



"""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 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       _lowerCamelCase						:    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 	snake_case_  (				A_ :		List[str],    A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, 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 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			[]
       token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')						)
       return token






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    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 	snake_case_  (				A_ :		List[str],    A_ :		Union[str, Any],    A_ :		List[Any],    A_ :		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			'''imagenet-1k-id2label.json'''
       _lowerCamelCase						:    Dict         =			10_00

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

       _lowerCamelCase						:    List[str]         =			idalabel
       _lowerCamelCase						:    List[str]         =			{v: k for k, v in idalabel.items()}

       _lowerCamelCase						:    Dict         =			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":
              _lowerCamelCase						:    Tuple         =			[1, 2, 10]

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

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

       _lowerCamelCase						:    str         =			CvtForImageClassification(A_						)
       _lowerCamelCase						:    Dict         =			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''						)
       _lowerCamelCase						:    Any         =			image_size
       _lowerCamelCase						:    Any         =			torch.load(A_,    map_location=torch.device('''cpu'''						)						)

       _lowerCamelCase						:    List[str]         =			OrderedDict()
       _lowerCamelCase						:    str         =			[]

       for idx in range(len(config.depth						)						):
              if config.cls_token[idx]:
                     _lowerCamelCase						:    Tuple         =			list_of_state_dict + cls_token(A_						)
              _lowerCamelCase						:    Dict         =			list_of_state_dict + embeddings(A_						)
              for cnt in range(config.depth[idx]						):
                     _lowerCamelCase						:    List[str]         =			list_of_state_dict + attention(A_,    A_						)

       _lowerCamelCase						:    Any         =			list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(A_						)
       for i in range(len(A_						)						):
              _lowerCamelCase						:    Optional[Any]         =			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__      =    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__      =    parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







"""simple docstring"""






from maths.prime_factors import prime_factors






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       if not isinstance(A_,    A_						):
              _lowerCamelCase						:    str         =			F'''Input value of [number={number}] must be an integer'''
              raise TypeError(A_						)
       if number < 1:
              raise ValueError('''Input must be a positive integer'''						)
       return -1 if len(prime_factors(A_						)						) % 2 else 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()


