



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__	):
      lowercase__     =  384
      lowercase__     =  7
      if "tiny" in model_name:
            lowercase__     =  96
            lowercase__     =  (2, 2, 6, 2)
            lowercase__     =  (3, 6, 12, 24)
      elif "small" in model_name:
            lowercase__     =  96
            lowercase__     =  (2, 2, 18, 2)
            lowercase__     =  (3, 6, 12, 24)
      elif "base" in model_name:
            lowercase__     =  128
            lowercase__     =  (2, 2, 18, 2)
            lowercase__     =  (4, 8, 16, 32)
            lowercase__     =  12
            lowercase__     =  512
      elif "large" in model_name:
            lowercase__     =  192
            lowercase__     =  (2, 2, 18, 2)
            lowercase__     =  (6, 12, 24, 48)
            lowercase__     =  12
            lowercase__     =  768

      # set label information
      lowercase__     =  150
      lowercase__     =  'huggingface/label-files'
      lowercase__     =  'ade20k-id2label.json'
      lowercase__     =  json.load(open(hf_hub_download(A__						,  A__						,  repo_type='dataset'	)						,  'r'	)	)
      lowercase__     =  {int(A__	): v for k, v in idalabel.items()}
      lowercase__     =  {v: k for k, v in idalabel.items()}

      lowercase__     =  SwinConfig(
          embed_dim=A__						,  depths=A__						,  num_heads=A__						,  window_size=A__						,  out_features=['stage1', 'stage2', 'stage3', 'stage4']						,  )
      lowercase__     =  UperNetConfig(
          backbone_config=A__						,  auxiliary_in_channels=A__						,  num_labels=A__						,  idalabel=A__						,  labelaid=A__						,  )

      return config

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  []

      # 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__	):
      lowercase__     =  dct.pop(A__	)
      lowercase__     =  val

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
      for i in range(len(backbone_config.depths	)	):
            lowercase__     =  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)
                  lowercase__     =  state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''	)
                  lowercase__     =  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
                  lowercase__     =  in_proj_weight[:dim, :]
                  lowercase__     =  in_proj_bias[: dim]
                  lowercase__     =  in_proj_weight[
                      dim : dim * 2, :
                  ]
                  lowercase__     =  in_proj_bias[
                      dim : dim * 2
                  ]
                  lowercase__     =  in_proj_weight[
                      -dim :, :
                  ]
                  lowercase__     =  in_proj_bias[-dim :]
                  # fmt: on

def   _lowerCAmelCase   (						A__	):
      lowercase__,	lowercase__     =  x.shape
      lowercase__     =  x.reshape(A__						,  4						,  in_channel // 4	)
      lowercase__     =  x[:, [0, 2, 1, 3], :].transpose(1						,  2	).reshape(A__						,  A__	)
      return x

def   _lowerCAmelCase   (						A__	):
      lowercase__,	lowercase__     =  x.shape
      lowercase__     =  x.reshape(A__						,  in_channel // 4						,  4	)
      lowercase__     =  x[:, :, [0, 2, 1, 3]].transpose(1						,  2	).reshape(A__						,  A__	)

      return x

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  x.shape[0]
      lowercase__     =  x.reshape(4						,  in_channel // 4	)
      lowercase__     =  x[[0, 2, 1, 3], :].transpose(0						,  1	).reshape(A__	)
      return x

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  x.shape[0]
      lowercase__     =  x.reshape(in_channel // 4						,  4	)
      lowercase__     =  x[:, [0, 2, 1, 3]].transpose(0						,  1	).reshape(A__	)
      return x

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  {
          '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',
      }
      lowercase__     =  model_name_to_url[model_name]
      lowercase__     =  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	)

      lowercase__     =  get_upernet_config(A__	)
      lowercase__     =  UperNetForSemanticSegmentation(A__	)
      model.eval()

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

      # rename keys
      lowercase__     =  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:
                        lowercase__     =  reverse_correct_unfold_reduction_order(A__	)
                  if "norm" in key:
                        lowercase__     =  reverse_correct_unfold_norm_order(A__	)

      model.load_state_dict(A__	)

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

      lowercase__     =  SegformerImageProcessor()
      lowercase__     =  processor(A__						,  return_tensors='pt'	).pixel_values

      with torch.no_grad():
            lowercase__     =  model(A__	)
            lowercase__     =  outputs.logits

      print(logits.shape	)
      print('First values of logits:'						,  logits[0, 0, :3, :3]	)
      # assert values
      if model_name == "upernet-swin-tiny":
            lowercase__     =  torch.tensor(
                [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]	)
      elif model_name == "upernet-swin-small":
            lowercase__     =  torch.tensor(
                [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]	)
      elif model_name == "upernet-swin-base":
            lowercase__     =  torch.tensor(
                [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]	)
      elif model_name == "upernet-swin-large":
            lowercase__     =  torch.tensor(
                [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]	)
      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__":
       a__      :				Optional[Any]   	=			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."
       )

       a__      :				Any   	=			parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class       UpperCAmelCase__(							unittest.TestCase     ,							lowerCamelCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[str])	->				Any:




         """simple docstring"""

         lowercase__     =  load_tool('text-classification')
         self.tool.setup()
         lowercase__     =  load_tool('text-classification'			,				remote=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.remote_tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')



   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.remote_tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')
