

'''simple docstring'''




from __future__ import annotations

import math
import random
from typing import Any






class   _SCREAMING_SNAKE_CASE   :



	def __init__(   self		:   Union[str, Any]      ):
						__magic_name__						       =    []
						__magic_name__						       =    0
						__magic_name__						       =    0



	def 			snake_case__  (   self		:   int      ):
						return self.head == self.tail



	def 			snake_case__  (   self		:   int			,				a__		:   Any      ):
						self.data.append(a__      )
						__magic_name__						       =    self.tail + 1



	def 			snake_case__  (   self		:   Tuple      ):
						__magic_name__						       =    self.data[self.head]
						__magic_name__						       =    self.head + 1
						return ret



	def 			snake_case__  (   self		:   Optional[Any]      ):
						return self.tail - self.head



	def 			snake_case__  (   self		:   List[Any]      ):
						print(self.data      )
						print('''**************'''      )
						print(self.data[self.head : self.tail]      )






class   _SCREAMING_SNAKE_CASE   :



	def __init__(   self		:   List[str]			,				a__		:   Any      ):
						__magic_name__						       =    data
						__magic_name__						       =    None
						__magic_name__						       =    None
						__magic_name__						       =    1



	def 			snake_case__  (   self		:   Optional[int]      ):
						return self.data



	def 			snake_case__  (   self		:   List[Any]      ):
						return self.left



	def 			snake_case__  (   self		:   Tuple      ):
						return self.right



	def 			snake_case__  (   self		:   Any      ):
						return self.height



	def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Any      ):
						__magic_name__						       =    data



	def 			snake_case__  (   self		:   int			,				a__		:   MyNode | None      ):
						__magic_name__						       =    node



	def 			snake_case__  (   self		:   Tuple			,				a__		:   MyNode | None      ):
						__magic_name__						       =    node



	def 			snake_case__  (   self		:   List[str]			,				a__		:   int      ):
						__magic_name__						       =    height




def 						UpperCamelCase	(			a			)				->     int:






					'''simple docstring'''
					if node is None:
										return 0
					return node.get_height()




def 						UpperCamelCase	(			a		,						a			)				->     int:






					'''simple docstring'''
					if a > b:
										return a
					return b




def 						UpperCamelCase	(			a			)				->     MyNode:






					'''simple docstring'''
					print('''left rotation node:'''		,						node.get_data()			)
					__magic_name__						       =    node.get_left()
					assert ret is not None
					node.set_left(ret.get_right()			)
					ret.set_right(a			)
					__magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
					node.set_height(a			)
					__magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
					ret.set_height(a			)
					return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






					'''simple docstring'''
					print('''right rotation node:'''		,						node.get_data()			)
					__magic_name__						       =    node.get_right()
					assert ret is not None
					node.set_right(ret.get_left()			)
					ret.set_left(a			)
					__magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
					node.set_height(a			)
					__magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
					ret.set_height(a			)
					return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






					'''simple docstring'''
					__magic_name__						       =    node.get_left()
					assert left_child is not None
					node.set_left(left_rotation(a			)			)
					return right_rotation(a			)




def 						UpperCamelCase	(			a			)				->     MyNode:






					'''simple docstring'''
					__magic_name__						       =    node.get_right()
					assert right_child is not None
					node.set_right(right_rotation(a			)			)
					return left_rotation(a			)




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






					'''simple docstring'''
					if node is None:
										return MyNode(a			)
					if data < node.get_data():
										node.set_left(insert_node(node.get_left()		,						a			)			)
										if (
										    get_height(node.get_left()			) - get_height(node.get_right()			) == 2
										):  # an unbalance detected
															__magic_name__						       =    node.get_left()
															assert left_child is not None
															if (
															    data < left_child.get_data()
															):  # new node is the left child of the left child
																				__magic_name__						       =    right_rotation(a			)
															else:
																				__magic_name__						       =    lr_rotation(a			)
					else:
										node.set_right(insert_node(node.get_right()		,						a			)			)
										if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
															__magic_name__						       =    node.get_right()
															assert right_child is not None
															if data < right_child.get_data():
																				__magic_name__						       =    rl_rotation(a			)
															else:
																				__magic_name__						       =    left_rotation(a			)
					__magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
					node.set_height(a			)
					return node




def 						UpperCamelCase	(			a			)				->     Any:






