'''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( _A       :					List[str]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Dict 					=	3_84
   if "tiny" in model_name:
      UpperCAmelCase__				:					Dict 					=	[3, 3, 9, 3]
      UpperCAmelCase__				:					str 					=	[96, 1_92, 3_84, 7_68]
   if "small" in model_name:
      UpperCAmelCase__				:					List[str] 					=	[3, 3, 27, 3]
      UpperCAmelCase__				:					List[str] 					=	[96, 1_92, 3_84, 7_68]
   if "base" in model_name:
      UpperCAmelCase__				:					List[str] 					=	[3, 3, 27, 3]
      UpperCAmelCase__				:					int 					=	[1_28, 2_56, 5_12, 10_24]
      UpperCAmelCase__				:					str 					=	5_12
   if "large" in model_name:
      UpperCAmelCase__				:					Dict 					=	[3, 3, 27, 3]
      UpperCAmelCase__				:					Tuple 					=	[1_92, 3_84, 7_68, 15_36]
      UpperCAmelCase__				:					List[Any] 					=	7_68
   if "xlarge" in model_name:
      UpperCAmelCase__				:					Tuple 					=	[3, 3, 27, 3]
      UpperCAmelCase__				:					Any 					=	[2_56, 5_12, 10_24, 20_48]
      UpperCAmelCase__				:					int 					=	10_24

   # set label information
   UpperCAmelCase__				:					str 					=	1_50
   UpperCAmelCase__				:					Union[str, Any] 					=	'''huggingface/label-files'''
   UpperCAmelCase__				:					Any 					=	'''ade20k-id2label.json'''
   UpperCAmelCase__				:					Dict 					=	json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''     )	,      '''r'''     )     )
   UpperCAmelCase__				:					Any 					=	{int(_A     ): v for k, v in idalabel.items()}
   UpperCAmelCase__				:					Any 					=	{v: k for k, v in idalabel.items()}

   UpperCAmelCase__				:					int 					=	ConvNextConfig(
       depths=_A	,      hidden_sizes=_A	,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     )
   UpperCAmelCase__				:					List[Any] 					=	UperNetConfig(
       backbone_config=_A	,      auxiliary_in_channels=_A	,      num_labels=_A	,      idalabel=_A	,      labelaid=_A	,      )

   return config







def __UpperCamelCase( _A       :					Union[str, Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[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 __UpperCamelCase( _A       :					Optional[int]	,      _A       :					int	,      _A       :					Dict     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	dct.pop(_A     )
   UpperCAmelCase__				:					Any 					=	val







def __UpperCamelCase( _A       :					List[Any]	,      _A       :					Union[str, Any]	,      _A       :					Dict     ):
   '''simple docstring'''
   UpperCAmelCase__				:					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''',
   }
   UpperCAmelCase__				:					Any 					=	model_name_to_url[model_name]
   UpperCAmelCase__				:					Tuple 					=	torch.hub.load_state_dict_from_url(_A	,      map_location='''cpu'''     )['''state_dict''']

   UpperCAmelCase__				:					str 					=	get_upernet_config(_A     )
   UpperCAmelCase__				:					Any 					=	UperNetForSemanticSegmentation(_A     )
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      UpperCAmelCase__				:					Optional[int] 					=	state_dict.pop(_A     )
      if "bn" in key:
         UpperCAmelCase__				:					Tuple 					=	key.replace('''bn'''	,      '''batch_norm'''     )
      UpperCAmelCase__				:					int 					=	val

   # rename keys
   UpperCAmelCase__				:					Dict 					=	create_rename_keys(_A     )
   for src, dest in rename_keys:
      rename_key(_A	,      _A	,      _A     )

   model.load_state_dict(_A     )

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

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

   with torch.no_grad():
      UpperCAmelCase__				:					int 					=	model(_A     )

   if model_name == "upernet-convnext-tiny":
      UpperCAmelCase__				:					Optional[Any] 					=	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":
      UpperCAmelCase__				:					int 					=	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":
      UpperCAmelCase__				:					List[Any] 					=	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":
      UpperCAmelCase__				:					Optional[Any] 					=	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":
      UpperCAmelCase__				:					Union[str, Any] 					=	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]	,      _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__":
  UpperCamelCase__  :						Optional[int]					=      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__  :						List[Any]					=      parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate


@dataclass(frozen=lowerCAmelCase )
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= field(default='''question-answering-extractive'''		,metadata={'''include_in_asdict_even_if_is_default''': True} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features(
          {
              '''answers''': Sequence(
                  {
                      '''text''': Value('''string''' ),
                      '''answer_start''': Value('''int32''' ),
                  } )
          } )
      UpperCAmelCase_   :   str   				= "question"
      UpperCAmelCase_   :   str   				= "context"
      UpperCAmelCase_   :   str   				= "answers"




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict[str, str]:



         '''simple docstring'''



         return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}




