

'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase         =						logging.get_logger()






@dataclass
class   _SCREAMING_SNAKE_CASE   :
	__SCREAMING_SNAKE_CASE					:nn.Module
	__SCREAMING_SNAKE_CASE					:List[nn.Module]      	=					field(default_factory=__a     )
	__SCREAMING_SNAKE_CASE					:list      	=					field(default_factory=__a     )



	def 			snake_case__  (   self		:   List[str]			,				a__		:   str			,				a__		:   Tensor			,				a__		:   Tensor      ):
						__magic_name__						       =    len(list(m.modules()      )      ) == 1 or isinstance(a__			,				nn.Convad      ) or isinstance(a__			,				nn.BatchNormad      )
						if has_not_submodules:
											self.traced.append(a__      )



	def __call__(   self		:   List[Any]			,				a__		:   Tensor      ):
						for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook      )      )
						self.module(a__      )
						[x.remove() for x in self.handles]
						return self



	@property
	def 			snake_case__  (   self		:   Dict      ):
						# check the len of the state_dict keys to see if we have learnable params
						return list(filter(lambda a__      : len(list(x.state_dict().keys()      )      ) > 0			,				self.traced      )      )






@dataclass
class   _SCREAMING_SNAKE_CASE   :
	__SCREAMING_SNAKE_CASE					:nn.Module
	__SCREAMING_SNAKE_CASE					:nn.Module
	__SCREAMING_SNAKE_CASE					:int      	=					0
	__SCREAMING_SNAKE_CASE					:List      	=					field(default_factory=__a     )
	__SCREAMING_SNAKE_CASE					:List      	=					field(default_factory=__a     )



	def __call__(   self		:   int			,				a__		:   Tensor      ):
						__magic_name__						       =    Tracker(self.dest      )(a__      ).parametrized
						__magic_name__						       =    Tracker(self.src      )(a__      ).parametrized

						__magic_name__						       =    list(filter(lambda a__      : type(a__      ) not in self.src_skip			,				a__      )      )
						__magic_name__						       =    list(filter(lambda a__      : type(a__      ) not in self.dest_skip			,				a__      )      )

						if len(a__      ) != len(a__      ):
											raise Exception(
											    F'''Numbers of operations are different. Source module has {len(a__      )} operations while'''
											    F''' destination module has {len(a__      )}.'''      )

						for dest_m, src_m in zip(a__			,				a__      ):
											dest_m.load_state_dict(src_m.state_dict()      )
											if self.verbose == 1:
																print(F'''Transfered from={src_m} to={dest_m}'''      )




def 						UpperCamelCase	(			a		,						a		,						a		,						a = True			)				->     Any:






					'''simple docstring'''
					print(F'''Converting {name}...'''			)
					with torch.no_grad():
										__magic_name__						       =    timm.create_model(a		,						pretrained=a			).eval()
										__magic_name__						       =    ResNetForImageClassification(a			).eval()
										__magic_name__						       =    ModuleTransfer(src=a		,						dest=a			)
										__magic_name__						       =    torch.randn((1, 3, 224, 224)			)
										module_transfer(a			)

					assert torch.allclose(from_model(a			)		,						our_model(a			).logits			), "The model logits don't match the original one."

					__magic_name__						       =    F'''resnet{"-".join(name.split("resnet"			)			)}'''
					print(a			)

					if push_to_hub:
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name		,						commit_message='''Add model'''		,						use_temp_dir=a		,						)

										# we can use the convnext one
										__magic_name__						       =    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''			)
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name		,						commit_message='''Add image processor'''		,						use_temp_dir=a		,						)

										print(F'''Pushed {checkpoint_name}'''			)




def 						UpperCamelCase	(			a		,						a = None		,						a = True			)				->     int:






					'''simple docstring'''
					__magic_name__						       =    '''imagenet-1k-id2label.json'''
					__magic_name__						       =    1000
					__magic_name__						       =    (1, num_labels)

					__magic_name__						       =    '''huggingface/label-files'''
					__magic_name__						       =    num_labels
					__magic_name__						       =    json.load(open(hf_hub_download(a		,						a		,						repo_type='''dataset'''			)		,						'''r'''			)			)
					__magic_name__						       =    {int(a			): v for k, v in idalabel.items()}

