




'''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_       (UpperCamelCase			:    List[Any] ):





							'''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(UpperCamelCase					,		UpperCamelCase					,		repo_type='''dataset''' )					,		'''r''' ) )
							_a      	=							{int(UpperCamelCase ): v for k, v in idalabel.items()}
							_a      	=							{v: k for k, v in idalabel.items()}

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

							return config
def 						snake_case_       (UpperCamelCase			:    str ):





							'''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 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							dct.pop(UpperCamelCase )
							_a      	=							val
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    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(UpperCamelCase					,		map_location='''cpu''' )['''state_dict''']

							_a      	=							get_upernet_config(UpperCamelCase )
							_a      	=							UperNetForSemanticSegmentation(UpperCamelCase )
							model.eval()

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

							# rename keys
							_a      	=							create_rename_keys(UpperCamelCase )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )

							model.load_state_dict(UpperCamelCase )

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

							_a      	=							SegformerImageProcessor()
							_a      	=							processor(UpperCamelCase					,		return_tensors='''pt''' ).pixel_values

							with torch.no_grad():
														_a      	=							model(UpperCamelCase )

							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]					,		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__":
				_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 requests
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{'''Content-Type''': '''application/json'''}
							_a      	=							requests.post(UpperCamelCase					,		json={'''text''': message_body}					,		headers=UpperCamelCase )
							if response.status_code != 200:
														_a      	=							(
														    '''Request to slack returned an error '''
														    f'{response.status_code}, the response is:\n{response.text}'
														)
														raise ValueError(UpperCamelCase )


if __name__ == "__main__":
				# Set the slack url to the one provided by Slack when you create the webhook at
				# https://my.slack.com/services/new/incoming-webhook/
				send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')

