
"""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  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  384
			if "tiny" in model_name:
						lowerCamelCase_					=  [3, 3, 9, 3]
						lowerCamelCase_					=  [96, 192, 384, 768]
			if "small" in model_name:
						lowerCamelCase_					=  [3, 3, 27, 3]
						lowerCamelCase_					=  [96, 192, 384, 768]
			if "base" in model_name:
						lowerCamelCase_					=  [3, 3, 27, 3]
						lowerCamelCase_					=  [128, 256, 512, 1_024]
						lowerCamelCase_					=  512
			if "large" in model_name:
						lowerCamelCase_					=  [3, 3, 27, 3]
						lowerCamelCase_					=  [192, 384, 768, 1_536]
						lowerCamelCase_					=  768
			if "xlarge" in model_name:
						lowerCamelCase_					=  [3, 3, 27, 3]
						lowerCamelCase_					=  [256, 512, 1_024, 2_048]
						lowerCamelCase_					=  1_024

			# set label information
			lowerCamelCase_					=  150
			lowerCamelCase_					=  '''huggingface/label-files'''
			lowerCamelCase_					=  '''ade20k-id2label.json'''
			lowerCamelCase_					=  json.load(open(hf_hub_download(__a	,__a	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(__a						): v for k, v in idalabel.items()}
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

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

			return config






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  []

			# 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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  dct.pop(__a						)
			lowerCamelCase_					=  val






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  {
			    '''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_					=  model_name_to_url[model_name]
			lowerCamelCase_					=  torch.hub.load_state_dict_from_url(__a	,map_location='''cpu'''						)['''state_dict''']

			lowerCamelCase_					=  get_upernet_config(__a						)
			lowerCamelCase_					=  UperNetForSemanticSegmentation(__a						)
			model.eval()

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

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

			model.load_state_dict(__a						)

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

			lowerCamelCase_					=  SegformerImageProcessor()
			lowerCamelCase_					=  processor(__a	,return_tensors='''pt'''						).pixel_values

			with torch.no_grad():
						lowerCamelCase_					=  model(__a						)

			if model_name == "upernet-convnext-tiny":
						lowerCamelCase_					=  torch.tensor(
						    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]						)
			elif model_name == "upernet-convnext-small":
						lowerCamelCase_					=  torch.tensor(
						    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]						)
			elif model_name == "upernet-convnext-base":
						lowerCamelCase_					=  torch.tensor(
						    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]						)
			elif model_name == "upernet-convnext-large":
						lowerCamelCase_					=  torch.tensor(
						    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]						)
			elif model_name == "upernet-convnext-xlarge":
						lowerCamelCase_					=  torch.tensor(
						    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]						)
			print('''Logits:'''	,outputs.logits[0, 0, :3, :3]						)
			assert torch.allclose(outputs.logits[0, 0, :3, :3]	,__a	,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(__a						)
						print(f"Saving processor to {pytorch_dump_folder_path}"						)
						processor.save_pretrained(__a						)

			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__":
						A_    							=					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."""
						)

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










def   __lowerCamelCase   (				__a  :int = 4_0_0_0_0_0_0       )						->							int:






		"""simple docstring"""

		A__     =  []
		A__ ,     A__     =  0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(__a       )
				A__ ,     A__     =  b, a + b
		return sum(__a       )


if __name__ == "__main__":
					print(F'''{solution() = }''')



