


from __future__ import annotations

from random import random
from typing import Generic, TypeVar

_snake_case					:		Tuple      =    TypeVar("KT")
_snake_case					:		str      =    TypeVar("VT")

class     a		(Generic[KT, VT]				):




							"""simple docstring"""


							def __init__(							self	:  Optional[Any]   ,     lowerCamelCase	:  KT | str = "root"   ,     lowerCamelCase	:  VT | None = None    )     ->   Any:
														__snake_case		:      Union[str, Any]										=       key
														__snake_case		:      Tuple										=       value
														__snake_case		:      list[Node[KT, VT]]										=       []

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






							@property
							def    __snake_case (							self	:  int    )     ->   int:
														return len(self.forward    )

class     a		(Generic[KT, VT]				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     lowerCamelCase	:  float = 0.5   ,     lowerCamelCase	:  int = 16    )     ->   Dict:
														__snake_case		:      Node[KT, VT]										=       Node[KT, VT]()
														__snake_case		:      Optional[int]										=       0
														__snake_case		:      Optional[Any]										=       p
														__snake_case		:      Tuple										=       max_level

							def __str__(							self	:  Optional[Any]    )     ->   str:
														__snake_case		:      Union[str, Any]										=       list(self    )

														if len(lowerCamelCase    ) == 0:
																					return F'SkipList(level={self.level})'

														__snake_case		:      List[str]										=       max((len(str(lowerCamelCase    )    ) for item in items)   ,     default=4    )
														__snake_case		:      List[str]										=       max(lowerCamelCase   ,     4    ) + 4

														__snake_case		:      Dict										=       self.head
														__snake_case		:      int										=       []

														__snake_case		:      int										=       node.forward.copy()
														lines.append(F'[{node.key}]'.ljust(lowerCamelCase   ,     "-"    ) + "* " * len(lowerCamelCase    )    )
														lines.append(" " * label_size + "| " * len(lowerCamelCase    )    )

														while len(node.forward    ) != 0:
																					__snake_case		:      List[Any]										=       node.forward[0]

																					lines.append(
																					    F'[{node.key}]'.ljust(lowerCamelCase   ,     "-"    )
																					    + " ".join(str(n.key    ) if n.key == node.key else "|" for n in forwards    )    )
																					lines.append(" " * label_size + "| " * len(lowerCamelCase    )    )
																					__snake_case		:      Tuple										=       node.forward

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

							def __iter__(							self	:  List[str]    )     ->   Tuple:
														__snake_case		:      Dict										=       self.head

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

							def    __snake_case (							self	:  Optional[int]    )     ->   int:
														__snake_case		:      Dict										=       1
														while random() < self.p and level < self.max_level:
																					level += 1

														return level

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  str    )     ->   tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
														__snake_case		:      Optional[int]										=       []

														__snake_case		:      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:
																												__snake_case		:      List[Any]										=       node.forward[i]
																					# Each leftmost node (relative to searched node) will potentially have to
																					# be updated.
																					update_vector.append(lowerCamelCase    )

														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    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  KT    )     ->   List[Any]:
														__snake_case		:      Any										=       self._locate_node(lowerCamelCase    )

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

							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  KT   ,     lowerCamelCase	:  VT    )     ->   Optional[Any]:
														__snake_case		:      Union[str, Any]										=       self._locate_node(lowerCamelCase    )
														if node is not None:
																					__snake_case		:      List[Any]										=       value
														else:
																					__snake_case		:      int										=       self.random_level()

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

																					__snake_case		:      Optional[Any]										=       Node(lowerCamelCase   ,     lowerCamelCase    )

																					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(lowerCamelCase    )
																												else:
																																			__snake_case		:      Optional[int]										=       new_node






							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  VT    )     ->   VT | None:
														__snake_case		:      Any										=       self._locate_node(lowerCamelCase    )

														if node is not None:
																					return node.value

														return None







def     lowerCAmelCase_					( ):
							__snake_case		:      List[str]										=       SkipList()
							skip_list.insert("Key1"							,     3     )
							skip_list.insert("Key2"							,     1_2     )
							skip_list.insert("Key3"							,     4_1     )
							skip_list.insert("Key4"							,     -1_9     )

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

							assert len(__lowerCamelCase     ) == 4
							assert all_values["Key1"] == 3
							assert all_values["Key2"] == 1_2
							assert all_values["Key3"] == 4_1
							assert all_values["Key4"] == -1_9







def     lowerCAmelCase_					( ):
							__snake_case		:      Optional[int]										=       SkipList()
							skip_list.insert("Key1"							,     1_0     )
							skip_list.insert("Key1"							,     1_2     )

							skip_list.insert("Key5"							,     7     )
							skip_list.insert("Key7"							,     1_0     )
							skip_list.insert("Key10"							,     5     )

							skip_list.insert("Key7"							,     7     )
							skip_list.insert("Key5"							,     5     )
							skip_list.insert("Key10"							,     1_0     )

