

'''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:Any						      =					[]
  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:Optional[Any]						      =					[]
  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:int						      =					[]
  token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token") )
  return token





def    A_  ( ):
  SCREAMING_SNAKE_CASE: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:Tuple						      =					"imagenet-1k-id2label.json"
  SCREAMING_SNAKE_CASE:int						      =					1000

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

  SCREAMING_SNAKE_CASE:Optional[int]						      =					idalabel
  SCREAMING_SNAKE_CASE:Dict						      =					{v: k for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE:Optional[Any]						      =					CvtConfig(num_labels=snake_case      ,       idalabel=snake_case      ,       labelaid=snake_case )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit("/"      ,       1 )[-1][4:6] == "13":
    SCREAMING_SNAKE_CASE: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:Any						      =					[2, 2, 20]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[3, 12, 16]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[192, 768, 1024]

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

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

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

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

  model.load_state_dict(snake_case )
  model.save_pretrained(snake_case )
  image_processor.save_pretrained(snake_case )


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

if __name__ == "__main__":
    A_       						=							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_       						=							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









'''simple docstring'''

from importlib import import_module

from .logging import get_logger


A_       						=							get_logger(__name__)





class 							_snake_case :
   def __init__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Dict=None							):
     SCREAMING_SNAKE_CASE:Any						      =					attrs or []
     if module is not None:
       for key in module.__dict__:
         if key in attrs or not key.startswith("__"							):
           setattr(self     ,SCREAMING_SNAKE_CASE__     ,getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:Any						      =					module._original_module if isinstance(SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj							) else module





class 							_snake_case :
   _A       :     Any       =					[]
   def __init__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None							):
     SCREAMING_SNAKE_CASE:Any						      =					obj
     SCREAMING_SNAKE_CASE:Any						      =					target
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					new
     SCREAMING_SNAKE_CASE:Any						      =					target.split("."							)[0]
     SCREAMING_SNAKE_CASE:str						      =					{}
     SCREAMING_SNAKE_CASE:List[Any]						      =					attrs or []
   def __enter__( self			:      Tuple							):
     *SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					self.target.split("."							)

     # Patch modules:
     # it's used to patch attributes of submodules like "os.path.join";
     # in this case we need to patch "os" and "os.path"

     for i in range(len(SCREAMING_SNAKE_CASE__							)							):
       try:
         SCREAMING_SNAKE_CASE:int						      =					import_module(".".join(submodules[: i + 1]							)							)
       except ModuleNotFoundError:
         continue
       # We iterate over all the globals in self.obj in case we find "os" or "os.path"
       for attr in self.obj.__dir__():
         SCREAMING_SNAKE_CASE:str						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
         # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
         # This allows to patch renamed modules like "from os import path as ospath".
         if obj_attr is submodule or (
             (isinstance(SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj							) and obj_attr._original_module is submodule)
         ):
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					obj_attr
           # patch at top level
           setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj(SCREAMING_SNAKE_CASE__     ,attrs=self.attrs							)							)
           SCREAMING_SNAKE_CASE:int						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
           # construct lower levels patches
           for key in submodules[i + 1 :]:
             setattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)     ,attrs=self.attrs							)							)
             SCREAMING_SNAKE_CASE:Dict						      =					getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
           # finally set the target attribute
           setattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,self.new							)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

     if submodules:  # if it's an attribute of a submodule like "os.path.join"
       try:
         SCREAMING_SNAKE_CASE:Any						      =					getattr(import_module(".".join(SCREAMING_SNAKE_CASE__							)							)     ,SCREAMING_SNAKE_CASE__							)
       except (AttributeError, ModuleNotFoundError):
         return
       # We iterate over all the globals in self.obj in case we find "os.path.join"
       for attr in self.obj.__dir__():
         # We don't check for the name of the global, but rather if its value *is* "os.path.join".
         # This allows to patch renamed attributes like "from os.path import join as pjoin".
         if getattr(self.obj     ,SCREAMING_SNAKE_CASE__							) is attr_value:
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
           setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.new							)
     elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
       SCREAMING_SNAKE_CASE:int						      =					globals()["__builtins__"][target_attr]
       setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.new							)
     else:
       raise RuntimeError(F'''Tried to patch attribute {target_attr} instead of a submodule.'''							)
   def __exit__( self			:      List[Any]     ,*SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     for attr in list(self.original							):
       setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.original.pop(SCREAMING_SNAKE_CASE__							)							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     self.__enter__()
     self._active_patches.append(self							)







   def     __UpperCamelCase						( self			:      Dict							):
     try:
       self._active_patches.remove(self							)
     except ValueError:
       # If the patch hasn't been started this will fail
       return None

     return self.__exit__()






