






import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple				):
			__a						:  List[str]									=     3_8_4
			if "tiny" in model_name:
						__a						:  Optional[int]									=     [3, 3, 9, 3]
						__a						:  Tuple									=     [9_6, 1_9_2, 3_8_4, 7_6_8]
			if "small" in model_name:
						__a						:  Any									=     [3, 3, 2_7, 3]
						__a						:  List[Any]									=     [9_6, 1_9_2, 3_8_4, 7_6_8]
			if "base" in model_name:
						__a						:  str									=     [3, 3, 2_7, 3]
						__a						:  Dict									=     [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
						__a						:  Optional[int]									=     5_1_2
			if "large" in model_name:
						__a						:  Tuple									=     [3, 3, 2_7, 3]
						__a						:  str									=     [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
						__a						:  Dict									=     7_6_8
			if "xlarge" in model_name:
						__a						:  int									=     [3, 3, 2_7, 3]
						__a						:  Optional[Any]									=     [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
						__a						:  Optional[int]									=     1_0_2_4

			# set label information
			__a						:  Tuple									=     1_5_0
			__a						:  Optional[int]									=     'huggingface/label-files'
			__a						:  Any									=     'ade20k-id2label.json'
			__a						:  Dict									=     json.load(open(hf_hub_download(lowerCamelCase_   ,  lowerCamelCase_   ,  repo_type='dataset'				)   ,  'r'				)				)
			__a						:  Union[str, Any]									=     {int(lowerCamelCase_				): v for k, v in idalabel.items()}
			__a						:  Any									=     {v: k for k, v in idalabel.items()}

			__a						:  Optional[int]									=     ConvNextConfig(
			    depths=lowerCamelCase_   ,  hidden_sizes=lowerCamelCase_   ,  out_features=['stage1', 'stage2', 'stage3', 'stage4']				)
			__a						:  Optional[int]									=     UperNetConfig(
			    backbone_config=lowerCamelCase_   ,  auxiliary_in_channels=lowerCamelCase_   ,  num_labels=lowerCamelCase_   ,  idalabel=lowerCamelCase_   ,  labelaid=lowerCamelCase_   ,  )

			return config

def        UpperCAmelCase__		(						lowerCamelCase_       :       str				):
			__a						:  int									=     []

			# fmt: off
			# stem
			rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight')				)
			rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias')				)
			rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight')				)
			rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.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.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')				)
									rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')				)
						if i > 0:
									rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')				)
									rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')				)
									rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')				)
									rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')				)

						rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')				)
						rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')				)

			# decode head
			rename_keys.extend(
			    [
			        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
			        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
			        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
			        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
			    ]				)
			# fmt: on

			return rename_keys

def        UpperCAmelCase__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Dict				):
			__a						:  List[str]									=     dct.pop(lowerCamelCase_				)
			__a						:  Optional[Any]									=     val

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Tuple				):
			__a						:  str									=     {
			    'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
			    'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
			    'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
			    'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
			    'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
			}
			__a						:  Any									=     model_name_to_url[model_name]
			__a						:  List[Any]									=     torch.hub.load_state_dict_from_url(lowerCamelCase_   ,  map_location='cpu'				)['state_dict']

			__a						:  Optional[int]									=     get_upernet_config(lowerCamelCase_				)
			__a						:  Any									=     UperNetForSemanticSegmentation(lowerCamelCase_				)
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						__a						:  Dict									=     state_dict.pop(lowerCamelCase_				)
						if "bn" in key:
									__a						:  Dict									=     key.replace('bn'   ,  'batch_norm'				)
						__a						:  int									=     val

			# rename keys
			__a						:  Dict									=     create_rename_keys(lowerCamelCase_				)
			for src, dest in rename_keys:
						rename_key(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			model.load_state_dict(lowerCamelCase_				)

			# verify on image
			__a						:  str									=     'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
			__a						:  Optional[Any]									=     Image.open(requests.get(lowerCamelCase_   ,  stream=lowerCamelCase_				).raw				).convert('RGB'				)

