






'''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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str]    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    384
   if "tiny" in model_name:
      __a				     =    [3, 3, 9, 3]
      __a				     =    [96, 192, 384, 768]
   if "small" in model_name:
      __a				     =    [3, 3, 27, 3]
      __a				     =    [96, 192, 384, 768]
   if "base" in model_name:
      __a				     =    [3, 3, 27, 3]
      __a				     =    [128, 256, 512, 1024]
      __a				     =    512
   if "large" in model_name:
      __a				     =    [3, 3, 27, 3]
      __a				     =    [192, 384, 768, 1536]
      __a				     =    768
   if "xlarge" in model_name:
      __a				     =    [3, 3, 27, 3]
      __a				     =    [256, 512, 1024, 2048]
      __a				     =    1024

   # set label information
   __a				     =    150
   __a				     =    'huggingface/label-files'
   __a				     =    'ade20k-id2label.json'
   __a				     =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					repo_type='dataset'    ),					'r'    )    )
   __a				     =    {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
   __a				     =    {v: k for k, v in idalabel.items()}

   __a				     =    ConvNextConfig(
       depths=SCREAMING_SNAKE_CASE__,					hidden_sizes=SCREAMING_SNAKE_CASE__,					out_features=['stage1', 'stage2', 'stage3', 'stage4']    )
   __a				     =    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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str]    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    []

   # 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	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	int    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    dct.pop(SCREAMING_SNAKE_CASE__    )
   __a				     =    val



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	str,					SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    {
       '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				     =    model_name_to_url[model_name]
   __a				     =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__,					map_location='cpu'    )['state_dict']

   __a				     =    get_upernet_config(SCREAMING_SNAKE_CASE__    )
   __a				     =    UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__    )
   model.eval()

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

   # rename keys
   __a				     =    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				     =    'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
   __a				     =    Image.open(requests.get(SCREAMING_SNAKE_CASE__,					stream=SCREAMING_SNAKE_CASE__    ).raw    ).convert('RGB'    )

   __a				     =    SegformerImageProcessor()
   __a				     =    processor(SCREAMING_SNAKE_CASE__,					return_tensors='pt'    ).pixel_values

   with torch.no_grad():
      __a				     =    model(SCREAMING_SNAKE_CASE__    )

   if model_name == "upernet-convnext-tiny":
      __a				     =    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":
      __a				     =    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":
      __a				     =    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":
      __a				     =    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":
      __a				     =    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],					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__":
 __UpperCamelCase      :       Tuple      			=      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      :       int      			=      parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''



import warnings

from .generation import TFGenerationMixin
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      # warning at import time
      warnings.warn(
          "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
          "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."      ,      _lowerCAmelCase      ,      )