
'''simple docstring'''






import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A			      =      logging.get_logger(__name__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							Tuple=False       ,							UpperCAmelCase__							:							str=False):
				lowerCamelCase					:			List[str]									= 'backbone.' if is_semantic else ''

				lowerCamelCase					:			Union[str, Any]									= []
				for i in range(config.num_hidden_layers):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight'''))
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias'''))
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight'''))
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias'''))
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight'''))
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias'''))
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight'''))
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias'''))
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight'''))
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias'''))

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
				        (F'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
				        (F'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
				        (F'''{prefix}pos_embed''', 'beit.embeddings.position_embeddings'),
				    ])

				if has_lm_head:
								# mask token + layernorm
								rename_keys.extend(
								    [
								        ('mask_token', 'beit.embeddings.mask_token'),
								        ('norm.weight', 'layernorm.weight'),
								        ('norm.bias', 'layernorm.bias'),
								    ])
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
								        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
								        ('head.weight', 'classifier.weight'),
								        ('head.bias', 'classifier.bias'),
								    ])

				return rename_keys





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Optional[Any]=False       ,							UpperCAmelCase__							:							int=False):
				for i in range(config.num_hidden_layers):
								lowerCamelCase					:			Tuple									= 'backbone.' if is_semantic else ''
								# queries, keys and values
								lowerCamelCase					:			Optional[Any]									= state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight''')
								lowerCamelCase					:			List[str]									= state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias''')
								lowerCamelCase					:			Dict									= state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias''')

								lowerCamelCase					:			Union[str, Any]									= in_proj_weight[
								    : config.hidden_size, :
								]
								lowerCamelCase					:			Optional[Any]									= q_bias
								lowerCamelCase					:			str									= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase					:			Any									= in_proj_weight[
								    -config.hidden_size :, :
								]
								lowerCamelCase					:			Union[str, Any]									= v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								lowerCamelCase					:			List[Any]									= state_dict.pop(F'''{prefix}blocks.{i}.gamma_1''')
								lowerCamelCase					:			Any									= state_dict.pop(F'''{prefix}blocks.{i}.gamma_2''')

								lowerCamelCase					:			List[Any]									= gamma_a
								lowerCamelCase					:			Any									= gamma_a





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Optional[int]):
				lowerCamelCase					:			Union[str, Any]									= dct.pop(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= val





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			int									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				lowerCamelCase					:			Optional[int]									= Image.open(requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__).raw)
				return im





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							List[str]=False):
				lowerCamelCase					:			str									= False if 'rvlcdip' in checkpoint_url else True
				lowerCamelCase					:			Optional[int]									= BeitConfig(use_absolute_position_embeddings=UpperCAmelCase__       ,							use_mask_token=UpperCAmelCase__)

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								lowerCamelCase					:			int									= 10_24
								lowerCamelCase					:			List[Any]									= 40_96
								lowerCamelCase					:			List[Any]									= 24
								lowerCamelCase					:			Optional[Any]									= 16

				# labels
				if "rvlcdip" in checkpoint_url:
								lowerCamelCase					:			Dict									= 16
								lowerCamelCase					:			Dict									= 'huggingface/label-files'
								lowerCamelCase					:			List[str]									= 'rvlcdip-id2label.json'
								lowerCamelCase					:			List[str]									= json.load(open(hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__       ,							repo_type='dataset')       ,							'r'))
								lowerCamelCase					:			Union[str, Any]									= {int(UpperCAmelCase__): v for k, v in idalabel.items()}
								lowerCamelCase					:			Union[str, Any]									= idalabel
								lowerCamelCase					:			List[Any]									= {v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				lowerCamelCase					:			str									= torch.hub.load_state_dict_from_url(UpperCAmelCase__       ,							map_location='cpu')['model']

				lowerCamelCase					:			Union[str, Any]									= create_rename_keys(UpperCAmelCase__       ,							has_lm_head=UpperCAmelCase__)
				for src, dest in rename_keys:
								rename_key(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
				read_in_q_k_v(UpperCAmelCase__       ,							UpperCAmelCase__       ,							has_lm_head=UpperCAmelCase__)

