
"""simple docstring"""






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 						_snake_case       (	lowercase__		:  List[Any]		)  ->				str:


			'''simple docstring'''





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

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

			lowerCAmelCase_    :Any			     =  ConvNextConfig(
			    depths=UpperCAmelCase_      ,     hidden_sizes=UpperCAmelCase_      ,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		)
			lowerCAmelCase_    :Any			     =  UperNetConfig(
			    backbone_config=UpperCAmelCase_      ,     auxiliary_in_channels=UpperCAmelCase_      ,     num_labels=UpperCAmelCase_      ,     idalabel=UpperCAmelCase_      ,     labelaid=UpperCAmelCase_      ,     )

			return config






def 						_snake_case       (	lowercase__		:  Tuple		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :Dict			     =  []

			# 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 						_snake_case       (	lowercase__		:  Optional[Any]      ,     lowercase__		:  int      ,     lowercase__		:  int		)  ->				Optional[int]:


			'''simple docstring'''





			lowerCAmelCase_    :List[str]			     =  dct.pop(UpperCAmelCase_		)
			lowerCAmelCase_    :Any			     =  val






def 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  List[Any]		)  ->				Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  {
			    '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',
			}
			lowerCAmelCase_    :List[Any]			     =  model_name_to_url[model_name]
			lowerCAmelCase_    :Any			     =  torch.hub.load_state_dict_from_url(UpperCAmelCase_      ,     map_location="""cpu"""		)['state_dict']

			lowerCAmelCase_    :Tuple			     =  get_upernet_config(UpperCAmelCase_		)
			lowerCAmelCase_    :Optional[int]			     =  UperNetForSemanticSegmentation(UpperCAmelCase_		)
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						lowerCAmelCase_    :str			     =  state_dict.pop(UpperCAmelCase_		)
						if "bn" in key:
									lowerCAmelCase_    :Union[str, Any]			     =  key.replace("""bn"""      ,     """batch_norm"""		)
						lowerCAmelCase_    :Union[str, Any]			     =  val

			# rename keys
			lowerCAmelCase_    :Optional[int]			     =  create_rename_keys(UpperCAmelCase_		)
			for src, dest in rename_keys:
						rename_key(UpperCAmelCase_      ,     UpperCAmelCase_      ,     UpperCAmelCase_		)

			model.load_state_dict(UpperCAmelCase_		)

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

			lowerCAmelCase_    :Tuple			     =  SegformerImageProcessor()
			lowerCAmelCase_    :Tuple			     =  processor(UpperCAmelCase_      ,     return_tensors="""pt"""		).pixel_values

			with torch.no_grad():
						lowerCAmelCase_    :Dict			     =  model(UpperCAmelCase_		)

			if model_name == "upernet-convnext-tiny":
						lowerCAmelCase_    :Optional[int]			     =  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":
						lowerCAmelCase_    :List[str]			     =  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":
						lowerCAmelCase_    :Dict			     =  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":
						lowerCAmelCase_    :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":
						lowerCAmelCase_    :Optional[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]      ,     UpperCAmelCase_      ,     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(UpperCAmelCase_		)
						print(f"""Saving processor to {pytorch_dump_folder_path}"""		)
						processor.save_pretrained(UpperCAmelCase_		)

			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__":
				__UpperCAmelCase       =  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.'
				)

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










'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         if dst_width < 0 or dst_height < 0:
              raise ValueError('Destination width/height should be > 0'	)

         __lowerCamelCase   :	Dict								=		img
         __lowerCamelCase   :	Any								=		img.shape[1]
         __lowerCamelCase   :	Optional[int]								=		img.shape[0]
         __lowerCamelCase   :	Dict								=		dst_width
         __lowerCamelCase   :	str								=		dst_height

         __lowerCamelCase   :	Dict								=		self.src_w / self.dst_w
         __lowerCamelCase   :	List[Any]								=		self.src_h / self.dst_h

         __lowerCamelCase   :	Optional[int]								=		(
             np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta	) * 2_55
         )







    def       lowercase_				(							self	)				->     List[Any]:
         for i in range(self.dst_h	):
              for j in range(self.dst_w	):
                   __lowerCamelCase   :	Union[str, Any]								=		self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_x * x	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_y * y	)


if __name__ == "__main__":
      A__						,					A__		:					Optional[Any]												=  800, 600
      A__		:					List[str]												=  imread("""image_data/lena.jpg""", 1)
      A__		:					List[Any]												=  NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
      )
      waitKey(0)
      destroyAllWindows()






