
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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[int]         =							3_84
				if "tiny" in model_name:
								A__  :	Any         =							[3, 3, 9, 3]
								A__  :	Union[str, Any]         =							[96, 1_92, 3_84, 7_68]
				if "small" in model_name:
								A__  :	int         =							[3, 3, 27, 3]
								A__  :	Dict         =							[96, 1_92, 3_84, 7_68]
				if "base" in model_name:
								A__  :	Tuple         =							[3, 3, 27, 3]
								A__  :	List[Any]         =							[1_28, 2_56, 5_12, 10_24]
								A__  :	Union[str, Any]         =							5_12
				if "large" in model_name:
								A__  :	int         =							[3, 3, 27, 3]
								A__  :	Union[str, Any]         =							[1_92, 3_84, 7_68, 15_36]
								A__  :	Optional[Any]         =							7_68
				if "xlarge" in model_name:
								A__  :	Dict         =							[3, 3, 27, 3]
								A__  :	str         =							[2_56, 5_12, 10_24, 20_48]
								A__  :	List[str]         =							10_24

				# set label information
				A__  :	Any         =							1_50
				A__  :	Optional[int]         =							'''huggingface/label-files'''
				A__  :	Union[str, Any]         =							'''ade20k-id2label.json'''
				A__  :	List[Any]         =							json.load(open(hf_hub_download(__UpperCamelCase							,						__UpperCamelCase							,						repo_type='''dataset'''       )							,						'''r'''       )       )
				A__  :	Any         =							{int(__UpperCamelCase       ): v for k, v in idalabel.items()}
				A__  :	Any         =							{v: k for k, v in idalabel.items()}

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

				return config

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict       ) ->       Dict:

				"""simple docstring"""







				A__  :	Optional[Any]         =							[]

				# 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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						List[Any]							,						__UpperCamelCase :						Optional[int]       ) ->       Dict:

				"""simple docstring"""







				A__  :	Optional[Any]         =							dct.pop(__UpperCamelCase       )
				A__  :	Dict         =							val

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						str							,						__UpperCamelCase :						Optional[Any]       ) ->       Tuple:

				"""simple docstring"""







				A__  :	int         =							{
				    '''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__  :	Dict         =							model_name_to_url[model_name]
				A__  :	str         =							torch.hub.load_state_dict_from_url(__UpperCamelCase							,						map_location='''cpu'''       )['''state_dict''']

				A__  :	Dict         =							get_upernet_config(__UpperCamelCase       )
				A__  :	Union[str, Any]         =							UperNetForSemanticSegmentation(__UpperCamelCase       )
				model.eval()

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

				# rename keys
				A__  :	List[str]         =							create_rename_keys(__UpperCamelCase       )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				model.load_state_dict(__UpperCamelCase       )

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

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

				with torch.no_grad():
								A__  :	Dict         =							model(__UpperCamelCase       )

				if model_name == "upernet-convnext-tiny":
								A__  :	Union[str, Any]         =							torch.tensor(
								    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]       )
				elif model_name == "upernet-convnext-small":
								A__  :	List[str]         =							torch.tensor(
								    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]       )
				elif model_name == "upernet-convnext-base":
								A__  :	Any         =							torch.tensor(
								    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]       )
				elif model_name == "upernet-convnext-large":
								A__  :	Union[str, Any]         =							torch.tensor(
								    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]       )
				elif model_name == "upernet-convnext-xlarge":
								A__  :	List[Any]         =							torch.tensor(
								    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]       )
				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__":
	_SCREAMING_SNAKE_CASE    :      int          =			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    :      Tuple          =			parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				if n == 1 or not isinstance(__UpperCamelCase							,						__UpperCamelCase       ):
								return 0
				elif n == 2:
								return 1
				else:
								A__  :	Any         =							[0, 1]
								for i in range(2							,						n + 1       ):
												sequence.append(sequence[i - 1] + sequence[i - 2]       )

								return sequence[n]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				A__  :	Dict         =							0
				A__  :	Optional[int]         =							2

				while digits < n:
								index += 1
								A__  :	Dict         =							len(str(fibonacci(__UpperCamelCase       )       )       )

				return index

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int = 10_00       ) ->       int:

				"""simple docstring"""







				return fibonacci_digits_index(__UpperCamelCase       )


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))