



'''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 				__lowerCamelCase				(   A__    )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  []
   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 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  []
   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 				__lowerCamelCase				(   A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  []
   token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')    )
   return token







def 				__lowerCamelCase				(   )       ->	Union[str, Any]:






   """simple docstring"""
   UpperCamelCase					=  []
   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 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  'imagenet-1k-id2label.json'
   UpperCamelCase					=  1_000

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

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

   UpperCamelCase					=  UpperCamelCase					=  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":
      UpperCamelCase					=  [1, 2, 10]

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

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

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

   UpperCamelCase					=  OrderedDict()
   UpperCamelCase					=  []

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

   UpperCamelCase					=  list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(A__    )
   for i in range(len(A__    )    ):
      UpperCamelCase					=  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__":
   _lowerCamelCase	:       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=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."
   )

   _lowerCamelCase	:       Any      	=	parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







from bisect import bisect
from itertools import accumulate




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[str],  A		:					Tuple,  A		:					Optional[Any]   ):






			'''simple docstring'''







			a    =			sorted(zip(A,  A   ),  key=lambda A   : x[0] / x[1],  reverse=A   )
			a    ,		a    =			[i[0] for i in r], [i[1] for i in r]
			a    =			list(accumulate(A   )   )
			a    =			bisect(A,  A   )
			return (
			    0
			    if k == 0
			    else sum(vl[:k]   ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
			    if k != n
			    else sum(vl[:k]   )
			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






