

"""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()
snake_case_   :   Dict        =       logging.get_logger(__name__)



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				SwinConfig.from_pretrained(
					    "microsoft/swin-tiny-patch4-window7-224"							,      out_features=["stage1", "stage2", "stage3", "stage4"]	)
					UpperCAmelCase			:   Any      =				MaskFormerConfig(backbone_config=_lowercase	)

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

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

					return config



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				[]
					# 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_	(    _lowercase       :		Any							,      _lowercase       :		str							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				dct.pop(_lowercase	)
					UpperCAmelCase			:   Tuple      =				val



def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   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			:   List[str]      =				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			:   Tuple      =				state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
															UpperCAmelCase			:   Any      =				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			:   Optional[Any]      =				in_proj_weight[:dim, :]
															UpperCAmelCase			:   Dict      =				in_proj_bias[: dim]
															UpperCAmelCase			:   Any      =				in_proj_weight[
															    dim : dim * 2, :
															]
															UpperCAmelCase			:   Dict      =				in_proj_bias[
															    dim : dim * 2
															]
															UpperCAmelCase			:   List[str]      =				in_proj_weight[
															    -dim :, :
															]
															UpperCAmelCase			:   str      =				in_proj_bias[-dim :]
															# fmt: on



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				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			:   Any      =				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""	)
										UpperCAmelCase			:   int      =				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			:   str      =				in_proj_weight[: hidden_size, :]
										UpperCAmelCase			:   List[str]      =				in_proj_bias[:config.hidden_size]
										UpperCAmelCase			:   Any      =				in_proj_weight[hidden_size : hidden_size * 2, :]
										UpperCAmelCase			:   Optional[Any]      =				in_proj_bias[hidden_size : hidden_size * 2]
										UpperCAmelCase			:   str      =				in_proj_weight[-hidden_size :, :]
										UpperCAmelCase			:   List[str]      =				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			:   Union[str, Any]      =				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""	)
										UpperCAmelCase			:   List[str]      =				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			:   Dict      =				in_proj_weight[: hidden_size, :]
										UpperCAmelCase			:   Dict      =				in_proj_bias[:config.hidden_size]
										UpperCAmelCase			:   Optional[int]      =				in_proj_weight[hidden_size : hidden_size * 2, :]
										UpperCAmelCase			:   List[str]      =				in_proj_bias[hidden_size : hidden_size * 2]
										UpperCAmelCase			:   Dict      =				in_proj_weight[-hidden_size :, :]
										UpperCAmelCase			:   str      =				in_proj_bias[-hidden_size :]
					# fmt: on



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				"http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCAmelCase			:   Any      =				Image.open(requests.get(_lowercase							,      stream=_lowercase	).raw	)
					return im



@torch.no_grad()
def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		str							,      _lowercase       :		str							,      _lowercase       :		bool = False	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				get_maskformer_config(_lowercase	)

					# load original state_dict
					with open(_lowercase							,      "rb"	) as f:
										UpperCAmelCase			:   Dict      =				pickle.load(_lowercase	)
					UpperCAmelCase			:   Union[str, Any]      =				data["model"]

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

					# rename keys
					UpperCAmelCase			:   Optional[Any]      =				create_rename_keys(_lowercase	)
					for src, dest in rename_keys:
										rename_key(_lowercase							,      _lowercase							,      _lowercase	)
					read_in_swin_q_k_v(_lowercase							,      config.backbone_config	)
					read_in_decoder_q_k_v(_lowercase							,      _lowercase	)

					# update to torch tensors
					for key, value in state_dict.items():
										UpperCAmelCase			:   Optional[Any]      =				torch.from_numpy(_lowercase	)

					# load 🤗 model
					UpperCAmelCase			:   Optional[int]      =				MaskFormerForInstanceSegmentation(_lowercase	)
					model.eval()

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

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

					# verify results
					UpperCAmelCase			:   Tuple      =				prepare_img()
					if "vistas" in model_name:
										UpperCAmelCase			:   str      =				65
					elif "cityscapes" in model_name:
										UpperCAmelCase			:   List[Any]      =				6_55_35
					else:
										UpperCAmelCase			:   int      =				2_55
					UpperCAmelCase			:   List[str]      =				True if "ade" in model_name else False
					UpperCAmelCase			:   Tuple      =				MaskFormerImageProcessor(ignore_index=_lowercase							,      reduce_labels=_lowercase	)

					UpperCAmelCase			:   List[Any]      =				image_processor(_lowercase							,      return_tensors="pt"	)

					UpperCAmelCase			:   Optional[Any]      =				model(**_lowercase	)

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

					if model_name == "maskformer-swin-tiny-ade":
										UpperCAmelCase			:   str      =				torch.tensor(
										    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]	)
					assert torch.allclose(outputs.class_queries_logits[0, :3, :3]							,      _lowercase							,      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(_lowercase	).mkdir(exist_ok=_lowercase	)
										model.save_pretrained(_lowercase	)
										image_processor.save_pretrained(_lowercase	)

					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__":
	snake_case_   :   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."""
	)

	snake_case_   :   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"""






import math



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					return math.sqrt(_lowercase	) * math.sqrt(_lowercase	) == num



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				0
					UpperCAmelCase			:   Tuple      =				n
					while left <= right:
										UpperCAmelCase			:   Tuple      =				(left + right) // 2
										if mid**2 == n:
															return True
										elif mid**2 > n:
															UpperCAmelCase			:   List[str]      =				mid - 1
										else:
															UpperCAmelCase			:   Any      =				mid + 1
					return False


if __name__ == "__main__":
	import doctest

	doctest.testmod()


