






"""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       _A    (__a							)				-> List[Any]:
    """simple docstring"""






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

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

    SCREAMING_SNAKE_CASE_   :						str							=	ConvNextConfig(
        depths=__A					,       hidden_sizes=__A					,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
    SCREAMING_SNAKE_CASE_   :						int							=	UperNetConfig(
        backbone_config=__A					,       auxiliary_in_channels=__A					,       num_labels=__A					,       idalabel=__A					,       labelaid=__A					,       )

    return config



def       _A    (__a							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	[]

    # 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       _A    (__a					,       __a					,       __a							)				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	dct.pop(__A							)
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	val



def       _A    (__a					,       __a					,       __a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	{
        """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_   :						Tuple							=	model_name_to_url[model_name]
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.hub.load_state_dict_from_url(__A					,       map_location='''cpu'''							)["""state_dict"""]

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	get_upernet_config(__A							)
    SCREAMING_SNAKE_CASE_   :						Any							=	UperNetForSemanticSegmentation(__A							)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	state_dict.pop(__A							)
        if "bn" in key:
            SCREAMING_SNAKE_CASE_   :						str							=	key.replace('''bn'''					,       '''batch_norm'''							)
        SCREAMING_SNAKE_CASE_   :						str							=	val

    # rename keys
    SCREAMING_SNAKE_CASE_   :						str							=	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_   :						Union[str, Any]							=	"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
    SCREAMING_SNAKE_CASE_   :						Any							=	Image.open(requests.get(__A					,       stream=__A							).raw							).convert('''RGB'''							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	SegformerImageProcessor()
    SCREAMING_SNAKE_CASE_   :						List[str]							=	processor(__A					,       return_tensors='''pt'''							).pixel_values

    with torch.no_grad():
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(__A							)

    if model_name == "upernet-convnext-tiny":
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.tensor(
            [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]							)
    elif model_name == "upernet-convnext-small":
        SCREAMING_SNAKE_CASE_   :						Any							=	torch.tensor(
            [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]							)
    elif model_name == "upernet-convnext-base":
        SCREAMING_SNAKE_CASE_   :						Dict							=	torch.tensor(
            [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]							)
    elif model_name == "upernet-convnext-large":
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.tensor(
            [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]							)
    elif model_name == "upernet-convnext-xlarge":
        SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.tensor(
            [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]							)
    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__":
  UpperCAmelCase_     :				str         =				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."""
  )

  UpperCAmelCase_     :				Tuple         =				parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