				# load HuggingFace model
				lowerCamelCase					:			Optional[int]									= BeitForMaskedImageModeling(UpperCAmelCase__) if has_lm_head else BeitForImageClassification(UpperCAmelCase__)
				model.eval()
				model.load_state_dict(UpperCAmelCase__)

				# Check outputs on an image
				lowerCamelCase					:			str									= BeitImageProcessor(
				    size=config.image_size       ,							resample=PILImageResampling.BILINEAR       ,							do_center_crop=UpperCAmelCase__)
				lowerCamelCase					:			Optional[int]									= prepare_img()

				lowerCamelCase					:			Any									= image_processor(images=UpperCAmelCase__       ,							return_tensors='pt')
				lowerCamelCase					:			Optional[int]									= encoding['pixel_values']

				lowerCamelCase					:			str									= model(UpperCAmelCase__)
				lowerCamelCase					:			Union[str, Any]									= outputs.logits

				# verify logits
				lowerCamelCase					:			Dict									= [1, 16] if 'rvlcdip' in checkpoint_url else [1, 1_96, 81_92]
				assert logits.shape == torch.Size(UpperCAmelCase__), "Shape of logits not as expected"

				Path(UpperCAmelCase__).mkdir(exist_ok=UpperCAmelCase__)
				print(F'''Saving model to {pytorch_dump_folder_path}''')
				model.save_pretrained(UpperCAmelCase__)
				print(F'''Saving image processor to {pytorch_dump_folder_path}''')
				image_processor.save_pretrained(UpperCAmelCase__)

				if push_to_hub:
								if has_lm_head:
												lowerCamelCase					:			Optional[Any]									= 'dit-base' if 'base' in checkpoint_url else 'dit-large'
								else:
												lowerCamelCase					:			Union[str, Any]									= 'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
								image_processor.push_to_hub(
								    repo_path_or_name=Path(UpperCAmelCase__       ,							UpperCAmelCase__)       ,							organization='nielsr'       ,							commit_message='Add image processor'       ,							use_temp_dir=UpperCAmelCase__       ,							)
								model.push_to_hub(
								    repo_path_or_name=Path(UpperCAmelCase__       ,							UpperCAmelCase__)       ,							organization='nielsr'       ,							commit_message='Add model'       ,							use_temp_dir=UpperCAmelCase__       ,							)


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_url',
					    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
					    type=str,
					    help='URL to the original PyTorch checkpoint (.pth file).',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
					)
					parser.add_argument(
					    '--push_to_hub',
					    action='store_true',
					)
					A			      =      parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


'''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()
A			      =      logging.get_logger(__name__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Union[str, Any]									= SwinConfig.from_pretrained(
				    'microsoft/swin-tiny-patch4-window7-224'       ,							out_features=['stage1', 'stage2', 'stage3', 'stage4'])
				lowerCamelCase					:			Dict									= MaskFormerConfig(backbone_config=UpperCAmelCase__)

				lowerCamelCase					:			Dict									= 'huggingface/label-files'
				if "ade20k-full" in model_name:
								# this should be ok
								lowerCamelCase					:			Union[str, Any]									= 8_47
								lowerCamelCase					:			Any									= 'maskformer-ade20k-full-id2label.json'
				elif "ade" in model_name:
								# this should be ok
								lowerCamelCase					:			str									= 1_50
								lowerCamelCase					:			Union[str, Any]									= 'ade20k-id2label.json'
				elif "coco-stuff" in model_name:
								# this should be ok
								lowerCamelCase					:			int									= 1_71
								lowerCamelCase					:			List[Any]									= 'maskformer-coco-stuff-id2label.json'
				elif "coco" in model_name:
								# TODO
								lowerCamelCase					:			int									= 1_33
								lowerCamelCase					:			List[str]									= 'coco-panoptic-id2label.json'
				elif "cityscapes" in model_name:
								# this should be ok
								lowerCamelCase					:			str									= 19
								lowerCamelCase					:			Union[str, Any]									= 'cityscapes-id2label.json'
				elif "vistas" in model_name:
								# this should be ok
								lowerCamelCase					:			Union[str, Any]									= 65
								lowerCamelCase					:			List[Any]									= 'mapillary-vistas-id2label.json'

