






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__     :   Union[str, Any]  ):




     '''simple docstring'''






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

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

     __snake_case			:List[Any]          =						ConvNextConfig(
         depths=a_	,hidden_sizes=a_	,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  )
     __snake_case			:List[Any]          =						UperNetConfig(
         backbone_config=a_	,auxiliary_in_channels=a_	,num_labels=a_	,idalabel=a_	,labelaid=a_	,)

     return config






def   UpperCamelCase  (       snake_case__     :   List[Any]  ):




     '''simple docstring'''






     __snake_case			:str          =						[]

     # 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__     :   int	,snake_case__     :   List[str]	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						dct.pop(a_  )
     __snake_case			:List[str]          =						val






def   UpperCamelCase  (       snake_case__     :   Union[str, Any]	,snake_case__     :   Optional[Any]	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Optional[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''',
     }
     __snake_case			:List[Any]          =						model_name_to_url[model_name]
     __snake_case			:Any          =						torch.hub.load_state_dict_from_url(a_	,map_location="""cpu"""  )['''state_dict''']

     __snake_case			:Tuple          =						get_upernet_config(a_  )
     __snake_case			:Optional[int]          =						UperNetForSemanticSegmentation(a_  )
     model.eval()

     # replace "bn" => "batch_norm"
     for key in state_dict.copy().keys():
          __snake_case			:List[Any]          =						state_dict.pop(a_  )
          if "bn" in key:
               __snake_case			:List[Any]          =						key.replace("""bn"""	,"""batch_norm"""  )
          __snake_case			:Optional[int]          =						val

     # rename keys
     __snake_case			:str          =						create_rename_keys(a_  )
     for src, dest in rename_keys:
          rename_key(a_	,a_	,a_  )

     model.load_state_dict(a_  )

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

     __snake_case			:List[str]          =						SegformerImageProcessor()
     __snake_case			:Any          =						processor(a_	,return_tensors="""pt"""  ).pixel_values

     with torch.no_grad():
          __snake_case			:Any          =						model(a_  )

     if model_name == "upernet-convnext-tiny":
          __snake_case			: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":
          __snake_case			:Any          =						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":
          __snake_case			: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":
          __snake_case			:List[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":
          __snake_case			:Dict          =						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__":
     lowerCamelCase__							=     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."""
     )

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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= "visual_bert"



       def __init__(							self			,						a__=3_05_22			,						a__=7_68			,						a__=5_12			,						a__=12			,						a__=12			,						a__=30_72			,						a__="gelu"			,						a__=0.1			,						a__=0.1			,						a__=5_12			,						a__=2			,						a__=0.02			,						a__=1e-12			,						a__=False			,						a__=True			,						a__=1			,						a__=0			,						a__=2			,						**a__			,						) ->      Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__			,						bos_token_id=a__			,						eos_token_id=a__			,						**a__    )

            __snake_case			:Tuple          =						vocab_size
            __snake_case			:Any          =						max_position_embeddings
            __snake_case			:int          =						hidden_size
            __snake_case			:List[str]          =						visual_embedding_dim
            __snake_case			:List[str]          =						num_hidden_layers
            __snake_case			:Tuple          =						num_attention_heads
            __snake_case			:str          =						intermediate_size
            __snake_case			:Union[str, Any]          =						hidden_act
            __snake_case			:Union[str, Any]          =						hidden_dropout_prob
            __snake_case			:str          =						attention_probs_dropout_prob
            __snake_case			:Tuple          =						initializer_range
            __snake_case			:str          =						type_vocab_size
            __snake_case			:Optional[Any]          =						layer_norm_eps
            __snake_case			:Union[str, Any]          =						bypass_transformer
            __snake_case			:Tuple          =						special_visual_initialize


