

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 (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					3_8_4
      if "tiny" in model_name:
            __lowerCAmelCase					:						Optional[Any]					=					[3, 3, 9, 3]
            __lowerCAmelCase					:						Tuple					=					[9_6, 1_9_2, 3_8_4, 7_6_8]
      if "small" in model_name:
            __lowerCAmelCase					:						str					=					[3, 3, 2_7, 3]
            __lowerCAmelCase					:						str					=					[9_6, 1_9_2, 3_8_4, 7_6_8]
      if "base" in model_name:
            __lowerCAmelCase					:						str					=					[3, 3, 2_7, 3]
            __lowerCAmelCase					:						List[str]					=					[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
            __lowerCAmelCase					:						Dict					=					5_1_2
      if "large" in model_name:
            __lowerCAmelCase					:						List[str]					=					[3, 3, 2_7, 3]
            __lowerCAmelCase					:						Dict					=					[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
            __lowerCAmelCase					:						Union[str, Any]					=					7_6_8
      if "xlarge" in model_name:
            __lowerCAmelCase					:						Any					=					[3, 3, 2_7, 3]
            __lowerCAmelCase					:						str					=					[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
            __lowerCAmelCase					:						str					=					1_0_2_4

      # set label information
      __lowerCAmelCase					:						Optional[Any]					=					1_5_0
      __lowerCAmelCase					:						int					=					'''huggingface/label-files'''
      __lowerCAmelCase					:						str					=					'''ade20k-id2label.json'''
      __lowerCAmelCase					:						Tuple					=					json.load(open(hf_hub_download(lowercase__   ,	lowercase__   ,	repo_type='''dataset'''						)   ,	'''r'''						)						)
      __lowerCAmelCase					:						str					=					{int(lowercase__						): v for k, v in idalabel.items()}
      __lowerCAmelCase					:						List[Any]					=					{v: k for k, v in idalabel.items()}

      __lowerCAmelCase					:						str					=					ConvNextConfig(
          depths=lowercase__   ,	hidden_sizes=lowercase__   ,	out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						)
      __lowerCAmelCase					:						List[Any]					=					UperNetConfig(
          backbone_config=lowercase__   ,	auxiliary_in_channels=lowercase__   ,	num_labels=lowercase__   ,	idalabel=lowercase__   ,	labelaid=lowercase__   ,	)

      return config







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Optional[Any]					=					[]

      # 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 (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Optional[Any]					=					dct.pop(lowercase__						)
      __lowerCAmelCase					:						List[str]					=					val







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Dict					=					{
          '''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					:						str					=					model_name_to_url[model_name]
      __lowerCAmelCase					:						Tuple					=					torch.hub.load_state_dict_from_url(lowercase__   ,	map_location='''cpu'''						)['''state_dict''']

      __lowerCAmelCase					:						Optional[int]					=					get_upernet_config(lowercase__						)
      __lowerCAmelCase					:						List[Any]					=					UperNetForSemanticSegmentation(lowercase__						)
      model.eval()

      # replace "bn" => "batch_norm"
      for key in state_dict.copy().keys():
            __lowerCAmelCase					:						str					=					state_dict.pop(lowercase__						)
            if "bn" in key:
                  __lowerCAmelCase					:						Tuple					=					key.replace('''bn'''   ,	'''batch_norm'''						)
            __lowerCAmelCase					:						Dict					=					val

      # rename keys
      __lowerCAmelCase					:						Tuple					=					create_rename_keys(lowercase__						)
      for src, dest in rename_keys:
            rename_key(lowercase__   ,	lowercase__   ,	lowercase__						)

      model.load_state_dict(lowercase__						)

      # verify on image
      __lowerCAmelCase					:						Dict					=					'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
      __lowerCAmelCase					:						Optional[int]					=					Image.open(requests.get(lowercase__   ,	stream=lowercase__						).raw						).convert('''RGB'''						)

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

      with torch.no_grad():
            __lowerCAmelCase					:						int					=					model(lowercase__						)

      if model_name == "upernet-convnext-tiny":
            __lowerCAmelCase					:						str					=					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					:						List[str]					=					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					:						Optional[Any]					=					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					:						int					=					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					:						List[Any]					=					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]   ,	lowercase__   ,	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(lowercase__						)
            print(f"""Saving processor to {pytorch_dump_folder_path}"""						)
            processor.save_pretrained(lowercase__						)

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





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class   __lowercase      (unittest.TestCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						List[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 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						Any					=					PNDMScheduler()

       __lowerCAmelCase					:						Dict					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						int					=					np.array([1.0, 1.0, 0.0, 1.0, 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   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					'''google/ddpm-cifar10-32'''

       __lowerCAmelCase					:						Union[str, Any]					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						int					=					PNDMScheduler()

       __lowerCAmelCase					:						Any					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)
       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]					)

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


