





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()
_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      str         =SwinConfig.from_pretrained(
							    '''microsoft/swin-tiny-patch4-window7-224'''    ,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']   )
							__lowerCamelCase      :      Optional[Any]         =MaskFormerConfig(backbone_config=__snake_case   )

							__lowerCamelCase      :      List[str]         ='''huggingface/label-files'''
							if "ade20k-full" in model_name:
														# this should be ok
														__lowerCamelCase      :      Union[str, Any]         =847
														__lowerCamelCase      :      Union[str, Any]         ='''maskformer-ade20k-full-id2label.json'''
							elif "ade" in model_name:
														# this should be ok
														__lowerCamelCase      :      Dict         =150
														__lowerCamelCase      :      Any         ='''ade20k-id2label.json'''
							elif "coco-stuff" in model_name:
														# this should be ok
														__lowerCamelCase      :      Dict         =171
														__lowerCamelCase      :      Optional[Any]         ='''maskformer-coco-stuff-id2label.json'''
							elif "coco" in model_name:
														# TODO
														__lowerCamelCase      :      Optional[Any]         =133
														__lowerCamelCase      :      Dict         ='''coco-panoptic-id2label.json'''
							elif "cityscapes" in model_name:
														# this should be ok
														__lowerCamelCase      :      List[Any]         =19
														__lowerCamelCase      :      Union[str, Any]         ='''cityscapes-id2label.json'''
							elif "vistas" in model_name:
														# this should be ok
														__lowerCamelCase      :      Optional[int]         =65
														__lowerCamelCase      :      List[str]         ='''mapillary-vistas-id2label.json'''

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

							return config



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =dct.pop(__snake_case   )
							__lowerCamelCase      :      List[Any]         =val



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase      :      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   )   ):
														__lowerCamelCase      :      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)
																					__lowerCamelCase      :      Any         =state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'   )
																					__lowerCamelCase      :      str         =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
																					__lowerCamelCase      :      int         =in_proj_weight[:dim, :]
																					__lowerCamelCase      :      int         =in_proj_bias[: dim]
																					__lowerCamelCase      :      Dict         =in_proj_weight[
																					    dim : dim * 2, :
																					]
																					__lowerCamelCase      :      Union[str, Any]         =in_proj_bias[
																					    dim : dim * 2
																					]
																					__lowerCamelCase      :      str         =in_proj_weight[
																					    -dim :, :
																					]
																					__lowerCamelCase      :      int         =in_proj_bias[-dim :]
																					# fmt: on



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







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



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Any = False   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =get_maskformer_config(__snake_case   )

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

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

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

							# update to torch tensors
							for key, value in state_dict.items():
														__lowerCamelCase      :      List[str]         =torch.from_numpy(__snake_case   )

							# load 🤗 model
							__lowerCamelCase      :      Optional[int]         =MaskFormerForInstanceSegmentation(__snake_case   )
							model.eval()

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

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

							# verify results
							__lowerCamelCase      :      str         =prepare_img()
							if "vistas" in model_name:
														__lowerCamelCase      :      Optional[Any]         =65
							elif "cityscapes" in model_name:
														__lowerCamelCase      :      Dict         =65535
							else:
														__lowerCamelCase      :      str         =255
							__lowerCamelCase      :      List[Any]         =True if '''ade''' in model_name else False
							__lowerCamelCase      :      Union[str, Any]         =MaskFormerImageProcessor(ignore_index=__snake_case    ,						reduce_labels=__snake_case   )

							__lowerCamelCase      :      Optional[Any]         =image_processor(__snake_case    ,						return_tensors='''pt'''   )

							__lowerCamelCase      :      str         =model(**__snake_case   )

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

							if model_name == "maskformer-swin-tiny-ade":
														__lowerCamelCase      :      str         =torch.tensor(
														    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]   )
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,						__snake_case    ,						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(__snake_case   ).mkdir(exist_ok=__snake_case   )
														model.save_pretrained(__snake_case   )
														image_processor.save_pretrained(__snake_case   )

							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__":
							_UpperCamelCase							=      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.'
							)

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











"""simple docstring"""





def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =[[0] * n for i in range(SCREAMING_SNAKE_CASE   )]
							for i in range(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      Union[str, Any]         =y_points[i]

							for i in range(2    ,						SCREAMING_SNAKE_CASE   ):
														for j in range(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =(
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