					'''simple docstring'''
					while True:
										__magic_name__						       =    root.get_right()
										if right_child is None:
															break
										__magic_name__						       =    right_child
					return root.get_data()




def 						UpperCamelCase	(			a			)				->     Any:






					'''simple docstring'''
					while True:
										__magic_name__						       =    root.get_left()
										if left_child is None:
															break
										__magic_name__						       =    left_child
					return root.get_data()




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






					'''simple docstring'''
					__magic_name__						       =    root.get_left()
					__magic_name__						       =    root.get_right()
					if root.get_data() == data:
										if left_child is not None and right_child is not None:
															__magic_name__						       =    get_left_most(a			)
															root.set_data(a			)
															root.set_right(del_node(a		,						a			)			)
										elif left_child is not None:
															__magic_name__						       =    left_child
										elif right_child is not None:
															__magic_name__						       =    right_child
										else:
															return None
					elif root.get_data() > data:
										if left_child is None:
															print('''No such data'''			)
															return root
										else:
															root.set_left(del_node(a		,						a			)			)
					else:  # root.get_data() < data
										if right_child is None:
															return root
										else:
															root.set_right(del_node(a		,						a			)			)

					if get_height(a			) - get_height(a			) == 2:
										assert right_child is not None
										if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
															__magic_name__						       =    left_rotation(a			)
										else:
															__magic_name__						       =    rl_rotation(a			)
					elif get_height(a			) - get_height(a			) == -2:
										assert left_child is not None
										if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
															__magic_name__						       =    right_rotation(a			)
										else:
															__magic_name__						       =    lr_rotation(a			)
					__magic_name__						       =    my_max(get_height(root.get_right()			)		,						get_height(root.get_left()			)			) + 1
					root.set_height(a			)
					return root






class   _SCREAMING_SNAKE_CASE   :



	def __init__(   self		:   List[Any]      ):
						__magic_name__						       =    None



	def 			snake_case__  (   self		:   List[Any]      ):
						return get_height(self.root      )



	def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Any      ):
						print('''insert:''' + str(a__      )      )
						__magic_name__						       =    insert_node(self.root			,				a__      )



	def 			snake_case__  (   self		:   Dict			,				a__		:   Any      ):
						print('''delete:''' + str(a__      )      )
						if self.root is None:
											print('''Tree is empty!'''      )
											return
						__magic_name__						       =    del_node(self.root			,				a__      )



	def __str__(   self		:   Optional[Any]			,				):  # a level traversale, gives a more intuitive look on the tree
						__magic_name__						       =    ''''''
						__magic_name__						       =    MyQueue()
						q.push(self.root      )
						__magic_name__						       =    self.get_height()
						if layer == 0:
											return output
						__magic_name__						       =    0
						while not q.is_empty():
											__magic_name__						       =    q.pop()
											__magic_name__						       =    ''' ''' * int(math.pow(2			,				layer - 1      )      )
											output += space
											if node is None:
																output += "*"
																q.push(a__      )
																q.push(a__      )
											else:
																output += str(node.get_data()      )
																q.push(node.get_left()      )
																q.push(node.get_right()      )
											output += space
											__magic_name__						       =    cnt + 1
											for i in range(100      ):
																if cnt == math.pow(2			,				a__      ) - 1:
																					__magic_name__						       =    layer - 1
																					if layer == 0:
																										output += "\n*************************************"
																										return output
																					output += "\n"
																					break
						output += "\n*************************************"
						return output




def 						UpperCamelCase	(			)				->     None:






					'''simple docstring'''
					import doctest

					doctest.testmod()


if __name__ == "__main__":
						_test()
						_lowerCAmelCase         =						AVLtree()
						_lowerCAmelCase         =						list(range(10))
						random.shuffle(lst)
						for i in lst:
												t.insert(i)
												print(str(t))
						random.shuffle(lst)
						for i in lst:
												t.del_node(i)
												print(str(t))









'''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
      )






