


'''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     (  A )		->					List[str]:
							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, 1024]
														lowerCAmelCase__      =			512
							if "large" in model_name:
														lowerCAmelCase__      =			[3, 3, 27, 3]
														lowerCAmelCase__      =			[192, 384, 768, 1536]
														lowerCAmelCase__      =			768
							if "xlarge" in model_name:
														lowerCAmelCase__      =			[3, 3, 27, 3]
														lowerCAmelCase__      =			[256, 512, 1024, 2048]
														lowerCAmelCase__      =			1024

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

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

							return config


def       _snake_case     (  A )		->					str:
							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       _snake_case     (  A				,					A				,					A )		->					Optional[Any]:
							lowerCAmelCase__      =			dct.pop(_snake_case )
							lowerCAmelCase__      =			val


def       _snake_case     (  A				,					A				,					A )		->					Optional[Any]:
							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(_snake_case				,					map_location='''cpu''' )['''state_dict''']

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

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

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

							model.load_state_dict(_snake_case )

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

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

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

							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]				,					_snake_case				,					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(_snake_case )
														print(F"""Saving processor to {pytorch_dump_folder_path}""" )
														processor.save_pretrained(_snake_case )

							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'''






def       _snake_case     (  A )		->					bool:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							if len(A ) == 1:
														return True
							lowerCAmelCase__      =			series[1] - series[0]
							for index in range(len(A ) - 1 ):
														if series[index + 1] - series[index] != common_diff:
																					return False
							return True


def       _snake_case     (  A )		->					float:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							lowerCAmelCase__      =			0
							for val in series:
														answer += val
							return answer / len(A )


if __name__ == "__main__":
		import doctest

		doctest.testmod()