



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_    ) ->       Optional[Any]:
						UpperCAmelCase         =     384
						if "tiny" in model_name:
												UpperCAmelCase         =     [3, 3, 9, 3]
												UpperCAmelCase         =     [96, 192, 384, 768]
						if "small" in model_name:
												UpperCAmelCase         =     [3, 3, 27, 3]
												UpperCAmelCase         =     [96, 192, 384, 768]
						if "base" in model_name:
												UpperCAmelCase         =     [3, 3, 27, 3]
												UpperCAmelCase         =     [128, 256, 512, 1_024]
												UpperCAmelCase         =     512
						if "large" in model_name:
												UpperCAmelCase         =     [3, 3, 27, 3]
												UpperCAmelCase         =     [192, 384, 768, 1_536]
												UpperCAmelCase         =     768
						if "xlarge" in model_name:
												UpperCAmelCase         =     [3, 3, 27, 3]
												UpperCAmelCase         =     [256, 512, 1_024, 2_048]
												UpperCAmelCase         =     1_024

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

						UpperCAmelCase         =     ConvNextConfig(
						    depths=_A    ,   hidden_sizes=_A    ,   out_features=["stage1", "stage2", "stage3", "stage4"]    )
						UpperCAmelCase         =     UperNetConfig(
						    backbone_config=_A    ,   auxiliary_in_channels=_A    ,   num_labels=_A    ,   idalabel=_A    ,   labelaid=_A    ,   )

						return config

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[Any]:
						UpperCAmelCase         =     []

						# 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_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple:
						UpperCAmelCase         =     dct.pop(_A    )
						UpperCAmelCase         =     val

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     {
						    "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",
						}
						UpperCAmelCase         =     model_name_to_url[model_name]
						UpperCAmelCase         =     torch.hub.load_state_dict_from_url(_A    ,   map_location="cpu"    )["state_dict"]

						UpperCAmelCase         =     get_upernet_config(_A    )
						UpperCAmelCase         =     UperNetForSemanticSegmentation(_A    )
						model.eval()

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

						# rename keys
						UpperCAmelCase         =     create_rename_keys(_A    )
						for src, dest in rename_keys:
												rename_key(_A    ,   _A    ,   _A    )

						model.load_state_dict(_A    )

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

						UpperCAmelCase         =     SegformerImageProcessor()
						UpperCAmelCase         =     processor(_A    ,   return_tensors="pt"    ).pixel_values

						with torch.no_grad():
												UpperCAmelCase         =     model(_A    )

						if model_name == "upernet-convnext-tiny":
												UpperCAmelCase         =     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":
												UpperCAmelCase         =     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":
												UpperCAmelCase         =     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":
												UpperCAmelCase         =     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":
												UpperCAmelCase         =     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__":
		__lowerCamelCase     :							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."
		)

		__lowerCamelCase     :							Union[str, Any]  							=  parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from ..utils import DummyObject, requires_backends





class  __magic_name__   (							metaclass=A__						):
		lowercase      :       int          =['''note_seq''']






		def __init__(						self      :     List[Any]				,		*UpperCamelCase__      :     Tuple				,		**UpperCamelCase__      :     Dict      )				->					int:
								'''simple docstring'''







								requires_backends(self				,		["note_seq"]      )






		@classmethod
		def SCREAMING_SNAKE_CASE_			(						cls      :     List[str]				,		*UpperCamelCase__      :     Dict				,		**UpperCamelCase__      :     str      )				->					Optional[int]:
								'''simple docstring'''







								requires_backends(cls				,		["note_seq"]      )







		@classmethod
		def SCREAMING_SNAKE_CASE_			(						cls      :     List[Any]				,		*UpperCamelCase__      :     Optional[int]				,		**UpperCamelCase__      :     Optional[Any]      )				->					Optional[int]:
								'''simple docstring'''







								requires_backends(cls				,		["note_seq"]      )