				lowerCamelCase					:			List[str]									= json.load(open(hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__       ,							repo_type='dataset')       ,							'r'))
				lowerCamelCase					:			str									= {int(UpperCAmelCase__): v for k, v in idalabel.items()}

				return config





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]):
				lowerCamelCase					:			List[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    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Optional[Any]):
				lowerCamelCase					:			List[str]									= dct.pop(UpperCAmelCase__)
				lowerCamelCase					:			Tuple									= val





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





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





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Union[str, Any]									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				lowerCamelCase					:			Tuple									= Image.open(requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__).raw)
				return im





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							bool = False):
				lowerCamelCase					:			Optional[Any]									= get_maskformer_config(UpperCAmelCase__)

				# load original state_dict
				with open(UpperCAmelCase__       ,							'rb') as f:
								lowerCamelCase					:			Tuple									= pickle.load(UpperCAmelCase__)
				lowerCamelCase					:			str									= data['model']

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

				# rename keys
				lowerCamelCase					:			Optional[Any]									= create_rename_keys(UpperCAmelCase__)
				for src, dest in rename_keys:
								rename_key(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
				read_in_swin_q_k_v(UpperCAmelCase__       ,							config.backbone_config)
				read_in_decoder_q_k_v(UpperCAmelCase__       ,							UpperCAmelCase__)

				# update to torch tensors
				for key, value in state_dict.items():
								lowerCamelCase					:			List[str]									= torch.from_numpy(UpperCAmelCase__)

				# load 🤗 model
				lowerCamelCase					:			str									= MaskFormerForInstanceSegmentation(UpperCAmelCase__)
				model.eval()

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

				lowerCamelCase			, lowerCamelCase					:			Optional[Any]									= model.load_state_dict(UpperCAmelCase__       ,							strict=UpperCAmelCase__)
				assert missing_keys == [
				    "model.pixel_level_module.encoder.model.layernorm.weight",
				    "model.pixel_level_module.encoder.model.layernorm.bias",
				]
				assert len(UpperCAmelCase__) == 0, F'''Unexpected keys: {unexpected_keys}'''

				# verify results
				lowerCamelCase					:			Union[str, Any]									= prepare_img()
				if "vistas" in model_name:
								lowerCamelCase					:			Optional[int]									= 65
				elif "cityscapes" in model_name:
								lowerCamelCase					:			Any									= 6_55_35
				else:
								lowerCamelCase					:			List[Any]									= 2_55
				lowerCamelCase					:			str									= True if 'ade' in model_name else False
				lowerCamelCase					:			List[Any]									= MaskFormerImageProcessor(ignore_index=UpperCAmelCase__       ,							reduce_labels=UpperCAmelCase__)

				lowerCamelCase					:			str									= image_processor(UpperCAmelCase__       ,							return_tensors='pt')

				lowerCamelCase					:			Dict									= model(**UpperCAmelCase__)

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

				if model_name == "maskformer-swin-tiny-ade":
								lowerCamelCase					:			List[str]									= 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]       ,							UpperCAmelCase__       ,							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(UpperCAmelCase__).mkdir(exist_ok=UpperCAmelCase__)
								model.save_pretrained(UpperCAmelCase__)
								image_processor.save_pretrained(UpperCAmelCase__)

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


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='maskformer-swin-tiny-ade',
					    type=str,
					    help=('Name of the MaskFormer model you\'d like to convert',),
					)
					parser.add_argument(
					    '--checkpoint_path',
					    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
					    type=str,
					    help='Path to the original state dict (.pth file).',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)

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