCase       )

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

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

			# rename keys
			A__	  =     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():
						A__	  =     torch.from_numpy(__UpperCamelCase       )

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

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

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

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

			A__	  =     model(**__UpperCamelCase       )

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

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









from __future__ import annotations


def A    (							__UpperCamelCase = 4       )    ->   list[list[int]]:
			A__	  =     abs(__UpperCamelCase       ) or 4
			return [[1 + x + y * row_size for x in range(__UpperCamelCase       )] for y in range(__UpperCamelCase       )]


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_column(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(reverse_column(__UpperCamelCase       )       )
			# OR.. reverse_column(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_column(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [list(__UpperCamelCase       ) for x in zip(*__UpperCamelCase       )]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     matrix[::-1]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [x[::-1] for x in matrix]
			return matrix


def A    (							__UpperCamelCase       )    ->   None:
			for i in matrix:
						print(*__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 90 counterclockwise:\n''')
							print_matrix(rotate_aa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 180:\n''')
							print_matrix(rotate_aaa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 270 counterclockwise:\n''')
							print_matrix(rotate_aaa(matrix))