							__snake_case		:      List[Any]										=       skip_list.head
							__snake_case		:      Optional[Any]										=       {}
							while node.level != 0:
														__snake_case		:      Any										=       node.forward[0]
														__snake_case		:      List[Any]										=       node.value

							if len(__lowerCamelCase     ) != 4:
														print()
							assert len(__lowerCamelCase     ) == 4
							assert all_values["Key1"] == 1_2
							assert all_values["Key7"] == 7
							assert all_values["Key5"] == 5
							assert all_values["Key10"] == 1_0







def     lowerCAmelCase_					( ):
							__snake_case		:      str										=       SkipList()
							assert skip_list.find("Some key"     ) is None







def     lowerCAmelCase_					( ):
							__snake_case		:      Any										=       SkipList()

							skip_list.insert("Key2"							,     2_0     )
							assert skip_list.find("Key2"     ) == 2_0

							skip_list.insert("Some Key"							,     1_0     )
							skip_list.insert("Key2"							,     8     )
							skip_list.insert("V"							,     1_3     )

							assert skip_list.find("Y"     ) is None
							assert skip_list.find("Key2"     ) == 8
							assert skip_list.find("Some Key"     ) == 1_0
							assert skip_list.find("V"     ) == 1_3







def     lowerCAmelCase_					( ):
							__snake_case		:      Optional[int]										=       SkipList()
							skip_list.delete("Some key"     )

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







def     lowerCAmelCase_					( ):
							__snake_case		:      str										=       SkipList()

							skip_list.insert("Key1"							,     1_2     )
							skip_list.insert("V"							,     1_3     )
							skip_list.insert("X"							,     1_4     )
							skip_list.insert("Key2"							,     1_5     )

							skip_list.delete("V"     )
							skip_list.delete("Key2"     )

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







def     lowerCAmelCase_					( ):
							__snake_case		:      Optional[Any]										=       SkipList()

							skip_list.insert("Key1"							,     1_2     )
							skip_list.insert("V"							,     1_3     )
							skip_list.insert("X"							,     1_4     )
							skip_list.insert("Key2"							,     1_5     )

							skip_list.delete("V"     )
							assert skip_list.find("V"     ) is None
							assert skip_list.find("X"     ) == 1_4
							assert skip_list.find("Key1"     ) == 1_2
							assert skip_list.find("Key2"     ) == 1_5

							skip_list.delete("X"     )
							assert skip_list.find("V"     ) is None
							assert skip_list.find("X"     ) is None
							assert skip_list.find("Key1"     ) == 1_2
							assert skip_list.find("Key2"     ) == 1_5

							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"     ) == 1_5

							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_					( ):
							__snake_case		:      Any										=       SkipList()

							skip_list.insert("Key1"							,     1_2     )
							skip_list.insert("V"							,     1_3     )
							skip_list.insert("X"							,     1_4_2     )
							skip_list.insert("Key2"							,     1_5     )

							skip_list.delete("X"     )

							def traverse_keys(__lowerCamelCase     ):
														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_					( ):
							def is_sorted(__lowerCamelCase     ):
														return all(next_item >= item for item, next_item in zip(__lowerCamelCase							,     lst[1:]     )     )

							__snake_case		:      Optional[int]										=       SkipList()
							for i in range(1_0     ):
														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(-1_2							,     -1_2     )
							skip_list.insert(7_7							,     7_7     )
							assert is_sorted(list(__lowerCamelCase     )     )







def     lowerCAmelCase_					( ):
							for _ in range(1_0_0     ):
														# 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_					( ):
							__snake_case		:      Tuple										=       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()
_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Dict										=       SwinConfig.from_pretrained(
							    "microsoft/swin-tiny-patch4-window7-224"							,     out_features=["stage1", "stage2", "stage3", "stage4"]     )
							__snake_case		:      List[Any]										=       MaskFormerConfig(backbone_config=__lowerCamelCase     )

							__snake_case		:      List[Any]										=       "huggingface/label-files"
							if "ade20k-full" in model_name:
														# this should be ok
														__snake_case		:      Any										=       8_4_7
														__snake_case		:      List[Any]										=       "maskformer-ade20k-full-id2label.json"
							elif "ade" in model_name:
														# this should be ok
														__snake_case		:      Optional[int]										=       1_5_0
														__snake_case		:      int										=       "ade20k-id2label.json"
							elif "coco-stuff" in model_name:
														# this should be ok
														__snake_case		:      Optional[Any]										=       1_7_1
														__snake_case		:      List[str]										=       "maskformer-coco-stuff-id2label.json"
							elif "coco" in model_name:
														# TODO
														__snake_case		:      Optional[int]										=       1_3_3
														__snake_case		:      int										=       "coco-panoptic-id2label.json"
							elif "cityscapes" in model_name:
														# this should be ok
														__snake_case		:      Union[str, Any]										=       1_9
														__snake_case		:      Dict										=       "cityscapes-id2label.json"
							elif "vistas" in model_name:
														# this should be ok
														__snake_case		:      Any										=       6_5
														__snake_case		:      Any										=       "mapillary-vistas-id2label.json"

