




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

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

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

      return config

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> int:
      SCREAMING_SNAKE_CASE_							=			[]

      # 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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	List[str]							)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE_							=			dct.pop(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			val

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	Any							)					-> List[Any]:
      SCREAMING_SNAKE_CASE_							=			{
          '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_							=			model_name_to_url[model_name]
      SCREAMING_SNAKE_CASE_							=			torch.hub.load_state_dict_from_url(__UpperCAmelCase			,			map_location='cpu'							)['state_dict']

      SCREAMING_SNAKE_CASE_							=			get_upernet_config(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			UperNetForSemanticSegmentation(__UpperCAmelCase							)
      model.eval()

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

      # rename keys
      SCREAMING_SNAKE_CASE_							=			create_rename_keys(__UpperCAmelCase							)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase							)

      model.load_state_dict(__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_							=			SegformerImageProcessor()
      SCREAMING_SNAKE_CASE_							=			processor(__UpperCAmelCase			,			return_tensors='pt'							).pixel_values

      with torch.no_grad():
            SCREAMING_SNAKE_CASE_							=			model(__UpperCAmelCase							)

      if model_name == "upernet-convnext-tiny":
            SCREAMING_SNAKE_CASE_							=			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":
            SCREAMING_SNAKE_CASE_							=			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":
            SCREAMING_SNAKE_CASE_							=			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":
            SCREAMING_SNAKE_CASE_							=			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":
            SCREAMING_SNAKE_CASE_							=			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]			,			__UpperCAmelCase			,			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(__UpperCAmelCase							)
            print(f"Saving processor to {pytorch_dump_folder_path}"							)
            processor.save_pretrained(__UpperCAmelCase							)

      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__ :   Optional[int]					=							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__ :   Optional[Any]					=							parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			stack[-1]
            stack.append(arr[index]							)
      return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowerCamelCase__ :   List[str]					=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )