

'''simple docstring'''

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_  ( snake_case ):
  SCREAMING_SNAKE_CASE:int						      =					[]
  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_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:List[str]						      =					[]
  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_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
  token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token") )
  return token





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					[]
  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_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					"imagenet-1k-id2label.json"
  SCREAMING_SNAKE_CASE:Dict						      =					1000

  SCREAMING_SNAKE_CASE:List[Any]						      =					"huggingface/label-files"
  SCREAMING_SNAKE_CASE:int						      =					num_labels
  SCREAMING_SNAKE_CASE:Dict						      =					json.load(open(cached_download(hf_hub_url(__UpperCamelCase      ,       __UpperCamelCase      ,       repo_type="dataset" ) )      ,       "r" ) )
  SCREAMING_SNAKE_CASE:str						      =					{int(__UpperCamelCase ): v for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE:List[str]						      =					idalabel
  SCREAMING_SNAKE_CASE:Tuple						      =					{v: k for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE:List[str]						      =					CvtConfig(num_labels=__UpperCamelCase      ,       idalabel=__UpperCamelCase      ,       labelaid=__UpperCamelCase )

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

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

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    SCREAMING_SNAKE_CASE:Optional[int]						      =					[2, 2, 20]
    SCREAMING_SNAKE_CASE:List[str]						      =					[3, 12, 16]
    SCREAMING_SNAKE_CASE:Optional[int]						      =					[192, 768, 1024]

  SCREAMING_SNAKE_CASE:Dict						      =					CvtForImageClassification(__UpperCamelCase )
  SCREAMING_SNAKE_CASE:List[str]						      =					AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
  SCREAMING_SNAKE_CASE:str						      =					image_size
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.load(__UpperCamelCase      ,       map_location=torch.device("cpu" ) )

  SCREAMING_SNAKE_CASE:Any						      =					OrderedDict()
  SCREAMING_SNAKE_CASE:Dict						      =					[]

  for idx in range(len(config.depth ) ):
    if config.cls_token[idx]:
      SCREAMING_SNAKE_CASE:Any						      =					list_of_state_dict + cls_token(__UpperCamelCase )
    SCREAMING_SNAKE_CASE:Dict						      =					list_of_state_dict + embeddings(__UpperCamelCase )
    for cnt in range(config.depth[idx] ):
      SCREAMING_SNAKE_CASE:int						      =					list_of_state_dict + attention(__UpperCamelCase      ,       __UpperCamelCase )

  SCREAMING_SNAKE_CASE:Any						      =					list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__UpperCamelCase )
  for i in range(len(__UpperCamelCase ) ):
    SCREAMING_SNAKE_CASE:int						      =					original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__UpperCamelCase )
  model.save_pretrained(__UpperCamelCase )
  image_processor.save_pretrained(__UpperCamelCase )


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

if __name__ == "__main__":
    A_     :   Tuple       						=							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=3_84,
        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."
    )

    A_     :   str       						=							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import warnings
from functools import wraps
from typing import Callable





def    A_  ( snake_case ):

  @wraps(snake_case )
  def _inner_fn(*snake_case      ,       **snake_case ):
    warnings.warn(
        (F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,       snake_case      ,       )
    return fn(*snake_case      ,       **snake_case )

  return _inner_fn






