'''simple docstring'''





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def     __lowerCAmelCase						(       a_		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       384
     SCREAMING_SNAKE_CASE					:			int    	=       7
     if "tiny" in model_name:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       96
          SCREAMING_SNAKE_CASE					:			int    	=       (2, 2, 6, 2)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (3, 6, 12, 24)
     elif "small" in model_name:
          SCREAMING_SNAKE_CASE					:			str    	=       96
          SCREAMING_SNAKE_CASE					:			List[str]    	=       (2, 2, 18, 2)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       (3, 6, 12, 24)
     elif "base" in model_name:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       128
          SCREAMING_SNAKE_CASE					:			Tuple    	=       (2, 2, 18, 2)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       (4, 8, 16, 32)
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       12
          SCREAMING_SNAKE_CASE					:			int    	=       512
     elif "large" in model_name:
          SCREAMING_SNAKE_CASE					:			Any    	=       192
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (2, 2, 18, 2)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       (6, 12, 24, 48)
          SCREAMING_SNAKE_CASE					:			Any    	=       12
          SCREAMING_SNAKE_CASE					:			str    	=       768

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

     SCREAMING_SNAKE_CASE					:			List[str]    	=       SwinConfig(
         embed_dim=a_       ,      depths=a_       ,      num_heads=a_       ,      window_size=a_       ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,      )
     SCREAMING_SNAKE_CASE					:			Tuple    	=       UperNetConfig(
         backbone_config=a_       ,      auxiliary_in_channels=a_       ,      num_labels=a_       ,      idalabel=a_       ,      labelaid=a_       ,      )

     return config


def     __lowerCAmelCase						(       a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       []

     # fmt: off
     # stem
     rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')		)
     rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')		)
     rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')		)
     rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.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}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")		)

          if i < 3:
               rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.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_       ,      a_       ,      a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       dct.pop(a_		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       val


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
     for i in range(len(backbone_config.depths		)		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       num_features[i]
          for j in range(backbone_config.depths[i]		):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""		)
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""		)
               # next, add query, keys and values (in that order) to the state dict
               SCREAMING_SNAKE_CASE					:			str    	=       in_proj_weight[:dim, :]
               SCREAMING_SNAKE_CASE					:			Any    	=       in_proj_bias[: dim]
               SCREAMING_SNAKE_CASE					:			Any    	=       in_proj_weight[
                   dim : dim * 2, :
               ]
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       in_proj_bias[
                   dim : dim * 2
               ]
               SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_weight[
                   -dim :, :
               ]
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       in_proj_bias[-dim :]
               # fmt: on


def     __lowerCAmelCase						(       a_		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       x.shape
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       x.reshape(a_       ,      4       ,      in_channel // 4		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       x[:, [0, 2, 1, 3], :].transpose(1       ,      2		).reshape(a_       ,      a_		)
     return x


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x.shape
     SCREAMING_SNAKE_CASE					:			Dict    	=       x.reshape(a_       ,      in_channel // 4       ,      4		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x[:, :, [0, 2, 1, 3]].transpose(1       ,      2		).reshape(a_       ,      a_		)

     return x


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       x.shape[0]
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x.reshape(4       ,      in_channel // 4		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       x[[0, 2, 1, 3], :].transpose(0       ,      1		).reshape(a_		)
     return x


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x.shape[0]
     SCREAMING_SNAKE_CASE					:			str    	=       x.reshape(in_channel // 4       ,      4		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       x[:, [0, 2, 1, 3]].transpose(0       ,      1		).reshape(a_		)
     return x


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       {
         'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
         'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
         'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
         'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
     }
     SCREAMING_SNAKE_CASE					:			str    	=       model_name_to_url[model_name]
     SCREAMING_SNAKE_CASE					:			str    	=       torch.hub.load_state_dict_from_url(a_       ,      map_location='cpu'       ,      file_name=a_		)[
         'state_dict'
     ]

     for name, param in state_dict.items():
          print(a_       ,      param.shape		)

     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       get_upernet_config(a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       UperNetForSemanticSegmentation(a_		)
     model.eval()

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

     # rename keys
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       create_rename_keys(a_		)
     for src, dest in rename_keys:
          rename_key(a_       ,      a_       ,      a_		)
     read_in_q_k_v(a_       ,      config.backbone_config		)

     # fix downsample parameters
     for key, value in state_dict.items():
          if "downsample" in key:
               if "reduction" in key:
                    SCREAMING_SNAKE_CASE					:			List[str]    	=       reverse_correct_unfold_reduction_order(a_		)
               if "norm" in key:
                    SCREAMING_SNAKE_CASE					:			Tuple    	=       reverse_correct_unfold_norm_order(a_		)

     model.load_state_dict(a_		)

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

     SCREAMING_SNAKE_CASE					:			Tuple    	=       SegformerImageProcessor()
     SCREAMING_SNAKE_CASE					:			int    	=       processor(a_       ,      return_tensors='pt'		).pixel_values

     with torch.no_grad():
          SCREAMING_SNAKE_CASE					:			str    	=       model(a_		)
          SCREAMING_SNAKE_CASE					:			int    	=       outputs.logits

     print(logits.shape		)
     print('First values of logits:'       ,      logits[0, 0, :3, :3]		)
     # assert values
     if model_name == "upernet-swin-tiny":
          SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor(
              [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]		)
     elif model_name == "upernet-swin-small":
          SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(
              [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]		)
     elif model_name == "upernet-swin-base":
          SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(
              [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]		)
     elif model_name == "upernet-swin-large":
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(
              [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]		)
     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  :Optional[int]  				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""upernet-swin-tiny""",
     type=str,
     choices=[f"""upernet-swin-{size}""" for size in ["""tiny""", """small""", """base""", """large"""]],
     help="""Name of the Swin + 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  :Dict  				=    parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''





import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						"segformer"






    def __init__(       self    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=[2, 2, 2, 2]    ,							lowercase__=[8, 4, 2, 1]    ,							lowercase__=[32, 64, 160, 256]    ,							lowercase__=[7, 3, 3, 3]    ,							lowercase__=[4, 2, 2, 2]    ,							lowercase__=[1, 2, 5, 8]    ,							lowercase__=[4, 4, 4, 4]    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.1    ,							lowercase__=0.0_2    ,							lowercase__=0.1    ,							lowercase__=1E-6    ,							lowercase__=256    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Union[str, Any]:
         super().__init__(**lowercase__ )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                  ' removed, as the behaviour will default to that of reshape_last_stage = True.'    ,							lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       num_channels
         SCREAMING_SNAKE_CASE					:			int    	=       num_encoder_blocks
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       depths
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sr_ratios
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       patch_sizes
         SCREAMING_SNAKE_CASE					:			int    	=       strides
         SCREAMING_SNAKE_CASE					:			Tuple    	=       mlp_ratios
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       classifier_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			int    	=       layer_norm_eps
         SCREAMING_SNAKE_CASE					:			Any    	=       decoder_hidden_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       kwargs.get('reshape_last_stage'    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						version.parse("1.11" )






    @property
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ] )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return 12