			__a						:  Union[str, Any]									=     SegformerImageProcessor()
			__a						:  str									=     processor(lowerCamelCase_   ,  return_tensors='pt'				).pixel_values

			with torch.no_grad():
						__a						:  Union[str, Any]									=     model(lowerCamelCase_				)

			if model_name == "upernet-convnext-tiny":
						__a						:  Optional[Any]									=     torch.tensor(
						    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]				)
			elif model_name == "upernet-convnext-small":
						__a						:  Tuple									=     torch.tensor(
						    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]				)
			elif model_name == "upernet-convnext-base":
						__a						:  Optional[int]									=     torch.tensor(
						    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]				)
			elif model_name == "upernet-convnext-large":
						__a						:  str									=     torch.tensor(
						    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]				)
			elif model_name == "upernet-convnext-xlarge":
						__a						:  int									=     torch.tensor(
						    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]				)
			print('Logits:'   ,  outputs.logits[0, 0, :3, :3]				)
			assert torch.allclose(outputs.logits[0, 0, :3, :3]   ,  lowerCamelCase_   ,  atol=1e-4				)
			print('Looks ok!'				)

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
						model.save_pretrained(lowerCamelCase_				)
						print(f'''Saving processor to {pytorch_dump_folder_path}'''				)
						processor.save_pretrained(lowerCamelCase_				)

			if push_to_hub:
						print(f'''Pushing model and processor for {model_name} to hub'''				)
						model.push_to_hub(f'''openmmlab/{model_name}'''				)
						processor.push_to_hub(f'''openmmlab/{model_name}'''				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default='''upernet-convnext-tiny''',
							    type=str,
							    choices=[F"upernet-convnext-{size}" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
							    help='''Name of the ConvNext UperNet 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 or not to push the converted model to the 🤗 hub.'''
							)

							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''



import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  str						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	old_name

     if "patch_embed" in old_name:
          __snake_case       ,     __snake_case       ,     __snake_case            =	old_name.split('''.'''						)

          if layer == "0":
               __snake_case            =	old_name.replace('''0''' ,						'''convolution1'''						)
          elif layer == "1":
               __snake_case            =	old_name.replace('''1''' ,						'''batchnorm_before'''						)
          elif layer == "3":
               __snake_case            =	old_name.replace('''3''' ,						'''convolution2'''						)
          else:
               __snake_case            =	old_name.replace('''4''' ,						'''batchnorm_after'''						)

     if "network" in old_name and re.search(R'''\d\.\d''' ,						_lowerCamelCase						):
          __snake_case            =	R'''\b\d{2}\b'''
          if bool(re.search(_lowerCamelCase ,						_lowerCamelCase						)						):
               __snake_case            =	re.search(R'''\d\.\d\d.''' ,						_lowerCamelCase						).group()
          else:
               __snake_case            =	re.search(R'''\d\.\d.''' ,						_lowerCamelCase						).group()
          if int(match[0]						) < 6:
               __snake_case            =	old_name.replace(_lowerCamelCase ,						''''''						)
               __snake_case            =	trimmed_name.replace('''network''' ,						match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]						)
               __snake_case            =	'''intermediate_stages.''' + trimmed_name
          else:
               __snake_case            =	old_name.replace(_lowerCamelCase ,						''''''						)
               if int(match[2]						) < num_meta4D_last_stage:
                    __snake_case            =	trimmed_name.replace('''network''' ,						'''meta4D_layers.blocks.''' + match[2]						)
               else:
                    __snake_case            =	str(int(match[2]						) - num_meta4D_last_stage						)
                    __snake_case            =	trimmed_name.replace('''network''' ,						'''meta3D_layers.blocks.''' + layer_index						)
                    if "norm1" in old_name:
                         __snake_case            =	trimmed_name.replace('''norm1''' ,						'''layernorm1'''						)
                    elif "norm2" in old_name:
                         __snake_case            =	trimmed_name.replace('''norm2''' ,						'''layernorm2'''						)
                    elif "fc1" in old_name:
                         __snake_case            =	trimmed_name.replace('''fc1''' ,						'''linear_in'''						)
                    elif "fc2" in old_name:
                         __snake_case            =	trimmed_name.replace('''fc2''' ,						'''linear_out'''						)

