





"""simple docstring"""





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

_UpperCamelCase					      =   TypeVar('KT')
_UpperCamelCase					      =   TypeVar('VT')
class 		SCREAMING_SNAKE_CASE_				(     Generic[KT, VT] ):
				"""simple docstring"""




				def __init__(  self	:Optional[Any]    ,     __lowercase	:Any = "root"    ,     __lowercase	:int = None      ):
											__lowerCamelCase   :   str       =key
											__lowerCamelCase   :   Tuple       =value
											__lowerCamelCase   :   Tuple       =[]




				def __repr__(  self	:List[str]      ):
											return f'Node({self.key}: {self.value})'




				@property
				def        __lowercase	(  self	:Optional[Any]      ):
											return len(self.forward      )

class 		SCREAMING_SNAKE_CASE_				(     Generic[KT, VT] ):
				"""simple docstring"""




				def __init__(  self	:str    ,     __lowercase	:Optional[int] = 0.5    ,     __lowercase	:Tuple = 16      ):
											__lowerCamelCase   :   str       =Node[KT, VT]()
											__lowerCamelCase   :   Optional[Any]       =0
											__lowerCamelCase   :   List[str]       =p
											__lowerCamelCase   :   Dict       =max_level




				def __str__(  self	:Optional[int]      ):
											__lowerCamelCase   :   Dict       =list(self      )

											if len(A_      ) == 0:
																		return f'SkipList(level={self.level})'

											__lowerCamelCase   :   List[str]       =max((len(str(A_      )      ) for item in items)    ,     default=4      )
											__lowerCamelCase   :   Optional[Any]       =max(A_    ,     4      ) + 4

											__lowerCamelCase   :   Tuple       =self.head
											__lowerCamelCase   :   int       =[]

											__lowerCamelCase   :   Union[str, Any]       =node.forward.copy()
											lines.append(f'[{node.key}]'.ljust(A_    ,     '''-'''      ) + '''* ''' * len(A_      )      )
											lines.append(''' ''' * label_size + '''| ''' * len(A_      )      )

											while len(node.forward      ) != 0:
																		__lowerCamelCase   :   int       =node.forward[0]

																		lines.append(
																		    f'[{node.key}]'.ljust(A_    ,     '''-'''      )
																		    + ''' '''.join(str(n.key      ) if n.key == node.key else '''|''' for n in forwards      )      )
																		lines.append(''' ''' * label_size + '''| ''' * len(A_      )      )
																		__lowerCamelCase   :   Union[str, Any]       =node.forward

											lines.append('''None'''.ljust(A_      ) + '''* ''' * len(A_      )      )
											return f'SkipList(level={self.level})\n' + "\n".join(A_      )




				def __iter__(  self	:Dict      ):
											__lowerCamelCase   :   Union[str, Any]       =self.head

											while len(node.forward      ) != 0:
																		yield node.forward[0].key
																		__lowerCamelCase   :   Optional[Any]       =node.forward[0]




				def        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   Any       =1
											while random() < self.p and level < self.max_level:
																		level += 1

											return level




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Dict      ):
											__lowerCamelCase   :   Tuple       =[]

											__lowerCamelCase   :   Union[str, Any]       =self.head

											for i in reversed(range(self.level      )      ):
																		# i < node.level - When node level is lesser than `i` decrement `i`.
																		# node.forward[i].key < key - Jumping to node with key value higher
																		#                             or equal to searched key would result
																		#                             in skipping searched key.
																		while i < node.level and node.forward[i].key < key:
																									__lowerCamelCase   :   Union[str, Any]       =node.forward[i]
																		# Each leftmost node (relative to searched node) will potentially have to
																		# be updated.
																		update_vector.append(A_      )

											update_vector.reverse()  # Note that we were inserting values in reverse order.

											# len(node.forward) != 0 - If current node doesn't contain any further
											#                          references then searched key is not present.
											# node.forward[0].key == key - Next node key should be equal to search key
											#                              if key is present.
											if len(node.forward      ) != 0 and node.forward[0].key == key:
																		return node.forward[0], update_vector
											else:
																		return None, update_vector




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   int       =self._locate_node(A_      )

											if node is not None:
																		for i, update_node in enumerate(A_      ):
																									# Remove or replace all references to removed node.
																									if update_node.level > i and update_node.forward[i].key == key:
																																if node.level > i:
																																							__lowerCamelCase   :   Optional[int]       =node.forward[i]
																																else:
																																							__lowerCamelCase   :   List[str]       =update_node.forward[:i]




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:List[Any]    ,     __lowercase	:str      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   Any       =self._locate_node(A_      )
											if node is not None:
																		__lowerCamelCase   :   Dict       =value
											else:
																		__lowerCamelCase   :   Tuple       =self.random_level()

																		if level > self.level:
																									# After level increase we have to add additional nodes to head.
																									for _ in range(self.level - 1    ,     A_      ):
																																update_vector.append(self.head      )
																									__lowerCamelCase   :   List[Any]       =level

																		__lowerCamelCase   :   Tuple       =Node(A_    ,     A_      )

																		for i, update_node in enumerate(update_vector[:level]      ):
																									# Change references to pass through new node.
																									if update_node.level > i:
																																new_node.forward.append(update_node.forward[i]      )

																									if update_node.level < i + 1:
																																update_node.forward.append(A_      )
																									else:
																																__lowerCamelCase   :   Optional[int]       =new_node




				def        __lowercase	(  self	:Dict    ,     __lowercase	:Optional[Any]      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   Tuple       =self._locate_node(A_      )

											if node is not None:
																		return node.value

											return None



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   str       =SkipList()
							skip_list.insert('''Key1'''     ,    3 )
							skip_list.insert('''Key2'''     ,    12 )
							skip_list.insert('''Key3'''     ,    41 )
							skip_list.insert('''Key4'''     ,    -19 )

							__lowerCamelCase   :   Optional[int]       =skip_list.head
							__lowerCamelCase   :   str       ={}
							while node.level != 0:
														__lowerCamelCase   :   Optional[Any]       =node.forward[0]
														__lowerCamelCase   :   str       =node.value

							assert len(lowerCAmelCase_ ) == 4
							assert all_values["Key1"] == 3
							assert all_values["Key2"] == 12
							assert all_values["Key3"] == 41
							assert all_values["Key4"] == -19



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   str       =SkipList()
							skip_list.insert('''Key1'''     ,    10 )
							skip_list.insert('''Key1'''     ,    12 )

							skip_list.insert('''Key5'''     ,    7 )
							skip_list.insert('''Key7'''     ,    10 )
							skip_list.insert('''Key10'''     ,    5 )

							skip_list.insert('''Key7'''     ,    7 )
							skip_list.insert('''Key5'''     ,    5 )
							skip_list.insert('''Key10'''     ,    10 )

							__lowerCamelCase   :   Dict       =skip_list.head
							__lowerCamelCase   :   str       ={}
							while node.level != 0:
														__lowerCamelCase   :   Union[str, Any]       =node.forward[0]
														__lowerCamelCase   :   str       =node.value

							if len(lowerCAmelCase_ ) != 4:
														print()
							assert len(lowerCAmelCase_ ) == 4
							assert all_values["Key1"] == 12
							assert all_values["Key7"] == 7
							assert all_values["Key5"] == 5
							assert all_values["Key10"] == 10



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   List[Any]       =SkipList()
							assert skip_list.find('''Some key''' ) is None



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =SkipList()

							skip_list.insert('''Key2'''     ,    20 )
							assert skip_list.find('''Key2''' ) == 20

							skip_list.insert('''Some Key'''     ,    10 )
							skip_list.insert('''Key2'''     ,    8 )
							skip_list.insert('''V'''     ,    13 )

							assert skip_list.find('''Y''' ) is None
							assert skip_list.find('''Key2''' ) == 8
							assert skip_list.find('''Some Key''' ) == 10
							assert skip_list.find('''V''' ) == 13



