






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				   =		logging.get_logger(__name__)
def 						_a    (      a :int	,							a :Dict=False )							->			Optional[Any]:
 a		  =      OrderedDict()
 for key, value in state_dict.items():
  if encoder_only and not key.startswith('''head''' ):
   a		  =      '''segformer.encoder.''' + key
  if key.startswith('''backbone''' ):
   a		  =      key.replace('''backbone'''	,							'''segformer.encoder''' )
  if "patch_embed" in key:
   # replace for example patch_embed1 by patch_embeddings.0
   a		  =      key[key.find('''patch_embed''' ) + len('''patch_embed''' )]
   a		  =      key.replace(F"""patch_embed{idx}"""	,							F"""patch_embeddings.{int(a )-1}""" )
  if "norm" in key:
   a		  =      key.replace('''norm'''	,							'''layer_norm''' )
  if "segformer.encoder.layer_norm" in key:
   # replace for example layer_norm1 by layer_norm.0
   a		  =      key[key.find('''segformer.encoder.layer_norm''' ) + len('''segformer.encoder.layer_norm''' )]
   a		  =      key.replace(F"""layer_norm{idx}"""	,							F"""layer_norm.{int(a )-1}""" )
  if "layer_norm1" in key:
   a		  =      key.replace('''layer_norm1'''	,							'''layer_norm_1''' )
  if "layer_norm2" in key:
   a		  =      key.replace('''layer_norm2'''	,							'''layer_norm_2''' )
  if "block" in key:
   # replace for example block1 by block.0
   a		  =      key[key.find('''block''' ) + len('''block''' )]
   a		  =      key.replace(F"""block{idx}"""	,							F"""block.{int(a )-1}""" )
  if "attn.q" in key:
   a		  =      key.replace('''attn.q'''	,							'''attention.self.query''' )
  if "attn.proj" in key:
   a		  =      key.replace('''attn.proj'''	,							'''attention.output.dense''' )
  if "attn" in key:
   a		  =      key.replace('''attn'''	,							'''attention.self''' )
  if "fc1" in key:
   a		  =      key.replace('''fc1'''	,							'''dense1''' )
  if "fc2" in key:
   a		  =      key.replace('''fc2'''	,							'''dense2''' )
  if "linear_pred" in key:
   a		  =      key.replace('''linear_pred'''	,							'''classifier''' )
  if "linear_fuse" in key:
   a		  =      key.replace('''linear_fuse.conv'''	,							'''linear_fuse''' )
   a		  =      key.replace('''linear_fuse.bn'''	,							'''batch_norm''' )
  if "linear_c" in key:
   # replace for example linear_c4 by linear_c.3
   a		  =      key[key.find('''linear_c''' ) + len('''linear_c''' )]
   a		  =      key.replace(F"""linear_c{idx}"""	,							F"""linear_c.{int(a )-1}""" )
  if key.startswith('''head''' ):
   a		  =      key.replace('''head'''	,							'''classifier''' )
  a		  =      value

 return new_state_dict
def 						_a    (      a :List[str]	,							a :Union[str, Any] )							->			Tuple:
 # for each of the encoder blocks:
 for i in range(config.num_encoder_blocks ):
  for j in range(config.depths[i] ):
   # read in weights + bias of keys and values (which is a single matrix in the original implementation)
   a		  =      state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight""" )
   a		  =      state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias""" )
   # next, add keys and values (in that order) to the state dict
   a		  =      kv_weight[
       : config.hidden_sizes[i], :
   ]
   a		  =      kv_bias[: config.hidden_sizes[i]]
   a		  =      kv_weight[
       config.hidden_sizes[i] :, :
   ]
   a		  =      kv_bias[
       config.hidden_sizes[i] :
   ]
def 						_a    (      )							->			Optional[Any]:
 a		  =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 a		  =      Image.open(requests.get(a	,							stream=a ).raw )

 return image
@torch.no_grad()
def 						_a    (      a :Tuple	,							a :Any	,							a :int )							->			Any:
 a		  =      SegformerConfig()
 a		  =      False

 # set attributes based on model_name
 a		  =      '''huggingface/label-files'''
 if "segformer" in model_name:
  a		  =      model_name[len('''segformer.''' ) : len('''segformer.''' ) + 2]
  if "ade" in model_name:
   a		  =      150
   a		  =      '''ade20k-id2label.json'''
   a		  =      (1, 150, 128, 128)
  elif "city" in model_name:
   a		  =      19
   a		  =      '''cityscapes-id2label.json'''
   a		  =      (1, 19, 128, 128)
  else:
   raise ValueError(F"""Model {model_name} not supported""" )
 elif "mit" in model_name:
  a		  =      True
  a		  =      model_name[4:6]
  a		  =      1_000
  a		  =      '''imagenet-1k-id2label.json'''
  a		  =      (1, 1_000)
 else:
  raise ValueError(F"""Model {model_name} not supported""" )

