

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        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					3_84
     SCREAMING_SNAKE_CASE__					=					7
     if "tiny" in model_name:
          SCREAMING_SNAKE_CASE__					=					96
          SCREAMING_SNAKE_CASE__					=					(2, 2, 6, 2)
          SCREAMING_SNAKE_CASE__					=					(3, 6, 12, 24)
     elif "small" in model_name:
          SCREAMING_SNAKE_CASE__					=					96
          SCREAMING_SNAKE_CASE__					=					(2, 2, 18, 2)
          SCREAMING_SNAKE_CASE__					=					(3, 6, 12, 24)
     elif "base" in model_name:
          SCREAMING_SNAKE_CASE__					=					1_28
          SCREAMING_SNAKE_CASE__					=					(2, 2, 18, 2)
          SCREAMING_SNAKE_CASE__					=					(4, 8, 16, 32)
          SCREAMING_SNAKE_CASE__					=					12
          SCREAMING_SNAKE_CASE__					=					5_12
     elif "large" in model_name:
          SCREAMING_SNAKE_CASE__					=					1_92
          SCREAMING_SNAKE_CASE__					=					(2, 2, 18, 2)
          SCREAMING_SNAKE_CASE__					=					(6, 12, 24, 48)
          SCREAMING_SNAKE_CASE__					=					12
          SCREAMING_SNAKE_CASE__					=					7_68

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

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

     return config
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]

     # 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        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					dct.pop(_A       )
     SCREAMING_SNAKE_CASE__					=					val
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[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__					=					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__					=					state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''       )
               SCREAMING_SNAKE_CASE__					=					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__					=					in_proj_weight[:dim, :]
               SCREAMING_SNAKE_CASE__					=					in_proj_bias[: dim]
               SCREAMING_SNAKE_CASE__					=					in_proj_weight[
                   dim : dim * 2, :
               ]
               SCREAMING_SNAKE_CASE__					=					in_proj_bias[
                   dim : dim * 2
               ]
               SCREAMING_SNAKE_CASE__					=					in_proj_weight[
                   -dim :, :
               ]
               SCREAMING_SNAKE_CASE__					=					in_proj_bias[-dim :]
               # fmt: on
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					x.shape
     SCREAMING_SNAKE_CASE__					=					x.reshape(_A   ,	4   ,	in_channel // 4       )
     SCREAMING_SNAKE_CASE__					=					x[:, [0, 2, 1, 3], :].transpose(1   ,	2       ).reshape(_A   ,	_A       )
     return x
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					x.shape
     SCREAMING_SNAKE_CASE__					=					x.reshape(_A   ,	in_channel // 4   ,	4       )
     SCREAMING_SNAKE_CASE__					=					x[:, :, [0, 2, 1, 3]].transpose(1   ,	2       ).reshape(_A   ,	_A       )

     return x
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					x.shape[0]
     SCREAMING_SNAKE_CASE__					=					x.reshape(4   ,	in_channel // 4       )
     SCREAMING_SNAKE_CASE__					=					x[[0, 2, 1, 3], :].transpose(0   ,	1       ).reshape(_A       )
     return x
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					x.shape[0]
     SCREAMING_SNAKE_CASE__					=					x.reshape(in_channel // 4   ,	4       )
     SCREAMING_SNAKE_CASE__					=					x[:, [0, 2, 1, 3]].transpose(0   ,	1       ).reshape(_A       )
     return x
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					{
         '''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__					=					model_name_to_url[model_name]
     SCREAMING_SNAKE_CASE__					=					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__					=					get_upernet_config(_A       )
     SCREAMING_SNAKE_CASE__					=					UperNetForSemanticSegmentation(_A       )
     model.eval()

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

     # rename keys
     SCREAMING_SNAKE_CASE__					=					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__					=					reverse_correct_unfold_reduction_order(_A       )
               if "norm" in key:
                    SCREAMING_SNAKE_CASE__					=					reverse_correct_unfold_norm_order(_A       )

     model.load_state_dict(_A       )

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

     SCREAMING_SNAKE_CASE__					=					SegformerImageProcessor()
     SCREAMING_SNAKE_CASE__					=					processor(_A   ,	return_tensors='''pt'''       ).pixel_values

     with torch.no_grad():
          SCREAMING_SNAKE_CASE__					=					model(_A       )
          SCREAMING_SNAKE_CASE__					=					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__					=					torch.tensor(
              [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]       )
     elif model_name == "upernet-swin-small":
          SCREAMING_SNAKE_CASE__					=					torch.tensor(
              [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]       )
     elif model_name == "upernet-swin-base":
          SCREAMING_SNAKE_CASE__					=					torch.tensor(
              [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]       )
     elif model_name == "upernet-swin-large":
          SCREAMING_SNAKE_CASE__					=					torch.tensor(
              [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]       )
     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__":
       _SCREAMING_SNAKE_CASE  :						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.'''
       )

       _SCREAMING_SNAKE_CASE  :						Tuple         =    parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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

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






@dataclass(frozen=A__ )
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      field(default="question-answering-extractive"		,  metadata={"include_in_asdict_even_if_is_default": True} )
  a     =      Features({"question": Value("string" ), "context": Value("string" )} )
  a     =      Features(
      {
          "answers": Sequence(
              {
                  "text": Value("string" ),
                  "answer_start": Value("int32" ),
              } )
      } )
  a     =      "question"
  a     =      "context"
  a     =      "answers"






  @property
  def    lowercase_	(							self		:	Dict      )	->							Dict[str, str]:
       return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}


