




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:	Any				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
			snake_case				:    Optional[Any]           =	"""huggingface/label-files"""
			snake_case				:    Tuple           =	"""imagenet-1k-id2label.json"""
			snake_case				:    List[Any]           =	json.load(open(hf_hub_download(lowerCAmelCase__						,lowerCAmelCase__						,repo_type="""dataset"""					)						,"""r"""					)					)
			snake_case				:    Any           =	{int(lowerCAmelCase__					): v for k, v in idalabel.items()}
			snake_case				:    List[str]           =	{v: k for k, v in idalabel.items()}

			snake_case				:    Dict           =	"""std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			snake_case				:    List[Any]           =	BitConfig(
			    conv_layer=lowerCAmelCase__						,num_labels=1000						,idalabel=lowerCAmelCase__						,labelaid=lowerCAmelCase__						,)

			return config




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Tuple:



			if "stem.conv" in name:
						snake_case				:    Union[str, Any]           =	name.replace("""stem.conv"""						,"""bit.embedder.convolution"""					)
			if "blocks" in name:
						snake_case				:    str           =	name.replace("""blocks"""						,"""layers"""					)
			if "head.fc" in name:
						snake_case				:    Optional[Any]           =	name.replace("""head.fc"""						,"""classifier.1"""					)
			if name.startswith("""norm"""					):
						snake_case				:    Optional[Any]           =	"""bit.""" + name
			if "bit" not in name and "classifier" not in name:
						snake_case				:    List[Any]           =	"""bit.encoder.""" + name

			return name




def 						SCREAMING_SNAKE_CASE__ (   )  ->       str:
			snake_case				:    List[str]           =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			snake_case				:    int           =	Image.open(requests.get(lowerCAmelCase__						,stream=lowerCAmelCase__					).raw					)
			return im




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=False					)  ->       Tuple:
			snake_case				:    Tuple           =	get_config(lowerCAmelCase__					)

			# load original model from timm
			snake_case				:    Tuple           =	create_model(lowerCAmelCase__						,pretrained=lowerCAmelCase__					)
			timm_model.eval()

			# load state_dict of original model
			snake_case				:    Tuple           =	timm_model.state_dict()
			for key in state_dict.copy().keys():
						snake_case				:    Dict           =	state_dict.pop(lowerCAmelCase__					)
						snake_case				:    int           =	val.squeeze() if """head""" in key else val

			# load HuggingFace model
			snake_case				:    int           =	BitForImageClassification(lowerCAmelCase__					)
			model.eval()
			model.load_state_dict(lowerCAmelCase__					)

			# create image processor
			snake_case				:    List[Any]           =	create_transform(**resolve_data_config({}						,model=lowerCAmelCase__					)					)
			snake_case				:    List[str]           =	transform.transforms

			snake_case				:    Union[str, Any]           =	{
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			snake_case				:    int           =	BitImageProcessor(
			    do_resize=lowerCAmelCase__						,size={"""shortest_edge""": timm_transforms[0].size}						,resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,do_center_crop=lowerCAmelCase__						,crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}						,do_normalize=lowerCAmelCase__						,image_mean=timm_transforms[-1].mean.tolist()						,image_std=timm_transforms[-1].std.tolist()						,)

			snake_case				:    Optional[Any]           =	prepare_img()
			snake_case				:    int           =	transform(lowerCAmelCase__					).unsqueeze(0					)
			snake_case				:    Tuple           =	processor(lowerCAmelCase__						,return_tensors="""pt"""					).pixel_values

			# verify pixel values
			assert torch.allclose(lowerCAmelCase__						,lowerCAmelCase__					)

			# verify logits
			with torch.no_grad():
						snake_case				:    Optional[Any]           =	model(lowerCAmelCase__					)
						snake_case				:    int           =	outputs.logits

			print("""Logits:"""						,logits[0, :3]					)
			print("""Predicted class:"""						,model.config.idalabel[logits.argmax(-1					).item()]					)
			snake_case				:    str           =	timm_model(lowerCAmelCase__					)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(lowerCAmelCase__						,outputs.logits						,atol=1E-3					)
			print("""Looks ok!"""					)

			if pytorch_dump_folder_path is not None:
						Path(lowerCAmelCase__					).mkdir(exist_ok=lowerCAmelCase__					)
						print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""					)
						model.save_pretrained(lowerCAmelCase__					)
						processor.save_pretrained(lowerCAmelCase__					)

			if push_to_hub:
						print(f"""Pushing model {model_name} and processor to the hub"""					)
						model.push_to_hub(f"""ybelkada/{model_name}"""					)
						processor.push_to_hub(f"""ybelkada/{model_name}"""					)


if __name__ == "__main__":
							lowerCamelCase	:	str				=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='resnetv2_50x1_bitm',
							    type=str,
							    help='Name of the BiT timm model you\'d like to convert.',
							)
							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 to push the model to the hub.',
							)

							lowerCamelCase	:	Any				=   parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:	List[Any]				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Dict           =	SwinConfig.from_pretrained(
       """microsoft/swin-tiny-patch4-window7-224"""						,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   snake_case				:    List[str]           =	MaskFormerConfig(backbone_config=lowercase					)

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

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

   return config




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   snake_case				:    Union[str, Any]           =	[]
   # 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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   snake_case				:    Tuple           =	dct.pop(lowercase					)
   snake_case				:    int           =	val




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   snake_case				:    List[str]           =	[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[Any]           =	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				:    Optional[Any]           =	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				:    Optional[Any]           =	in_proj_weight[:dim, :]
         snake_case				:    Optional[int]           =	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				:    List[Any]           =	in_proj_weight[
             -dim :, :
         ]
         snake_case				:    Any           =	in_proj_bias[-dim :]
         # fmt: on




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




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




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase = False					)  ->       Dict:
   snake_case				:    List[str]           =	get_maskformer_config(lowercase					)

   # load original state_dict
   with open(lowercase						,"""rb"""					) as f:
      snake_case				:    Optional[Any]           =	pickle.load(lowercase					)
   snake_case				:    Optional[Any]           =	data["""model"""]

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

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

   # update to torch tensors
   for key, value in state_dict.items():
      snake_case				:    List[Any]           =	torch.from_numpy(lowercase					)

   # load 🤗 model
   snake_case				:    int           =	MaskFormerForInstanceSegmentation(lowercase					)
   model.eval()

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

   snake_case     , snake_case				:    Optional[int]           =	model.load_state_dict(lowercase						,strict=lowercase					)
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(lowercase					) == 0, f"""Unexpected keys: {unexpected_keys}"""

   # verify results
   snake_case				:    List[str]           =	prepare_img()
   if "vistas" in model_name:
      snake_case				:    Optional[int]           =	65
   elif "cityscapes" in model_name:
      snake_case				:    int           =	65535
   else:
      snake_case				:    List[str]           =	255
   snake_case				:    List[Any]           =	True if """ade""" in model_name else False
   snake_case				:    Optional[int]           =	MaskFormerImageProcessor(ignore_index=lowercase						,reduce_labels=lowercase					)

   snake_case				:    Tuple           =	image_processor(lowercase						,return_tensors="""pt"""					)

   snake_case				:    str           =	model(**lowercase					)

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

   if model_name == "maskformer-swin-tiny-ade":
      snake_case				:    Union[str, Any]           =	torch.tensor(
          [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,lowercase						,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(lowercase					).mkdir(exist_ok=lowercase					)
      model.save_pretrained(lowercase					)
      image_processor.save_pretrained(lowercase					)

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


if __name__ == "__main__":
       lowerCamelCase	:	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.'
       )

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





