"""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       (     _SCREAMING_SNAKE_CASE       )     ->int:
  a__:					Optional[int]      							=     384
  if "tiny" in model_name:
    a__:					Any      							=     [3, 3, 9, 3]
    a__:					int      							=     [96, 192, 384, 768]
  if "small" in model_name:
    a__:					List[Any]      							=     [3, 3, 27, 3]
    a__:					List[Any]      							=     [96, 192, 384, 768]
  if "base" in model_name:
    a__:					Optional[int]      							=     [3, 3, 27, 3]
    a__:					List[str]      							=     [128, 256, 512, 1024]
    a__:					str      							=     512
  if "large" in model_name:
    a__:					Dict      							=     [3, 3, 27, 3]
    a__:					int      							=     [192, 384, 768, 1536]
    a__:					Optional[int]      							=     768
  if "xlarge" in model_name:
    a__:					Dict      							=     [3, 3, 27, 3]
    a__:					Dict      							=     [256, 512, 1024, 2048]
    a__:					Union[str, Any]      							=     1024

  # set label information
  a__:					Dict      							=     150
  a__:					Any      							=     'huggingface/label-files'
  a__:					Dict      							=     'ade20k-id2label.json'
  a__:					Tuple      							=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					repo_type='dataset'       )		,					'r'       )       )
  a__:					Tuple      							=     {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
  a__:					Optional[int]      							=     {v: k for k, v in idalabel.items()}

  a__:					Tuple      							=     ConvNextConfig(
      depths=_SCREAMING_SNAKE_CASE		,					hidden_sizes=_SCREAMING_SNAKE_CASE		,					out_features=['stage1', 'stage2', 'stage3', 'stage4']       )
  a__:					Union[str, Any]      							=     UperNetConfig(
      backbone_config=_SCREAMING_SNAKE_CASE		,					auxiliary_in_channels=_SCREAMING_SNAKE_CASE		,					num_labels=_SCREAMING_SNAKE_CASE		,					idalabel=_SCREAMING_SNAKE_CASE		,					labelaid=_SCREAMING_SNAKE_CASE		,					)

  return config







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
  a__:					Tuple      							=     []

  # 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       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  a__:					Union[str, Any]      							=     dct.pop(_SCREAMING_SNAKE_CASE       )
  a__:					Union[str, Any]      							=     val







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
  a__:					Optional[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',
  }
  a__:					Union[str, Any]      							=     model_name_to_url[model_name]
  a__:					Tuple      							=     torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE		,					map_location='cpu'       )['state_dict']

  a__:					List[Any]      							=     get_upernet_config(_SCREAMING_SNAKE_CASE       )
  a__:					Union[str, Any]      							=     UperNetForSemanticSegmentation(_SCREAMING_SNAKE_CASE       )
  model.eval()

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

  # rename keys
  a__:					List[Any]      							=     create_rename_keys(_SCREAMING_SNAKE_CASE       )
  for src, dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

  a__:					Tuple      							=     SegformerImageProcessor()
  a__:					List[str]      							=     processor(_SCREAMING_SNAKE_CASE		,					return_tensors='pt'       ).pixel_values

  with torch.no_grad():
    a__:					Dict      							=     model(_SCREAMING_SNAKE_CASE       )

  if model_name == "upernet-convnext-tiny":
    a__:					Optional[int]      							=     torch.tensor(
        [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]       )
  elif model_name == "upernet-convnext-small":
    a__:					str      							=     torch.tensor(
        [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]       )
  elif model_name == "upernet-convnext-base":
    a__:					str      							=     torch.tensor(
        [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]       )
  elif model_name == "upernet-convnext-large":
    a__:					Any      							=     torch.tensor(
        [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]       )
  elif model_name == "upernet-convnext-xlarge":
    a__:					Union[str, Any]      							=     torch.tensor(
        [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]       )
  print('Logits:'		,					outputs.logits[0, 0, :3, :3]       )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]		,					_SCREAMING_SNAKE_CASE		,					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(_SCREAMING_SNAKE_CASE       )
    print(F'Saving processor to {pytorch_dump_folder_path}'       )
    processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

  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__":
 lowercase__ 	=     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.'
 )

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



"""simple docstring"""
import unittest

from knapsack import knapsack as k




class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					List[Any]      							=     0
        a__:					Dict      							=     [0]
        a__:					int      							=     [0]
        a__:					Optional[Any]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)

        a__:					str      							=     [60]
        a__:					Dict      							=     [10]
        a__:					List[str]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					int      							=     3
        a__:					str      							=     [1, 2, 3]
        a__:					Dict      							=     [3, 2, 1]
        a__:					Optional[int]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      5)



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Any      							=     50
        a__:					Optional[int]      							=     [60, 1_00, 1_20]
        a__:					str      							=     [10, 20, 30]
        a__:					int      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      2_20)


if __name__ == "__main__":
 unittest.main()


