



from math import factorial, radians





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase = 18   , _UpperCAmelCase = 10				):
    __a						       =				angle_in_degrees - ((angle_in_degrees // 3_60.0) * 3_60.0)

    # Converting from degrees to radians
    __a						       =				radians(_UpperCAmelCase				)

    __a						       =				angle_in_radians
    __a						       =				3
    __a						       =				-1

    for _ in range(_UpperCAmelCase				):
        result += (b * (angle_in_radians**a)) / factorial(_UpperCAmelCase				)

        __a						       =				-b  # One positive term and the next will be negative and so on...
        a += 2  # Increased by 2 for every term.

    return round(_UpperCAmelCase   , _UpperCAmelCase				)


if __name__ == "__main__":
 __import__('''doctest''').testmod()













"""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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')    )
  return token
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       []
  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       'imagenet-1k-id2label.json'
  lowercase							    =       10_00

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

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

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

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

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

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

  lowercase							    =       OrderedDict()
  lowercase							    =       []

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

  lowercase							    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__snake_case    )
  for i in range(len(__snake_case    )    ):
    lowercase							    =       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__":
    _UpperCamelCase		:  Optional[Any]						=		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.'
    )

    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


