



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     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      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(_lowerCamelCase   ,   _lowerCamelCase   ,   repo_type="dataset" )   ,   "r" ) )
      lowerCamelCase								=		{int(_lowerCamelCase ): v for k, v in idalabel.items()}
      lowerCamelCase								=		{v: k for k, v in idalabel.items()}

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

      return config

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		dct.pop(_lowerCamelCase )
      lowerCamelCase								=		val

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      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(_lowerCamelCase   ,   map_location="cpu" )["state_dict"]

      lowerCamelCase								=		get_upernet_config(_lowerCamelCase )
      lowerCamelCase								=		UperNetForSemanticSegmentation(_lowerCamelCase )
      model.eval()

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

      # rename keys
      lowerCamelCase								=		create_rename_keys(_lowerCamelCase )
      for src, dest in rename_keys:
            rename_key(_lowerCamelCase   ,   _lowerCamelCase   ,   _lowerCamelCase )

      model.load_state_dict(_lowerCamelCase )

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

      lowerCamelCase								=		SegformerImageProcessor()
      lowerCamelCase								=		processor(_lowerCamelCase   ,   return_tensors="pt" ).pixel_values

      with torch.no_grad():
            lowerCamelCase								=		model(_lowerCamelCase )

      if model_name == "upernet-convnext-tiny":
            lowerCamelCase								=		torch.tensor(
                [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] )
      elif model_name == "upernet-convnext-small":
            lowerCamelCase								=		torch.tensor(
                [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]] )
      elif model_name == "upernet-convnext-base":
            lowerCamelCase								=		torch.tensor(
                [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]] )
      elif model_name == "upernet-convnext-large":
            lowerCamelCase								=		torch.tensor(
                [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]] )
      elif model_name == "upernet-convnext-xlarge":
            lowerCamelCase								=		torch.tensor(
                [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]] )
      print("Logits:"   ,   outputs.logits[0, 0, :3, :3] )
      assert torch.allclose(outputs.logits[0, 0, :3, :3]   ,   _lowerCamelCase   ,   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(_lowerCamelCase )
            print(F"""Saving processor to {pytorch_dump_folder_path}""" )
            processor.save_pretrained(_lowerCamelCase )

      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__":
     a_						:     Any	     =		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.'
     )

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




a_						:     List[str]	     =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

a_						:     Any	     =		[{'type': 'code', 'content': INSTALL_CONTENT}]
a_						:     str	     =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}