
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_						: List[Any]         =  logging.get_logger(__name__)







def        A__					(						lowerCamelCase						)  ->	Any:
				UpperCamelCase_:							List[str]	=						SwinConfig.from_pretrained(
				    """microsoft/swin-tiny-patch4-window7-224"""     ,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						)
				UpperCamelCase_:							Optional[Any]	=						MaskFormerConfig(backbone_config=lowerCamelCase						)

				UpperCamelCase_:							str	=						"""huggingface/label-files"""
				if "ade20k-full" in model_name:
								# this should be ok
								UpperCamelCase_:							Dict	=						8_47
								UpperCamelCase_:							Optional[int]	=						"""maskformer-ade20k-full-id2label.json"""
				elif "ade" in model_name:
								# this should be ok
								UpperCamelCase_:							Optional[int]	=						1_50
								UpperCamelCase_:							int	=						"""ade20k-id2label.json"""
				elif "coco-stuff" in model_name:
								# this should be ok
								UpperCamelCase_:							Tuple	=						1_71
								UpperCamelCase_:							Optional[int]	=						"""maskformer-coco-stuff-id2label.json"""
				elif "coco" in model_name:
								# TODO
								UpperCamelCase_:							Any	=						1_33
								UpperCamelCase_:							Tuple	=						"""coco-panoptic-id2label.json"""
				elif "cityscapes" in model_name:
								# this should be ok
								UpperCamelCase_:							List[Any]	=						19
								UpperCamelCase_:							int	=						"""cityscapes-id2label.json"""
				elif "vistas" in model_name:
								# this should be ok
								UpperCamelCase_:							Any	=						65
								UpperCamelCase_:							List[str]	=						"""mapillary-vistas-id2label.json"""

				UpperCamelCase_:							Union[str, Any]	=						json.load(open(hf_hub_download(lowerCamelCase     ,    lowerCamelCase     ,    repo_type="""dataset"""						)     ,    """r"""						)						)
				UpperCamelCase_:							List[Any]	=						{int(lowerCamelCase						): v for k, v in idalabel.items()}

				return config







def        A__					(						lowerCamelCase						)  ->	Optional[Any]:
				UpperCamelCase_:							Tuple	=						[]
				# 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						)  ->	int:
				UpperCamelCase_:							Union[str, Any]	=						dct.pop(lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						val







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Any:
				UpperCamelCase_:							List[Any]	=						[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
				for i in range(len(backbone_config.depths						)						):
								UpperCamelCase_:							Optional[Any]	=						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_:							Optional[Any]	=						state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
												UpperCamelCase_:							Optional[int]	=						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_:							int	=						in_proj_weight[:dim, :]
												UpperCamelCase_:							Any	=						in_proj_bias[: dim]
												UpperCamelCase_:							Any	=						in_proj_weight[
												    dim : dim * 2, :
												]
												UpperCamelCase_:							int	=						in_proj_bias[
												    dim : dim * 2
												]
												UpperCamelCase_:							Dict	=						in_proj_weight[
												    -dim :, :
												]
												UpperCamelCase_:							Tuple	=						in_proj_bias[-dim :]
												# fmt: on







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Any:
				# fmt: off
				UpperCamelCase_:							Optional[int]	=						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_:							int	=						state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''						)
								UpperCamelCase_:							str	=						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_:							Optional[int]	=						in_proj_weight[: hidden_size, :]
								UpperCamelCase_:							Tuple	=						in_proj_bias[:config.hidden_size]
								UpperCamelCase_:							Optional[Any]	=						in_proj_weight[hidden_size : hidden_size * 2, :]
								UpperCamelCase_:							str	=						in_proj_bias[hidden_size : hidden_size * 2]
								UpperCamelCase_:							Dict	=						in_proj_weight[-hidden_size :, :]
								UpperCamelCase_:							Dict	=						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_:							str	=						state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''						)
								UpperCamelCase_:							int	=						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_:							str	=						in_proj_weight[: hidden_size, :]
								UpperCamelCase_:							Optional[Any]	=						in_proj_bias[:config.hidden_size]
								UpperCamelCase_:							Union[str, Any]	=						in_proj_weight[hidden_size : hidden_size * 2, :]
								UpperCamelCase_:							List[str]	=						in_proj_bias[hidden_size : hidden_size * 2]
								UpperCamelCase_:							Tuple	=						in_proj_weight[-hidden_size :, :]
								UpperCamelCase_:							Tuple	=						in_proj_bias[-hidden_size :]
				# fmt: on







def        A__					(						)  ->	torch.Tensor:
				UpperCamelCase_:							Optional[Any]	=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:							Dict	=						Image.open(requests.get(lowerCamelCase     ,    stream=lowerCamelCase						).raw						)
				return im







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	Tuple:
				UpperCamelCase_:							List[Any]	=						get_maskformer_config(lowerCamelCase						)

				# load original state_dict
				with open(lowerCamelCase     ,    """rb"""						) as f:
								UpperCamelCase_:							Dict	=						pickle.load(lowerCamelCase						)
				UpperCamelCase_:							List[Any]	=						data["""model"""]

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

				# rename keys
				UpperCamelCase_:							int	=						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_:							Dict	=						torch.from_numpy(lowerCamelCase						)

				# load 🤗 model
				UpperCamelCase_:							List[str]	=						MaskFormerForInstanceSegmentation(lowerCamelCase						)
				model.eval()

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

				UpperCamelCase_,   UpperCamelCase_:							Optional[Any]	=						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_:							Union[str, Any]	=						prepare_img()
				if "vistas" in model_name:
								UpperCamelCase_:							List[Any]	=						65
				elif "cityscapes" in model_name:
								UpperCamelCase_:							Optional[Any]	=						6_55_35
				else:
								UpperCamelCase_:							Optional[int]	=						2_55
				UpperCamelCase_:							Optional[Any]	=						True if """ade""" in model_name else False
				UpperCamelCase_:							List[str]	=						MaskFormerImageProcessor(ignore_index=lowerCamelCase     ,    reduce_labels=lowerCamelCase						)

				UpperCamelCase_:							Dict	=						image_processor(lowerCamelCase     ,    return_tensors="""pt"""						)

				UpperCamelCase_:							Any	=						model(**lowerCamelCase						)

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

				if model_name == "maskformer-swin-tiny-ade":
								UpperCamelCase_:							Optional[int]	=						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[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."""
							)

							lowerCamelCase_						: List[Any]         =  parser.parse_args()
							convert_maskformer_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)








def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				while second != 0:
								UpperCamelCase_:							Optional[Any]	=						first & second
								first ^= second
								UpperCamelCase_:							Any	=						c << 1
				return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCamelCase_						: List[Any]         =  int(input("""Enter the first number: """).strip())
							lowerCamelCase_						: Tuple         =  int(input("""Enter the second number: """).strip())
							print(F"""{add(first, second) = }""")






