



"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
		# encoder.embeddings are double copied in original FLAVA
		return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Optional[int] 						=     {}

		for key, value in state_dict.items():
				if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
						continue

				a_     :      Tuple 						=     key.replace("heads.cmd.mim_head.cls.predictions", "mmm_image_head"	)
				a_     :      List[str] 						=     key.replace("heads.cmd.mlm_head.cls.predictions", "mmm_text_head"	)
				a_     :      str 						=     key.replace("heads.cmd.itm_head.cls", "itm_head"	)
				a_     :      List[Any] 						=     key.replace("heads.cmd.itm_head.pooler", "itm_head.pooler"	)
				a_     :      Dict 						=     key.replace("heads.cmd.clip_head.logit_scale", "flava.logit_scale"	)
				a_     :      List[Any] 						=     key.replace("heads.fairseq_mlm.cls.predictions", "mlm_head"	)
				a_     :      Any 						=     key.replace("heads.imagenet.mim_head.cls.predictions", "mim_head"	)
				a_     :      Any 						=     key.replace("mm_text_projection", "flava.text_to_mm_projection"	)
				a_     :      Optional[Any] 						=     key.replace("mm_image_projection", "flava.image_to_mm_projection"	)
				a_     :      Tuple 						=     key.replace("image_encoder.module", "flava.image_model"	)
				a_     :      Tuple 						=     key.replace("text_encoder.module", "flava.text_model"	)
				a_     :      Optional[int] 						=     key.replace("mm_encoder.module.encoder.cls_token", "flava.multimodal_model.cls_token"	)
				a_     :      Any 						=     key.replace("mm_encoder.module", "flava.multimodal_model"	)
				a_     :      int 						=     key.replace("text_projection", "flava.text_projection"	)
				a_     :      Union[str, Any] 						=     key.replace("image_projection", "flava.image_projection"	)

				a_     :      Optional[int] 						=     value.float()

		for key, value in codebook_state_dict.items():
				a_     :      Dict 						=     value

		return upgrade



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      int:
		if config_path is not None:
				a_     :      List[Any] 						=     FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Union[str, Any] 						=     FlavaConfig()

		a_     :      Optional[Any] 						=     FlavaForPreTraining(SCREAMING_SNAKE_CASE__	).eval()

		a_     :      Optional[Any] 						=     convert_dalle_checkpoint(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, save_checkpoint=SCREAMING_SNAKE_CASE__	)

		if os.path.exists(SCREAMING_SNAKE_CASE__	):
				a_     :      List[str] 						=     torch.load(SCREAMING_SNAKE_CASE__, map_location="cpu"	)
		else:
				a_     :      Optional[Any] 						=     torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__, map_location="cpu"	)

		a_     :      Tuple 						=     upgrade_state_dict(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		hf_model.load_state_dict(SCREAMING_SNAKE_CASE__	)
		a_     :      Optional[Any] 						=     hf_model.state_dict()
		a_     :      List[Any] 						=     count_parameters(SCREAMING_SNAKE_CASE__	)
		a_     :      Any 						=     count_parameters(SCREAMING_SNAKE_CASE__	) + count_parameters(SCREAMING_SNAKE_CASE__	)

		assert torch.allclose(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, atol=1e-3	)

		hf_model.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
					parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					SCREAMING_SNAKE_CASE_				= parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




"""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
					)