


'''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__     (		__lowerCamelCase			:				Dict				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =[]
   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__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				Tuple				):







   '''simple docstring'''




   _UpperCAmelCase  :  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 lowerCamelCase__     (		__lowerCamelCase			:				Union[str, Any]				):







   '''simple docstring'''




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





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =[]
   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__     (		__lowerCamelCase			:				List[str]			,       __lowerCamelCase			:				Union[str, Any]			,       __lowerCamelCase			:				Union[str, Any]			,       __lowerCamelCase			:				List[Any]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       ='imagenet-1k-id2label.json'
   _UpperCAmelCase  :  Tuple				       =1_0_0_0

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

   _UpperCAmelCase  :  Any				       =idalabel
   _UpperCAmelCase  :  Tuple				       ={v: k for k, v in idalabel.items()}

   _UpperCAmelCase  :  Optional[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  :  Dict				       =[1, 2, 1_0]

   # For depth size 21 (21 = 1+4+16)
   elif cvt_model.rsplit('/'			,       1				)[-1][4:6] == "21":
      _UpperCAmelCase  :  int				       =[1, 4, 1_6]

   # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
   else:
      _UpperCAmelCase  :  int				       =[2, 2, 2_0]
      _UpperCAmelCase  :  Optional[int]				       =[3, 1_2, 1_6]
      _UpperCAmelCase  :  Dict				       =[1_9_2, 7_6_8, 1_0_2_4]

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

   _UpperCAmelCase  :  Optional[Any]				       =OrderedDict()
   _UpperCAmelCase  :  Any				       =[]

   for idx in range(len(config.depth				)				):
      if config.cls_token[idx]:
         _UpperCAmelCase  :  str				       =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  :  Optional[int]				       =list_of_state_dict + attention(__lowerCamelCase			,       __lowerCamelCase				)

   _UpperCAmelCase  :  int				       =list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(__lowerCamelCase				)
   for i in range(len(__lowerCamelCase				)				):
      _UpperCAmelCase  :  Optional[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__":
 lowercase				=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.'
 )

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





'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =set()

   # Replace all the whitespace in our sentence
   _UpperCAmelCase  :  Dict				       =input_str.replace(' '			,       ''				)
   for alpha in input_str:
      if "a" <= alpha.lower() <= "z":
         frequency.add(alpha.lower()				)
   return len(__lowerCamelCase				) == 2_6





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =[False] * 2_6
   for char in input_str:
      if char.islower():
         _UpperCAmelCase  :  Dict				       =True
      elif char.isupper():
         _UpperCAmelCase  :  List[str]				       =True
   return all(__lowerCamelCase				)





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   return len({char for char in input_str.lower() if char.isalpha()}				) == 2_6





def lowerCamelCase__     (		):







   '''simple docstring'''




   from timeit import timeit

   _UpperCAmelCase  :  List[Any]				       ='from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
   print(timeit('is_pangram()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_faster()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_fastest()'			,       setup=__lowerCamelCase				)				)
   # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
   # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