               __snake_case            =	'''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(R'''.\d.''' ,						_lowerCamelCase						):
          __snake_case            =	old_name.replace('''network''' ,						'''intermediate_stages'''						)

     if "fc" in new_name:
          __snake_case            =	new_name.replace('''fc''' ,						'''convolution'''						)
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          __snake_case            =	new_name.replace('''norm1''' ,						'''batchnorm_before'''						)
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          __snake_case            =	new_name.replace('''norm2''' ,						'''batchnorm_after'''						)
     if "proj" in new_name:
          __snake_case            =	new_name.replace('''proj''' ,						'''projection'''						)
     if "dist_head" in new_name:
          __snake_case            =	new_name.replace('''dist_head''' ,						'''distillation_classifier'''						)
     elif "head" in new_name:
          __snake_case            =	new_name.replace('''head''' ,						'''classifier'''						)
     elif "patch_embed" in new_name:
          __snake_case            =	'''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          __snake_case            =	new_name.replace('''norm''' ,						'''layernorm'''						)
          __snake_case            =	'''efficientformer.''' + new_name
     else:
          __snake_case            =	'''efficientformer.encoder.''' + new_name

     return new_name







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     for key in checkpoint.copy().keys():
          __snake_case            =	checkpoint.pop(_lowerCamelCase						)
          __snake_case            =	val

     return checkpoint







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __snake_case            =	Image.open(requests.get(_lowerCamelCase ,						stream=_lowerCamelCase						).raw						)

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  bool						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	torch.load(_lowerCamelCase ,						map_location='''cpu'''						)['''model''']
     __snake_case            =	EfficientFormerConfig.from_json_file(_lowerCamelCase						)
     __snake_case            =	EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase						)
     __snake_case            =	'''_'''.join(checkpoint_path.split('''/'''						)[-1].split('''.'''						)[0].split('''_'''						)[:-1]						)

     __snake_case            =	config.depths[-1] - config.num_metaad_blocks + 1
     __snake_case            =	convert_torch_checkpoint(_lowerCamelCase ,						_lowerCamelCase						)

     model.load_state_dict(_lowerCamelCase						)
     model.eval()

     __snake_case            =	{
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     __snake_case            =	prepare_img()
     __snake_case            =	2_56
     __snake_case            =	2_24
     __snake_case            =	EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size} ,						crop_size={'''height''': crop_size, '''width''': crop_size} ,						resample=pillow_resamplings['''bicubic'''] ,						)
     __snake_case            =	processor(images=_lowerCamelCase ,						return_tensors='''pt'''						).pixel_values

     # original processing pipeline
     __snake_case            =	Compose(
         [
             Resize(_lowerCamelCase ,						interpolation=pillow_resamplings['''bicubic''']						),
             CenterCrop(_lowerCamelCase						),
             ToTensor(),
             Normalize(_lowerCamelCase ,						_lowerCamelCase						),
         ]						)
     __snake_case            =	image_transforms(_lowerCamelCase						).unsqueeze(0						)

     assert torch.allclose(_lowerCamelCase ,						_lowerCamelCase						)

     __snake_case            =	model(_lowerCamelCase						)
     __snake_case            =	outputs.logits

     __snake_case            =	(1, 10_00)

     if "l1" in model_name:
          __snake_case            =	torch.Tensor(
              [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]						)
          assert torch.allclose(logits[0, :10] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          __snake_case            =	torch.Tensor(
              [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]						)
          assert torch.allclose(logits[0, :10] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          __snake_case            =	torch.Tensor(
              [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]						)
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''						)

     # Save Checkpoints
     Path(_lowerCamelCase						).mkdir(exist_ok=_lowerCamelCase						)
     model.save_pretrained(_lowerCamelCase						)
     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)
     processor.save_pretrained(_lowerCamelCase						)
     print(f'''Processor successfuly saved at {pytorch_dump_path}'''						)

     if push_to_hub:
          print('''Pushing model to the hub...'''						)

          model.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}''' ,						commit_message='''Add model''' ,						use_temp_dir=_lowerCamelCase ,						)
          processor.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}''' ,						commit_message='''Add image processor''' ,						use_temp_dir=_lowerCamelCase ,						)


if __name__ == "__main__":
     UpperCAmelCase_							:	Optional[int]	     =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_model_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to EfficientFormer pytorch checkpoint.''',
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The json file for EfficientFormer model config.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )

     parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
     parser.add_argument(
         '''--no-push_to_hub''',
         dest='''push_to_hub''',
         action='''store_false''',
         help='''Do not push model and image processor to the hub''',
     )
     parser.set_defaults(push_to_hub=True)

     UpperCAmelCase_							:	Union[str, Any]	     =		parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )






