



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

   # set label information
   UpperCamelCase    :  Optional[int]							=	150
   UpperCamelCase    :  Optional[int]							=	"huggingface/label-files"
   UpperCamelCase    :  List[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    :  Dict							=	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 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     Tuple:
   UpperCamelCase    :  Dict							=	[]

   # 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	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Dict       ,     snake_case__       :    List[str]	)	->     Tuple:
   UpperCamelCase    :  List[Any]							=	dct.pop(snake_case__	)
   UpperCamelCase    :  int							=	val
def 		UpperCamelCase	(   snake_case__       :    Union[str, Any]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[Any]	)	->     int:
   UpperCamelCase    :  Any							=	{
       "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    :  List[Any]							=	model_name_to_url[model_name]
   UpperCamelCase    :  Tuple							=	torch.hub.load_state_dict_from_url(snake_case__       ,     map_location='cpu'	)["state_dict"]

   UpperCamelCase    :  Any							=	get_upernet_config(snake_case__	)
   UpperCamelCase    :  str							=	UperNetForSemanticSegmentation(snake_case__	)
   model.eval()

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

   # rename keys
   UpperCamelCase    :  int							=	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    :  Dict							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	).convert('RGB'	)

   UpperCamelCase    :  Optional[Any]							=	SegformerImageProcessor()
   UpperCamelCase    :  Tuple							=	processor(snake_case__       ,     return_tensors='pt'	).pixel_values

   with torch.no_grad():
      UpperCamelCase    :  List[str]							=	model(snake_case__	)

   if model_name == "upernet-convnext-tiny":
      UpperCamelCase    :  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    :  Dict							=	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    :  Tuple							=	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    :  Union[str, Any]							=	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    :  Optional[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]       ,     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__":
    __UpperCAmelCase          =       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          =       parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    if index == r:
        for j in range(lowercase ):
            print(data[j]						,							end=" " )
        print(" " )
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    snake_case    :      Union[str, Any]      					=    arr[i]
    combination_util(lowercase						,							lowercase						,							lowercase						,							index + 1						,							lowercase						,							i + 1 )
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							i + 1 )
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(lowercase						,							lowercase						,							lowercase						,							0						,							lowercase						,							0 )


if __name__ == "__main__":
  # Driver code to check the function above
  __snake_case   			=    [10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu





