






"""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     __lowercase					(    a :				Union[str, Any] )					->					Dict:
      __snake_case      :    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     __lowercase					(    a :				Optional[int]       ,		a :				Any )					->					Any:
      __snake_case      :    Tuple  			=[]
      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					(    a :				str )					->					Optional[Any]:
      __snake_case      :    Optional[Any]  			=[]
      token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''') )
      return token



def     __lowercase					(    )					->					Tuple:
      __snake_case      :    Optional[int]  			=[]
      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					(    a :				Dict       ,		a :				Dict       ,		a :				str       ,		a :				List[Any] )					->					Union[str, Any]:
      __snake_case      :    List[Any]  			='''imagenet-1k-id2label.json'''
      __snake_case      :    str  			=1_000

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

      __snake_case      :    int  			=idalabel
      __snake_case      :    Optional[int]  			={v: k for k, v in idalabel.items()}

      __snake_case      :    int  			=CvtConfig(num_labels=__UpperCamelCase       ,		idalabel=__UpperCamelCase       ,		labelaid=__UpperCamelCase )

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

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

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

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

      __snake_case      :    Any  			=OrderedDict()
      __snake_case      :    Dict  			=[]

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

      __snake_case      :    Dict  			=list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(__UpperCamelCase )
      for i in range(len(__UpperCamelCase ) ):
            __snake_case      :    Any  			=original_weights[list_of_state_dict[i][1]]

      model.load_state_dict(__UpperCamelCase )
      model.save_pretrained(__UpperCamelCase )
      image_processor.save_pretrained(__UpperCamelCase )


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

if __name__ == "__main__":
    UpperCamelCase_	:				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."""
    )

    UpperCamelCase_	:				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"""





from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

UpperCamelCase_	:				Optional[Any]										= 6_37_81_37.0
UpperCamelCase_	:				Optional[Any]										= 6_35_67_52.31_42_45
UpperCamelCase_	:				Union[str, Any]										= 6378137



def     __lowercase					(    a :				float       ,		a :				float       ,		a :				float       ,		a :				float )					->					float:
      __snake_case      :    Optional[Any]  			=(AXIS_A - AXIS_B) / AXIS_A
      # Parametric latitudes
      # https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
      __snake_case      :    List[str]  			=atan((1 - flattening) * tan(radians(a ) ) )
      __snake_case      :    Any  			=atan((1 - flattening) * tan(radians(a ) ) )

      # Compute central angle between two points
      # using haversine theta. sigma =  haversine_distance / equatorial radius
      __snake_case      :    Optional[Any]  			=haversine_distance(a       ,		a       ,		a       ,		a ) / EQUATORIAL_RADIUS

      # Intermediate P and Q values
      __snake_case      :    List[str]  			=(b_lata + b_lata) / 2
      __snake_case      :    Dict  			=(b_lata - b_lata) / 2

      # Intermediate X value
      # X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
      __snake_case      :    Optional[int]  			=(sin(a ) ** 2) * (cos(a ) ** 2)
      __snake_case      :    Any  			=cos(sigma / 2 ) ** 2
      __snake_case      :    Optional[Any]  			=(sigma - sin(a )) * (x_numerator / x_demonimator)

      # Intermediate Y value
      # Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
      __snake_case      :    Tuple  			=(cos(a ) ** 2) * (sin(a ) ** 2)
      __snake_case      :    Dict  			=sin(sigma / 2 ) ** 2
      __snake_case      :    Tuple  			=(sigma + sin(a )) * (y_numerator / y_denominator)

      return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