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

							return config







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Dict										=       []
							# stem
							# fmt: off
							rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")     )
							rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")     )
							rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")     )
							rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")     )
							# stages
							for i in range(len(config.backbone_config.depths     )     ):
														for j in range(config.backbone_config.depths[i]     ):
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')     )
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')     )

														if i < 3:
																					rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')     )
																					rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')     )
																					rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')     )
														rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')     )
														rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')     )

							# FPN
							rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")     )
							rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")     )
							rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")     )
							for source_index, target_index in zip(range(3							,     0							,     -1     )							,     range(0							,     3     )     ):
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')     )
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')     )
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')     )
														rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')     )
														rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')     )
														rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')     )
							rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")     )
							rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")     )

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers     ):
														# self-attention out projection
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')     )
														# cross-attention out projection
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')     )
														# MLP 1
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')     )
														# MLP 2
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')     )
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')     )
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')     )
														# layernorm 3 (final layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')     )

							rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")     )
							rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")     )

							# heads on top
							rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")     )

							rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")     )
							rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")     )

							rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")     )
							rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")     )

							for i in range(3     ):
														rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')     )
														rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')     )
							# fmt: on

							return rename_keys







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Dict										=       dct.pop(__lowerCamelCase     )
							__snake_case		:      Any										=       val







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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							# fmt: off
							__snake_case		:      Optional[int]										=       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)
														__snake_case		:      List[str]										=       state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'     )
														__snake_case		:      Union[str, Any]										=       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
														__snake_case		:      Any										=       in_proj_weight[: hidden_size, :]
														__snake_case		:      Optional[int]										=       in_proj_bias[:config.hidden_size]
														__snake_case		:      Any										=       in_proj_weight[hidden_size : hidden_size * 2, :]
														__snake_case		:      Any										=       in_proj_bias[hidden_size : hidden_size * 2]
														__snake_case		:      Tuple										=       in_proj_weight[-hidden_size :, :]
														__snake_case		:      Optional[Any]										=       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)
														__snake_case		:      Optional[Any]										=       state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'     )
														__snake_case		:      Union[str, Any]										=       state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'     )
														# next, add query, keys and values (in that order) to the state dict
														__snake_case		:      int										=       in_proj_weight[: hidden_size, :]
														__snake_case		:      Tuple										=       in_proj_bias[:config.hidden_size]
														__snake_case		:      str										=       in_proj_weight[hidden_size : hidden_size * 2, :]
														__snake_case		:      Optional[Any]										=       in_proj_bias[hidden_size : hidden_size * 2]
														__snake_case		:      Optional[Any]										=       in_proj_weight[-hidden_size :, :]
														__snake_case		:      Tuple										=       in_proj_bias[-hidden_size :]
							# fmt: on







def     lowerCAmelCase_					( ):
							__snake_case		:      List[str]										=       "http://images.cocodataset.org/val2017/000000039769.jpg"
							__snake_case		:      List[str]										=       Image.open(requests.get(__lowerCamelCase							,     stream=__lowerCamelCase     ).raw     )
							return im







@torch.no_grad()
def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = False     ):
							__snake_case		:      Optional[int]										=       get_maskformer_config(__lowerCamelCase     )

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

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

							# rename keys
							__snake_case		:      Tuple										=       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():
														__snake_case		:      int										=       torch.from_numpy(__lowerCamelCase     )

							# load 🤗 model
							__snake_case		:      List[str]										=       MaskFormerForInstanceSegmentation(__lowerCamelCase     )
							model.eval()

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

							__snake_case			,      __snake_case		:      List[str]										=       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
							__snake_case		:      Union[str, Any]										=       prepare_img()
							if "vistas" in model_name:
														__snake_case		:      Optional[int]										=       6_5
							elif "cityscapes" in model_name:
														__snake_case		:      Optional[int]										=       6_5_5_3_5
							else:
														__snake_case		:      Union[str, Any]										=       2_5_5
							__snake_case		:      Union[str, Any]										=       True if "ade" in model_name else False
							__snake_case		:      str										=       MaskFormerImageProcessor(ignore_index=__lowerCamelCase							,     reduce_labels=__lowerCamelCase     )

							__snake_case		:      List[str]										=       image_processor(__lowerCamelCase							,     return_tensors="pt"     )

							__snake_case		:      Tuple										=       model(**__lowerCamelCase     )

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

							if model_name == "maskformer-swin-tiny-ade":
														__snake_case		:      Optional[Any]										=       torch.tensor(
														    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]     )
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]							,     __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__":
			_snake_case					:		List[str]      =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="maskformer-swin-tiny-ade",
			    type=str,
			    help=("Name of the MaskFormer model you'd like to convert",),
			)
			parser.add_argument(
			    "--checkpoint_path",
			    default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
			    type=str,
			    help="Path to the original state dict (.pth file).",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
			)
			parser.add_argument(
			    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
			)

			_snake_case					:		List[str]      =    parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)

