






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

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

  UpperCamelCase_:   Optional[int]        =							ConvNextConfig(
      depths=snake_case__      ,       hidden_sizes=snake_case__      ,       out_features=['stage1', 'stage2', 'stage3', 'stage4']    )
  UpperCamelCase_:   Optional[int]        =							UperNetConfig(
      backbone_config=snake_case__      ,       auxiliary_in_channels=snake_case__      ,       num_labels=snake_case__      ,       idalabel=snake_case__      ,       labelaid=snake_case__      ,       )

  return config

def     snake_case   (UpperCAmelCase__    )			->							Optional[Any]:
  UpperCamelCase_:   Union[str, 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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   Optional[Any]        =							dct.pop(snake_case__    )
  UpperCamelCase_:   str        =							val

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:
  UpperCamelCase_:   List[str]        =							{
      '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_:   Dict        =							model_name_to_url[model_name]
  UpperCamelCase_:   str        =							torch.hub.load_state_dict_from_url(snake_case__      ,       map_location='cpu'    )['state_dict']

  UpperCamelCase_:   List[str]        =							get_upernet_config(snake_case__    )
  UpperCamelCase_:   Tuple        =							UperNetForSemanticSegmentation(snake_case__    )
  model.eval()

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

  # rename keys
  UpperCamelCase_:   List[str]        =							create_rename_keys(snake_case__    )
  for src, dest in rename_keys:
    rename_key(snake_case__      ,       snake_case__      ,       snake_case__    )

  model.load_state_dict(snake_case__    )

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

  UpperCamelCase_:   Optional[int]        =							SegformerImageProcessor()
  UpperCamelCase_:   Union[str, Any]        =							processor(snake_case__      ,       return_tensors='pt'    ).pixel_values

  with torch.no_grad():
    UpperCamelCase_:   Tuple        =							model(snake_case__    )

  if model_name == "upernet-convnext-tiny":
    UpperCamelCase_:   List[str]        =							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_:   Optional[int]        =							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_:   List[Any]        =							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_:   List[str]        =							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_:   Tuple        =							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]      ,       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(snake_case__    )
    print(F'''Saving processor to {pytorch_dump_folder_path}'''    )
    processor.save_pretrained(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__":
   A_						:						Any						      =					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.'
   )

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

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''mobilenet_v1'''


						def __init__(			self  :   Optional[int]	,A_  :   Optional[int]=3	,A_  :   Any=224	,A_  :   List[Any]=1.0	,A_  :   Union[str, Any]=8	,A_  :   Union[str, Any]="relu6"	,A_  :   Optional[Any]=True	,A_  :   List[str]=0.9_99	,A_  :   int=0.02	,A_  :   int=0.0_01	,**A_  :   Union[str, Any]	,)  -> Dict:
							super().__init__(**A_   )

							if depth_multiplier <= 0:
								raise ValueError('depth_multiplier must be greater than zero.'   )

							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = hidden_act
							A     = tf_padding
							A     = classifier_dropout_prob
							A     = initializer_range
							A     = layer_norm_eps







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     version.parse('''1.11'''     )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict([('pixel_values', {0: 'batch'})]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							if self.task == "image-classification":
								return OrderedDict([('logits', {0: 'batch'})]   )
							else:
								return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> float:
							return 1e-4