					__magic_name__						       =    idalabel
					__magic_name__						       =    {v: k for k, v in idalabel.items()}

					__magic_name__						       =    partial(a		,						num_labels=a		,						idalabel=a		,						labelaid=a			)

					__magic_name__						       =    {
					    '''resnet18''': ImageNetPreTrainedConfig(
					        depths=[2, 2, 2, 2]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type='''basic'''			),
					    '''resnet26''': ImageNetPreTrainedConfig(
					        depths=[2, 2, 2, 2]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type='''bottleneck'''			),
					    '''resnet34''': ImageNetPreTrainedConfig(
					        depths=[3, 4, 6, 3]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type='''basic'''			),
					    '''resnet50''': ImageNetPreTrainedConfig(
					        depths=[3, 4, 6, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type='''bottleneck'''			),
					    '''resnet101''': ImageNetPreTrainedConfig(
					        depths=[3, 4, 23, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type='''bottleneck'''			),
					    '''resnet152''': ImageNetPreTrainedConfig(
					        depths=[3, 8, 36, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type='''bottleneck'''			),
					}

					if model_name:
										convert_weight_and_push(a		,						names_to_config[model_name]		,						a		,						a			)
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(a		,						a		,						a		,						a			)
					return config, expected_shape


if __name__ == "__main__":
						_lowerCAmelCase         =						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default=None,
						    type=str,
						    help=(
						        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
						        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=Path,
						    required=True,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument(
						    "--push_to_hub",
						    default=True,
						    type=bool,
						    required=False,
						    help="If True, push model and image processor to the hub.",
						)

						_lowerCAmelCase         =						parser.parse_args()
						_lowerCAmelCase         =						args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''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()
_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    SwinConfig.from_pretrained(
         '''microsoft/swin-tiny-patch4-window7-224'''		,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
     __magic_name__						       =    MaskFormerConfig(backbone_config=a			)

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

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

     return config




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    []
     # 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	(			a		,						a		,						a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    dct.pop(a			)
     __magic_name__						       =    val




def 						UpperCamelCase	(			a		,						a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
     for i in range(len(backbone_config.depths			)			):
          __magic_name__						       =    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)
               __magic_name__						       =    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
               __magic_name__						       =    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
               __magic_name__						       =    in_proj_weight[:dim, :]
               __magic_name__						       =    in_proj_bias[: dim]
               __magic_name__						       =    in_proj_weight[
                   dim : dim * 2, :
               ]
               __magic_name__						       =    in_proj_bias[
                   dim : dim * 2
               ]
               __magic_name__						       =    in_proj_weight[
                   -dim :, :
               ]
               __magic_name__						       =    in_proj_bias[-dim :]
               # fmt: on




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     # fmt: off
     __magic_name__						       =    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)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    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)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    in_proj_bias[-hidden_size :]
     # fmt: on




def 						UpperCamelCase	(			)				->     torch.Tensor:






     '''simple docstring'''
     __magic_name__						       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __magic_name__						       =    Image.open(requests.get(a		,						stream=a			).raw			)
     return im




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a		,						a		,						a = False			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    get_maskformer_config(a			)

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

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

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

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

     # load 🤗 model
     __magic_name__						       =    MaskFormerForInstanceSegmentation(a			)
     model.eval()

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

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

     # verify results
     __magic_name__						       =    prepare_img()
     if "vistas" in model_name:
          __magic_name__						       =    65
     elif "cityscapes" in model_name:
          __magic_name__						       =    6_5535
     else:
          __magic_name__						       =    255
     __magic_name__						       =    True if '''ade''' in model_name else False
     __magic_name__						       =    MaskFormerImageProcessor(ignore_index=a		,						reduce_labels=a			)

     __magic_name__						       =    image_processor(a		,						return_tensors='''pt'''			)

     __magic_name__						       =    model(**a			)

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

     if model_name == "maskformer-swin-tiny-ade":
          __magic_name__						       =    torch.tensor(
              [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]			)
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,						a		,						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(a			).mkdir(exist_ok=a			)
          model.save_pretrained(a			)
          image_processor.save_pretrained(a			)

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