 # set config attributes
 a		  =      json.load(open(hf_hub_download(a	,							a	,							repo_type='''dataset''' )	,							'''r''' ) )
 a		  =      {int(a ): v for k, v in idalabel.items()}
 a		  =      idalabel
 a		  =      {v: k for k, v in idalabel.items()}
 if size == "b0":
  pass
 elif size == "b1":
  a		  =      [64, 128, 320, 512]
  a		  =      256
 elif size == "b2":
  a		  =      [64, 128, 320, 512]
  a		  =      768
  a		  =      [3, 4, 6, 3]
 elif size == "b3":
  a		  =      [64, 128, 320, 512]
  a		  =      768
  a		  =      [3, 4, 18, 3]
 elif size == "b4":
  a		  =      [64, 128, 320, 512]
  a		  =      768
  a		  =      [3, 8, 27, 3]
 elif size == "b5":
  a		  =      [64, 128, 320, 512]
  a		  =      768
  a		  =      [3, 6, 40, 3]
 else:
  raise ValueError(F"""Size {size} not supported""" )

 # load image processor (only resize + normalize)
 a		  =      SegformerImageProcessor(
     image_scale=(512, 512)	,							keep_ratio=a	,							align=a	,							do_random_crop=a )

 # prepare image
 a		  =      prepare_img()
 a		  =      image_processor(images=a	,							return_tensors='''pt''' ).pixel_values

 logger.info(F"""Converting model {model_name}...""" )

 # load original state dict
 if encoder_only:
  a		  =      torch.load(a	,							map_location=torch.device('''cpu''' ) )
 else:
  a		  =      torch.load(a	,							map_location=torch.device('''cpu''' ) )['''state_dict''']

 # rename keys
 a		  =      rename_keys(a	,							encoder_only=a )
 if not encoder_only:
  del state_dict["decode_head.conv_seg.weight"]
  del state_dict["decode_head.conv_seg.bias"]

 # key and value matrices need special treatment
 read_in_k_v(a	,							a )

 # create HuggingFace model and load state dict
 if encoder_only:
  a		  =      False
  a		  =      SegformerForImageClassification(a )
 else:
  a		  =      SegformerForSemanticSegmentation(a )
 model.load_state_dict(a )
 model.eval()

 # forward pass
 a		  =      model(a )
 a		  =      outputs.logits

 # set expected_slice based on model name
 # ADE20k checkpoints
 if model_name == "segformer.b0.512x512.ade.160k":
  a		  =      torch.tensor(
      [
          [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
          [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
          [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
      ] )
 elif model_name == "segformer.b1.512x512.ade.160k":
  a		  =      torch.tensor(
      [
          [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
          [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
          [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
      ] )
 elif model_name == "segformer.b2.512x512.ade.160k":
  a		  =      torch.tensor(
      [
          [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
          [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
          [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
      ] )
 elif model_name == "segformer.b3.512x512.ade.160k":
  a		  =      torch.tensor(
      [
          [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
          [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
          [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
      ] )
 elif model_name == "segformer.b4.512x512.ade.160k":
  a		  =      torch.tensor(
      [
          [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
          [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
          [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
      ] )
 elif model_name == "segformer.b5.640x640.ade.160k":
  a		  =      torch.tensor(
      [
          [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
          [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
          [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
      ] )
 # Cityscapes checkpoints
 elif model_name == "segformer.b0.1024x1024.city.160k":
  a		  =      torch.tensor(
      [
          [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
          [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
          [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
      ] )
 elif model_name == "segformer.b0.512x1024.city.160k":
  a		  =      torch.tensor(
      [
          [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
          [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
          [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
      ] )
 elif model_name == "segformer.b0.640x1280.city.160k":
  a		  =      torch.tensor(
      [
          [
              [-1.1372e01, -1.2787e01, -1.3477e01],
              [-1.2536e01, -1.4194e01, -1.4409e01],
              [-1.3217e01, -1.4888e01, -1.5327e01],
          ],
          [
              [-1.4791e01, -1.7122e01, -1.8277e01],
              [-1.7163e01, -1.9192e01, -1.9533e01],
              [-1.7897e01, -1.9991e01, -2.0315e01],
          ],
          [
              [7.6723e-01, 4.1921e-01, -7.7878e-02],
              [4.7772e-01, 9.5557e-03, -2.8082e-01],
              [3.6032e-01, -2.4826e-01, -5.1168e-01],
          ],
      ] )
 elif model_name == "segformer.b0.768x768.city.160k":
  a		  =      torch.tensor(
      [
          [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
          [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
          [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
      ] )
 elif model_name == "segformer.b1.1024x1024.city.160k":
  a		  =      torch.tensor(
      [
          [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
          [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
          [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
      ] )
 elif model_name == "segformer.b2.1024x1024.city.160k":
  a		  =      torch.tensor(
      [
          [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
          [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
          [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
      ] )
 elif model_name == "segformer.b3.1024x1024.city.160k":
  a		  =      torch.tensor(
      [
          [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
          [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
          [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
      ] )
 elif model_name == "segformer.b4.1024x1024.city.160k":
  a		  =      torch.tensor(
      [
          [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
          [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
          [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
      ] )
 elif model_name == "segformer.b5.1024x1024.city.160k":
  a		  =      torch.tensor(
      [
          [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
          [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
          [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
      ] )
 else:
  a		  =      logits.argmax(-1 ).item()
  print('''Predicted class:'''	,							model.config.idalabel[predicted_class_idx] )

