




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 							_SCREAMING_SNAKE_CASE   (		snake_case							)    ->					List[str]:
						_UpperCAmelCase	     =      3_8_4
						if "tiny" in model_name:
												_UpperCAmelCase	     =      [3, 3, 9, 3]
												_UpperCAmelCase	     =      [9_6, 1_9_2, 3_8_4, 7_6_8]
						if "small" in model_name:
												_UpperCAmelCase	     =      [3, 3, 2_7, 3]
												_UpperCAmelCase	     =      [9_6, 1_9_2, 3_8_4, 7_6_8]
						if "base" in model_name:
												_UpperCAmelCase	     =      [3, 3, 2_7, 3]
												_UpperCAmelCase	     =      [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
												_UpperCAmelCase	     =      5_1_2
						if "large" in model_name:
												_UpperCAmelCase	     =      [3, 3, 2_7, 3]
												_UpperCAmelCase	     =      [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
												_UpperCAmelCase	     =      7_6_8
						if "xlarge" in model_name:
												_UpperCAmelCase	     =      [3, 3, 2_7, 3]
												_UpperCAmelCase	     =      [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
												_UpperCAmelCase	     =      1_0_2_4

						# set label information
						_UpperCAmelCase	     =      1_5_0
						_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 							_SCREAMING_SNAKE_CASE   (		snake_case							)    ->					Dict:
						_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 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case      ,  snake_case							)    ->					int:
						_UpperCAmelCase	     =      dct.pop(a__							)
						_UpperCAmelCase	     =      val




def 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case      ,  snake_case							)    ->					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__":
				a    				=      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    				=      parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)