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =SkipList()
							skip_list.delete('''Some key''' )

							assert len(skip_list.head.forward ) == 0



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   List[str]       =SkipList()

							skip_list.insert('''Key1'''     ,    12 )
							skip_list.insert('''V'''     ,    13 )
							skip_list.insert('''X'''     ,    14 )
							skip_list.insert('''Key2'''     ,    15 )

							skip_list.delete('''V''' )
							skip_list.delete('''Key2''' )

							assert skip_list.find('''V''' ) is None
							assert skip_list.find('''Key2''' ) is None



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =SkipList()

							skip_list.insert('''Key1'''     ,    12 )
							skip_list.insert('''V'''     ,    13 )
							skip_list.insert('''X'''     ,    14 )
							skip_list.insert('''Key2'''     ,    15 )

							skip_list.delete('''V''' )
							assert skip_list.find('''V''' ) is None
							assert skip_list.find('''X''' ) == 14
							assert skip_list.find('''Key1''' ) == 12
							assert skip_list.find('''Key2''' ) == 15

							skip_list.delete('''X''' )
							assert skip_list.find('''V''' ) is None
							assert skip_list.find('''X''' ) is None
							assert skip_list.find('''Key1''' ) == 12
							assert skip_list.find('''Key2''' ) == 15

							skip_list.delete('''Key1''' )
							assert skip_list.find('''V''' ) is None
							assert skip_list.find('''X''' ) is None
							assert skip_list.find('''Key1''' ) is None
							assert skip_list.find('''Key2''' ) == 15

							skip_list.delete('''Key2''' )
							assert skip_list.find('''V''' ) is None
							assert skip_list.find('''X''' ) is None
							assert skip_list.find('''Key1''' ) is None
							assert skip_list.find('''Key2''' ) is None



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =SkipList()

							skip_list.insert('''Key1'''     ,    12 )
							skip_list.insert('''V'''     ,    13 )
							skip_list.insert('''X'''     ,    142 )
							skip_list.insert('''Key2'''     ,    15 )

							skip_list.delete('''X''' )

							def traverse_keys(SCREAMING_SNAKE_CASE							:				Tuple ):
														yield node.key
														for forward_node in node.forward:
																					yield from traverse_keys(lowerCAmelCase_ )

							assert len(set(traverse_keys(skip_list.head ) ) ) == 4



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							def is_sorted(SCREAMING_SNAKE_CASE							:				Optional[int] ):
														return all(next_item >= item for item, next_item in zip(lowerCAmelCase_     ,    lst[1:] ) )

							__lowerCamelCase   :   Union[str, Any]       =SkipList()
							for i in range(10 ):
														skip_list.insert(lowerCAmelCase_     ,    lowerCAmelCase_ )
							assert is_sorted(list(lowerCAmelCase_ ) )
							skip_list.delete(5 )
							skip_list.delete(8 )
							skip_list.delete(2 )
							assert is_sorted(list(lowerCAmelCase_ ) )
							skip_list.insert(-12     ,    -12 )
							skip_list.insert(77     ,    77 )
							assert is_sorted(list(lowerCAmelCase_ ) )



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							for _ in range(100 ):
														# Repeat test 100 times due to the probabilistic nature of skip list
														# random values == random bugs
														test_insert()
														test_insert_overrides_existing_value()

														test_searching_empty_list_returns_none()
														test_search()

														test_deleting_item_from_empty_list_do_nothing()
														test_deleted_items_are_not_founded_by_find_method()
														test_delete_removes_only_given_key()
														test_delete_doesnt_leave_dead_nodes()

														test_iter_always_yields_sorted_values()