 # verify logits
 if not encoder_only:
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3, :3, :3]	,							a	,							atol=1e-2 )

 # finally, save model and image processor
 logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
 Path(a ).mkdir(exist_ok=a )
 model.save_pretrained(a )
 image_processor.save_pretrained(a )


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()

     parser.add_argument(
         "--model_name",
         default="segformer.b0.512x512.ade.160k",
         type=str,
         help="Name of the model you'd like to convert.",
     )
     parser.add_argument(
         "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
     )
     UpperCAmelCase__				   =		parser.parse_args()
     convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_a    (      a :List[Any] )							->			Optional[int]:
 a		  =      []
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
         F"""stage{idx}.patch_embed.proj.weight""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
         F"""stage{idx}.patch_embed.proj.bias""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
         F"""stage{idx}.patch_embed.norm.weight""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
         F"""stage{idx}.patch_embed.norm.bias""",
     ) )
 return embed
def 						_a    (      a :List[Any]	,							a :Optional[int] )							->			Dict:
 a		  =      []
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
     ) )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
 return attention_weights
def 						_a    (      a :Any )							->			List[Any]:
 a		  =      []
 token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
 return token
def 						_a    (      )							->			Optional[int]:
 a		  =      []
 head.append(('''layernorm.weight''', '''norm.weight''') )
 head.append(('''layernorm.bias''', '''norm.bias''') )
 head.append(('''classifier.weight''', '''head.weight''') )
 head.append(('''classifier.bias''', '''head.bias''') )
 return head
def 						_a    (      a :Tuple	,							a :Optional[int]	,							a :List[Any]	,							a :Union[str, Any] )							->			Optional[int]:
 a		  =      '''imagenet-1k-id2label.json'''
 a		  =      1_000

 a		  =      '''huggingface/label-files'''
 a		  =      num_labels
 a		  =      json.load(open(cached_download(hf_hub_url(a	,							a	,							repo_type='''dataset''' ) )	,							'''r''' ) )
 a		  =      {int(a ): v for k, v in idalabel.items()}

 a		  =      idalabel
 a		  =      {v: k for k, v in idalabel.items()}

 a		  =      a		  =      CvtConfig(num_labels=a	,							idalabel=a	,							labelaid=a )

 # For depth size 13 (13 = 1+2+10)
 if cvt_model.rsplit('''/'''	,							1 )[-1][4:6] == "13":
  a		  =      [1, 2, 10]

 # For depth size 21 (21 = 1+4+16)
 elif cvt_model.rsplit('''/'''	,							1 )[-1][4:6] == "21":
  a		  =      [1, 4, 16]

 # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
 else:
  a		  =      [2, 2, 20]
  a		  =      [3, 12, 16]
  a		  =      [192, 768, 1_024]

 a		  =      CvtForImageClassification(a )
 a		  =      AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
 a		  =      image_size
 a		  =      torch.load(a	,							map_location=torch.device('''cpu''' ) )

 a		  =      OrderedDict()
 a		  =      []

 for idx in range(len(config.depth ) ):
  if config.cls_token[idx]:
   a		  =      list_of_state_dict + cls_token(a )
  a		  =      list_of_state_dict + embeddings(a )
  for cnt in range(config.depth[idx] ):
   a		  =      list_of_state_dict + attention(a	,							a )

 a		  =      list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(a )
 for i in range(len(a ) ):
  a		  =      original_weights[list_of_state_dict[i][1]]

 model.load_state_dict(a )
 model.save_pretrained(a )
 image_processor.save_pretrained(a )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     parser.add_argument(
         "--cvt_model",
         default="cvt-w24",
         type=str,
         help="Name of the cvt model you'd like to convert.",
     )
     parser.add_argument(
         "--image_size",
         default=384,
         type=int,
         help="Input Image Size",
     )
     parser.add_argument(
         "--cvt_file_name",
         default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
         type=str,
         help="Input Image Size",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )

     UpperCAmelCase__				   =		parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


