



"""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__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				384
      if "tiny" in model_name:
            UpperCAmelCase_     					=				[3, 3, 9, 3]
            UpperCAmelCase_     					=				[96, 192, 384, 768]
      if "small" in model_name:
            UpperCAmelCase_     					=				[3, 3, 27, 3]
            UpperCAmelCase_     					=				[96, 192, 384, 768]
      if "base" in model_name:
            UpperCAmelCase_     					=				[3, 3, 27, 3]
            UpperCAmelCase_     					=				[128, 256, 512, 1024]
            UpperCAmelCase_     					=				512
      if "large" in model_name:
            UpperCAmelCase_     					=				[3, 3, 27, 3]
            UpperCAmelCase_     					=				[192, 384, 768, 1536]
            UpperCAmelCase_     					=				768
      if "xlarge" in model_name:
            UpperCAmelCase_     					=				[3, 3, 27, 3]
            UpperCAmelCase_     					=				[256, 512, 1024, 2048]
            UpperCAmelCase_     					=				1024

      # set label information
      UpperCAmelCase_     					=				150
      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   a__      ( lowerCAmelCase__ ):
      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   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				dct.pop(_A )
      UpperCAmelCase_     					=				val
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      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__":
  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 math import factorial, radians







def __UpperCamelCase( _A       :					float	,      _A       :					int = 18	,      _A       :					int = 10     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

   # Converting from degrees to radians
   UpperCAmelCase__				:					Dict 					=	radians(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	angle_in_radians
   UpperCAmelCase__				:					List[str] 					=	3
   UpperCAmelCase__				:					int 					=	-1

   for _ in range(_A     ):
      result += (b * (angle_in_radians**a)) / factorial(_A     )

      UpperCAmelCase__				:					List[str] 					=	-b  # One positive term and the next will be negative and so on...
      a += 2  # Increased by 2 for every term.

   return round(_A	,      _A     )


if __name__ == "__main__":
  __import__('doctest').testmod()




