


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

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

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

				return config







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =[]

				# 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(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =dct.pop(a__)
				_SCREAMING_SNAKE_CASE			  =val







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  ={
				    '''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''',
				}
				_SCREAMING_SNAKE_CASE			  =model_name_to_url[model_name]
				_SCREAMING_SNAKE_CASE			  =torch.hub.load_state_dict_from_url(a__	,map_location='''cpu''')['''state_dict''']

				_SCREAMING_SNAKE_CASE			  =get_upernet_config(a__)
				_SCREAMING_SNAKE_CASE			  =UperNetForSemanticSegmentation(a__)
				model.eval()

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

				# rename keys
				_SCREAMING_SNAKE_CASE			  =create_rename_keys(a__)
				for src, dest in rename_keys:
								rename_key(a__	,a__	,a__)

				model.load_state_dict(a__)

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

				_SCREAMING_SNAKE_CASE			  =SegformerImageProcessor()
				_SCREAMING_SNAKE_CASE			  =processor(a__	,return_tensors='''pt''').pixel_values

				with torch.no_grad():
								_SCREAMING_SNAKE_CASE			  =model(a__)

				if model_name == "upernet-convnext-tiny":
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =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]	,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__":
							snake_case_						:			Optional[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.'''
							)

							snake_case_						:			Dict													=							parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case_						:			Optional[Any]													=							'''scheduler_config.json'''

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						1
						UpperCAmelCase             =						2
						UpperCAmelCase             =						3
						UpperCAmelCase             =						4
						UpperCAmelCase             =						5
						UpperCAmelCase             =						6
						UpperCAmelCase             =						7
						UpperCAmelCase             =						8
						UpperCAmelCase             =						9
						UpperCAmelCase             =						10
						UpperCAmelCase             =						11
						UpperCAmelCase             =						12
						UpperCAmelCase             =						13
						UpperCAmelCase             =						14

@dataclass
class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						42

class 						A__							:
						UpperCAmelCase             =						SCHEDULER_CONFIG_NAME
						UpperCAmelCase             =						[]
						UpperCAmelCase             =						True




						@classmethod
						def 							__UpperCamelCase				(       cls					:      List[str]				,    _a					:      Dict[str, Any] = None				,    _a					:      Optional[str] = None				,    _a					:      Optional[Any]=False				,    **_a					:      Dict				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =cls.load_config(
										    pretrained_model_name_or_path=_a				,    subfolder=_a				,    return_unused_kwargs=_a				,    return_commit_hash=_a				,    **_a				,    )
										return cls.from_config(_a				,    return_unused_kwargs=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Union[str, os.PathLike]				,    _a					:      bool = False				,    **_a					:      int  )							->     Dict:


										"""simple docstring"""





										self.save_config(save_directory=_a				,    push_to_hub=_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     List[Any]:


										"""simple docstring"""





										return self._get_compatibles()




						@classmethod
						def 							__UpperCamelCase				(       cls					:      List[Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =list(set([cls.__name__] + cls._compatibles  )  )
										_SCREAMING_SNAKE_CASE			  =importlib.import_module(__name__.split('''.'''  )[0]  )
										_SCREAMING_SNAKE_CASE			  =[
										    getattr(_a				,    _a  ) for c in compatible_classes_str if hasattr(_a				,    _a  )
										]
										return compatible_classes