



"""simple docstring"""
from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_				= [
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

SCREAMING_SNAKE_CASE_				= [
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]



def        lowerCAmelCase_						(							)			->      str:
		a_     :      List[Any] 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, bootstrap_aggregation=SCREAMING_SNAKE_CASE__, rouge_keys=["rouge2", "rougeL"]	)
		assert isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_     :      str 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, bootstrap_aggregation=SCREAMING_SNAKE_CASE__, rouge_keys=["rouge2"]	)
		assert (
		    pd.DataFrame(no_aggregation["rouge2"]	).fmeasure.mean()
		    == pd.DataFrame(no_aggregation_just_ra["rouge2"]	).fmeasure.mean()
		)



def        lowerCAmelCase_						(							)			->      str:
		a_     :      Dict 						=     "rougeLsum"
		a_     :      List[str] 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, newline_sep=SCREAMING_SNAKE_CASE__, rouge_keys=[k]	)[k]
		a_     :      Dict 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, newline_sep=SCREAMING_SNAKE_CASE__, rouge_keys=[k]	)[k]
		assert score > score_no_sep



def        lowerCAmelCase_						(							)			->      Tuple:
		a_     :      Any 						=     ["rouge1", "rouge2", "rougeL"]
		a_     :      Union[str, Any] 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, newline_sep=SCREAMING_SNAKE_CASE__, rouge_keys=SCREAMING_SNAKE_CASE__	)
		a_     :      Any 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, newline_sep=SCREAMING_SNAKE_CASE__, rouge_keys=SCREAMING_SNAKE_CASE__	)
		assert score_sep == score_no_sep



def        lowerCAmelCase_						(							)			->      Union[str, Any]:
		a_     :      Optional[Any] 						=     [
		    "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
		    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
		]
		a_     :      Union[str, Any] 						=     [
		    "Margot Frank, died in 1945, a month earlier than previously thought.",
		    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
		    " the final seconds on board Flight 9525.",
		]
		assert calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, newline_sep=SCREAMING_SNAKE_CASE__	) == calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, newline_sep=SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							)			->      str:
		a_     :      Dict 						=     [
		    "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
		]
		a_     :      Tuple 						=     [
		    " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
		]

		a_     :      Union[str, Any] 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, rouge_keys=["rougeLsum"], newline_sep=SCREAMING_SNAKE_CASE__	)["rougeLsum"]
		a_     :      int 						=     calculate_rouge(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, rouge_keys=["rougeLsum"]	)["rougeLsum"]
		assert new_score > prev_score



def        lowerCAmelCase_						(							)			->      List[Any]:
		a_     :      str 						=     Path("examples/seq2seq/test_data/wmt_en_ro"	)
		a_     :      str 						=     calculate_rouge_path(data_dir.joinpath("test.source"	), data_dir.joinpath("test.target"	)	)
		assert isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_     :      Tuple 						=     calculate_rouge_path(
		    data_dir.joinpath("test.source"	), data_dir.joinpath("test.target"	), bootstrap_aggregation=SCREAMING_SNAKE_CASE__	)
		assert isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)




"""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()
SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      List[Any] 						=     SwinConfig.from_pretrained(
		    "microsoft/swin-tiny-patch4-window7-224", out_features=["stage1", "stage2", "stage3", "stage4"]	)
		a_     :      List[str] 						=     MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE__	)

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

		a_     :      List[str] 						=     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, repo_type="dataset"	), "r"	)	)
		a_     :      Optional[Any] 						=     {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}

		return config



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

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

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

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

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

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

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

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

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

		return rename_keys



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Optional[int] 						=     dct.pop(SCREAMING_SNAKE_CASE__	)
		a_     :      List[Any] 						=     val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		a_     :      int 						=     [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
		for i in range(len(backbone_config.depths	)	):
				a_     :      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)
						a_     :      List[str] 						=     state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
						a_     :      str 						=     state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""	)
						# next, add query, keys and values (in that order) to the state dict
						a_     :      Tuple 						=     in_proj_weight[:dim, :]
						a_     :      Union[str, Any] 						=     in_proj_bias[: dim]
						a_     :      Dict 						=     in_proj_weight[
						    dim : dim * 2, :
						]
						a_     :      Tuple 						=     in_proj_bias[
						    dim : dim * 2
						]
						a_     :      Optional[int] 						=     in_proj_weight[
						    -dim :, :
						]
						a_     :      str 						=     in_proj_bias[-dim :]
						# fmt: on



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



def        lowerCAmelCase_						(							)			->      torch.Tensor:
		a_     :      Any 						=     "http://images.cocodataset.org/val2017/000000039769.jpg"
		a_     :      Optional[Any] 						=     Image.open(requests.get(SCREAMING_SNAKE_CASE__, stream=SCREAMING_SNAKE_CASE__	).raw	)
		return im



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

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

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

		# rename keys
		a_     :      List[Any] 						=     create_rename_keys(SCREAMING_SNAKE_CASE__	)
		for src, dest in rename_keys:
				rename_key(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__, config.backbone_config	)
		read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

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

		# load 🤗 model
		a_     :      Tuple 						=     MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__	)
		model.eval()

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

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

		# verify results
		a_     :      List[Any] 						=     prepare_img()
		if "vistas" in model_name:
				a_     :      int 						=     65
		elif "cityscapes" in model_name:
				a_     :      str 						=     65_535
		else:
				a_     :      int 						=     255
		a_     :      List[str] 						=     True if "ade" in model_name else False
		a_     :      Any 						=     MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE__, reduce_labels=SCREAMING_SNAKE_CASE__	)

		a_     :      int 						=     image_processor(SCREAMING_SNAKE_CASE__, return_tensors="pt"	)

		a_     :      Optional[int] 						=     model(**SCREAMING_SNAKE_CASE__	)

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

		if model_name == "maskformer-swin-tiny-ade":
				a_     :      Union[str, Any] 						=     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], SCREAMING_SNAKE_CASE__, atol=1e-4	)
		print("Looks ok!"	)

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= 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."""
					)

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