






"""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__		):
			UpperCAmelCase_  :				str      =      3_84
			if "tiny" in model_name:
						UpperCAmelCase_  :				int      =      [3, 3, 9, 3]
						UpperCAmelCase_  :				Optional[int]      =      [96, 1_92, 3_84, 7_68]
			if "small" in model_name:
						UpperCAmelCase_  :				Optional[int]      =      [3, 3, 27, 3]
						UpperCAmelCase_  :				Dict      =      [96, 1_92, 3_84, 7_68]
			if "base" in model_name:
						UpperCAmelCase_  :				Tuple      =      [3, 3, 27, 3]
						UpperCAmelCase_  :				int      =      [1_28, 2_56, 5_12, 10_24]
						UpperCAmelCase_  :				Dict      =      5_12
			if "large" in model_name:
						UpperCAmelCase_  :				Tuple      =      [3, 3, 27, 3]
						UpperCAmelCase_  :				Optional[Any]      =      [1_92, 3_84, 7_68, 15_36]
						UpperCAmelCase_  :				Dict      =      7_68
			if "xlarge" in model_name:
						UpperCAmelCase_  :				Tuple      =      [3, 3, 27, 3]
						UpperCAmelCase_  :				List[Any]      =      [2_56, 5_12, 10_24, 20_48]
						UpperCAmelCase_  :				List[Any]      =      10_24

			# set label information
			UpperCAmelCase_  :				int      =      1_50
			UpperCAmelCase_  :				str      =      "huggingface/label-files"
			UpperCAmelCase_  :				Tuple      =      "ade20k-id2label.json"
			UpperCAmelCase_  :				int      =      json.load(open(hf_hub_download(A__			,A__			,repo_type="dataset"		)			,"r"		)		)
			UpperCAmelCase_  :				List[str]      =      {int(A__		): v for k, v in idalabel.items()}
			UpperCAmelCase_  :				Optional[int]      =      {v: k for k, v in idalabel.items()}

			UpperCAmelCase_  :				Tuple      =      ConvNextConfig(
			    depths=A__			,hidden_sizes=A__			,out_features=["stage1", "stage2", "stage3", "stage4"]		)
			UpperCAmelCase_  :				Any      =      UperNetConfig(
			    backbone_config=A__			,auxiliary_in_channels=A__			,num_labels=A__			,idalabel=A__			,labelaid=A__			,)

			return config

def        snake_case   (     A__		):
			UpperCAmelCase_  :				str      =      []

			# 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__		):
			UpperCAmelCase_  :				Dict      =      dct.pop(A__		)
			UpperCAmelCase_  :				str      =      val

def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				int      =      {
			    "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_  :				Tuple      =      model_name_to_url[model_name]
			UpperCAmelCase_  :				List[Any]      =      torch.hub.load_state_dict_from_url(A__			,map_location="cpu"		)["state_dict"]

			UpperCAmelCase_  :				List[Any]      =      get_upernet_config(A__		)
			UpperCAmelCase_  :				List[Any]      =      UperNetForSemanticSegmentation(A__		)
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						UpperCAmelCase_  :				Optional[int]      =      state_dict.pop(A__		)
						if "bn" in key:
									UpperCAmelCase_  :				Dict      =      key.replace("bn"			,"batch_norm"		)
						UpperCAmelCase_  :				Dict      =      val

			# rename keys
			UpperCAmelCase_  :				Optional[int]      =      create_rename_keys(A__		)
			for src, dest in rename_keys:
						rename_key(A__			,A__			,A__		)

			model.load_state_dict(A__		)

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

			UpperCAmelCase_  :				List[str]      =      SegformerImageProcessor()
			UpperCAmelCase_  :				str      =      processor(A__			,return_tensors="pt"		).pixel_values

			with torch.no_grad():
						UpperCAmelCase_  :				Optional[int]      =      model(A__		)

			if model_name == "upernet-convnext-tiny":
						UpperCAmelCase_  :				List[Any]      =      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_  :				Dict      =      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_  :				List[Any]      =      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_  :				int      =      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_  :				List[Any]      =      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_       			=   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_       			=   parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4



