


class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



       def 				_lowerCAmelCase							(						self				:				str							)					->bool:
          return self.size == 0



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          return False if self.is_empty() else self.array[self.front]



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp









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

   # set label information
   lowerCamelCase_						:   str		= 150
   lowerCamelCase_						:   List[Any]		= """huggingface/label-files"""
   lowerCamelCase_						:   int		= """ade20k-id2label.json"""
   lowerCamelCase_						:   List[Any]		= json.load(open(hf_hub_download(A__						,						A__						,						repo_type="""dataset"""					)						,						"""r"""					)					)
   lowerCamelCase_						:   Optional[Any]		= {int(A__					): v for k, v in idalabel.items()}
   lowerCamelCase_						:   int		= {v: k for k, v in idalabel.items()}

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

   return config


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    Optional[Any]:
   lowerCamelCase_						:   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    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: List[Any]						,						A__						: int					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Dict		= dct.pop(A__					)
   lowerCamelCase_						:   int		= val


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: str						,						A__						: Any					)     ->    Optional[int]:
   lowerCamelCase_						:   int		= {
       """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""",
   }
   lowerCamelCase_						:   List[str]		= model_name_to_url[model_name]
   lowerCamelCase_						:   Dict		= torch.hub.load_state_dict_from_url(A__						,						map_location="""cpu"""					)["""state_dict"""]

   lowerCamelCase_						:   List[Any]		= get_upernet_config(A__					)
   lowerCamelCase_						:   Any		= UperNetForSemanticSegmentation(A__					)
   model.eval()

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

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

   model.load_state_dict(A__					)

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

   lowerCamelCase_						:   List[str]		= SegformerImageProcessor()
   lowerCamelCase_						:   Union[str, Any]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   with torch.no_grad():
      lowerCamelCase_						:   List[str]		= model(A__					)

   if model_name == "upernet-convnext-tiny":
      lowerCamelCase_						:   Dict		= 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":
      lowerCamelCase_						:   Optional[Any]		= 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":
      lowerCamelCase_						:   str		= 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":
      lowerCamelCase_						:   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":
      lowerCamelCase_						:   Any		= 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]						,						A__						,						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(A__					)
      print(f'''Saving processor to {pytorch_dump_folder_path}'''					)
      processor.save_pretrained(A__					)

   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__":
      snake_case__				:	Dict					       =					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.'
      )

      snake_case__				:	List[Any]					       =					parser.parse_args()
      convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





