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





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					[]
 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    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Optional[int] =					'imagenet-1k-id2label.json'
 UpperCamelCase_					:						int =					1000

 UpperCamelCase_					:						Optional[Any] =					'huggingface/label-files'
 UpperCamelCase_					:						List[str] =					num_labels
 UpperCamelCase_					:						int =					json.load(open(cached_download(hf_hub_url(lowerCamelCase  ,  lowerCamelCase  ,  repo_type='dataset'	)	)  ,  'r'	)	)
 UpperCamelCase_					:						Union[str, Any] =					{int(lowerCamelCase	): v for k, v in idalabel.items()}

 UpperCamelCase_					:						List[str] =					idalabel
 UpperCamelCase_					:						Union[str, Any] =					{v: k for k, v in idalabel.items()}

 UpperCamelCase_					:						Any =					CvtConfig(num_labels=lowerCamelCase  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase	)

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

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

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

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

 UpperCamelCase_					:						Optional[int] =					OrderedDict()
 UpperCamelCase_					:						Dict =					[]

 for idx in range(len(config.depth	)	):
  if config.cls_token[idx]:
   UpperCamelCase_					:						int =					list_of_state_dict + cls_token(lowerCamelCase	)
  UpperCamelCase_					:						Union[str, Any] =					list_of_state_dict + embeddings(lowerCamelCase	)
  for cnt in range(config.depth[idx]	):
   UpperCamelCase_					:						List[Any] =					list_of_state_dict + attention(lowerCamelCase  ,  lowerCamelCase	)

 UpperCamelCase_					:						Tuple =					list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(lowerCamelCase	)
 for i in range(len(lowerCamelCase	)	):
  UpperCamelCase_					:						int =					original_weights[list_of_state_dict[i][1]]

 model.load_state_dict(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 image_processor.save_pretrained(lowerCamelCase	)


# 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=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.'
   )

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




from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



