




"""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   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		List[str]:
 A__	=    3_84
 if "tiny" in model_name:
  A__	=    [3, 3, 9, 3]
  A__	=    [96, 1_92, 3_84, 7_68]
 if "small" in model_name:
  A__	=    [3, 3, 27, 3]
  A__	=    [96, 1_92, 3_84, 7_68]
 if "base" in model_name:
  A__	=    [3, 3, 27, 3]
  A__	=    [1_28, 2_56, 5_12, 10_24]
  A__	=    5_12
 if "large" in model_name:
  A__	=    [3, 3, 27, 3]
  A__	=    [1_92, 3_84, 7_68, 15_36]
  A__	=    7_68
 if "xlarge" in model_name:
  A__	=    [3, 3, 27, 3]
  A__	=    [2_56, 5_12, 10_24, 20_48]
  A__	=    10_24

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

 A__	=    ConvNextConfig(
     depths=lowercase_      ,			hidden_sizes=lowercase_      ,			out_features=["stage1", "stage2", "stage3", "stage4"]						)
 A__	=    UperNetConfig(
     backbone_config=lowercase_      ,			auxiliary_in_channels=lowercase_      ,			num_labels=lowercase_      ,			idalabel=lowercase_      ,			labelaid=lowercase_      ,			)

 return config







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[str]:
 A__	=    dct.pop(lowercase_						)
 A__	=    val







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 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(lowercase_      ,			map_location="cpu"						)["state_dict"]

 A__	=    get_upernet_config(lowercase_						)
 A__	=    UperNetForSemanticSegmentation(lowercase_						)
 model.eval()

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

 # rename keys
 A__	=    create_rename_keys(lowercase_						)
 for src, dest in rename_keys:
  rename_key(lowercase_      ,			lowercase_      ,			lowercase_						)

 model.load_state_dict(lowercase_						)

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

 A__	=    SegformerImageProcessor()
 A__	=    processor(lowercase_      ,			return_tensors="pt"						).pixel_values

 with torch.no_grad():
  A__	=    model(lowercase_						)

 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]      ,			lowercase_      ,			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(lowercase_						)
  print(f"""Saving processor to {pytorch_dump_folder_path}"""						)
  processor.save_pretrained(lowercase_						)

 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__":
      SCREAMING_SNAKE_CASE    =	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."
      )

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











"""simple docstring"""



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."     ,			snake_case_     ,			)
      A__	=    kwargs.pop("feature_extractor"				)

     A__	=    image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     if text is None and visual_prompt is None and images is None:
      raise ValueError("You have to specify either text, visual prompt or images."				)

     if text is not None and visual_prompt is not None:
      raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None and images is not None:
      A__	=    {
          "pixel_values": image_features.pixel_values,
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     elif text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,			snake_case_     ,			)
     return self.image_processor_class




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,			snake_case_     ,			)
     return self.image_processor





