





"""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_       (   UpperCAmelCase_  )						->int:





       """simple docstring"""


       __UpperCAmelCase    :						Optional[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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Union[str, Any]:





       """simple docstring"""


       __UpperCAmelCase    :						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       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





       """simple docstring"""


       __UpperCAmelCase    :						Union[str, Any]       =						[]
       token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')  )
       return token


def       lowerCamelCase_       (   )						->Optional[int]:





       """simple docstring"""


       __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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





       """simple docstring"""


       __UpperCAmelCase    :						Optional[Any]       =						'''imagenet-1k-id2label.json'''
       __UpperCAmelCase    :						List[Any]       =						10_00

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

       __UpperCAmelCase    :						str       =						idalabel
       __UpperCAmelCase    :						Optional[Any]       =						{v: k for k, v in idalabel.items()}

       __UpperCAmelCase    :						int       =						CvtConfig(num_labels=lowercase__  ,			idalabel=lowercase__  ,			labelaid=lowercase__  )

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

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

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              __UpperCAmelCase    :						str       =						[2, 2, 20]
              __UpperCAmelCase    :						Optional[int]       =						[3, 12, 16]
              __UpperCAmelCase    :						Dict       =						[1_92, 7_68, 10_24]

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

       __UpperCAmelCase    :						List[str]       =						OrderedDict()
       __UpperCAmelCase    :						List[Any]       =						[]

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

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

       model.load_state_dict(lowercase__  )
       model.save_pretrained(lowercase__  )
       image_processor.save_pretrained(lowercase__  )


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

if __name__ == "__main__":
  lowercase__				:str				   =       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_8_4,
      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__				:Optional[int]				   =       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_       (   UpperCAmelCase_  )						->float:





  """simple docstring"""


  return 10 - x * x


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->float:





  """simple docstring"""


  if equation(UpperCAmelCase_  ) * equation(UpperCAmelCase_  ) >= 0:
    raise ValueError('''Wrong space!'''  )

  __UpperCAmelCase    :						Tuple       =						a
  while (b - a) >= 0.01:
    # Find middle point
    __UpperCAmelCase    :						List[str]       =						(a + b) / 2
    # Check if middle point is root
    if equation(UpperCAmelCase_  ) == 0.0:
      break
    # Decide the side to repeat the steps
    if equation(UpperCAmelCase_  ) * equation(UpperCAmelCase_  ) < 0:
      __UpperCAmelCase    :						Union[str, Any]       =						c
    else:
      __UpperCAmelCase    :						str       =						c
  return c


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(bisection(-2, 5))
  print(bisection(0, 6))