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =SkipList()
							skip_list.insert(2     ,    '''2''' )
							skip_list.insert(4     ,    '''4''' )
							skip_list.insert(6     ,    '''4''' )
							skip_list.insert(4     ,    '''5''' )
							skip_list.insert(8     ,    '''4''' )
							skip_list.insert(9     ,    '''4''' )

							skip_list.delete(4 )

							print(lowerCAmelCase_ )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()











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()
_A					:				List[str]				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224''' ,							out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
    SCREAMING_SNAKE_CASE__											=			MaskFormerConfig(backbone_config=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    if "ade20k-full" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			8_4_7
        SCREAMING_SNAKE_CASE__											=			'''maskformer-ade20k-full-id2label.json'''
    elif "ade" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_5_0
        SCREAMING_SNAKE_CASE__											=			'''ade20k-id2label.json'''
    elif "coco-stuff" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_7_1
        SCREAMING_SNAKE_CASE__											=			'''maskformer-coco-stuff-id2label.json'''
    elif "coco" in model_name:
        # TODO
        SCREAMING_SNAKE_CASE__											=			1_3_3
        SCREAMING_SNAKE_CASE__											=			'''coco-panoptic-id2label.json'''
    elif "cityscapes" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_9
        SCREAMING_SNAKE_CASE__											=			'''cityscapes-id2label.json'''
    elif "vistas" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			6_5
        SCREAMING_SNAKE_CASE__											=			'''mapillary-vistas-id2label.json'''

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

    return config


def 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    # 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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			dct.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			val


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
    for i in range(len(backbone_config.depths	)	):
        SCREAMING_SNAKE_CASE__											=			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)
            SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''	)
            SCREAMING_SNAKE_CASE__											=			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
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[:dim, :]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[: dim]
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[
                dim : dim * 2, :
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[
                dim : dim * 2
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[
                -dim :, :
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[-dim :]
            # fmt: on


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    # fmt: off
    SCREAMING_SNAKE_CASE__											=			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)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[: hidden_size, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:config.hidden_size]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[hidden_size : hidden_size * 2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-hidden_size :, :]
        SCREAMING_SNAKE_CASE__											=			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)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[: hidden_size, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:config.hidden_size]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[hidden_size : hidden_size * 2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-hidden_size :, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[-hidden_size :]
    # fmt: on


def 							__snake_case							(    )				->						torch.Tensor:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = False	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			get_maskformer_config(lowerCAmelCase_	)

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

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

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

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

    # load 🤗 model
    SCREAMING_SNAKE_CASE__											=			MaskFormerForInstanceSegmentation(lowerCAmelCase_	)
    model.eval()

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

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

    # verify results
    SCREAMING_SNAKE_CASE__											=			prepare_img()
    if "vistas" in model_name:
        SCREAMING_SNAKE_CASE__											=			6_5
    elif "cityscapes" in model_name:
        SCREAMING_SNAKE_CASE__											=			6_5_5_3_5
    else:
        SCREAMING_SNAKE_CASE__											=			2_5_5
    SCREAMING_SNAKE_CASE__											=			True if '''ade''' in model_name else False
    SCREAMING_SNAKE_CASE__											=			MaskFormerImageProcessor(ignore_index=lowerCAmelCase_ ,							reduce_labels=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			image_processor(lowerCAmelCase_ ,							return_tensors='''pt'''	)

    SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)

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

    if model_name == "maskformer-swin-tiny-ade":
        SCREAMING_SNAKE_CASE__											=			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] ,							lowerCAmelCase_ ,							atol=1e-4	)
    print('''Looks ok!'''	)

    if pytorch_dump_folder_path is not None:
        print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''	)
        Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
        model.save_pretrained(lowerCAmelCase_	)
        image_processor.save_pretrained(lowerCAmelCase_	)

    if push_to_hub:
        print('''Pushing model and image processor to the hub...'''	)
        model.push_to_hub(f'''nielsr/{model_name}'''	)
        image_processor.push_to_hub(f'''nielsr/{model_name}'''	)


if __name__ == "__main__":
  _A					:				Optional[Any]				=			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."""
  )

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