



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  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    384
							if "tiny" in model_name:
														A      						=    [3, 3, 9, 3]
														A      						=    [96, 192, 384, 768]
							if "small" in model_name:
														A      						=    [3, 3, 27, 3]
														A      						=    [96, 192, 384, 768]
							if "base" in model_name:
														A      						=    [3, 3, 27, 3]
														A      						=    [128, 256, 512, 1024]
														A      						=    512
							if "large" in model_name:
														A      						=    [3, 3, 27, 3]
														A      						=    [192, 384, 768, 1536]
														A      						=    768
							if "xlarge" in model_name:
														A      						=    [3, 3, 27, 3]
														A      						=    [256, 512, 1024, 2048]
														A      						=    1024

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

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

							return config






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							List[Any]:

							'''simple docstring'''






							A      						=    []

							# 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  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    dct.pop(lowerCAmelCase__				)
							A      						=    val






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	Optional[int]				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    {
							    '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      						=    model_name_to_url[model_name]
							A      						=    torch.hub.load_state_dict_from_url(lowerCAmelCase__			,	map_location='cpu'				)['state_dict']

							A      						=    get_upernet_config(lowerCAmelCase__				)
							A      						=    UperNetForSemanticSegmentation(lowerCAmelCase__				)
							model.eval()

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

							# rename keys
							A      						=    create_rename_keys(lowerCAmelCase__				)
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							model.load_state_dict(lowerCAmelCase__				)

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

							A      						=    SegformerImageProcessor()
							A      						=    processor(lowerCAmelCase__			,	return_tensors='pt'				).pixel_values

							with torch.no_grad():
														A      						=    model(lowerCAmelCase__				)

							if model_name == "upernet-convnext-tiny":
														A      						=    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      						=    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      						=    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      						=    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      						=    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__":
				__snake_case     :str			       =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.'
				)

				__snake_case     :Dict			       =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 sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


							_a      	=							XGBRegressor(verbosity=0					,		random_state=42 )
							xgb.fit(UpperCamelCase					,		UpperCamelCase )
							# Predict target for test data
							_a      	=							xgb.predict(UpperCamelCase )
							_a      	=							predictions.reshape(len(UpperCamelCase )					,		1 )
							return predictions
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							xgboost(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# Error printing
							print(f'Mean Absolute Error : {mean_absolute_error(UpperCamelCase					,		UpperCamelCase )}' )
							print(f'Mean Square Error  : {mean_squared_error(UpperCamelCase					,		UpperCamelCase )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

