
"""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  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






       """simple docstring"""



       _lowerCAmelCase  	=			[]
       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  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






       """simple docstring"""



       _lowerCAmelCase  	=			[]
       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  (    SCREAMING_SNAKE_CASE:					int						):






       """simple docstring"""



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






def 	__snake_case  (    ):






       """simple docstring"""



       _lowerCAmelCase  	=			[]
       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  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str						):






       """simple docstring"""



       _lowerCAmelCase  	=			'imagenet-1k-id2label.json'
       _lowerCAmelCase  	=			1000

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

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

       _lowerCAmelCase  	=			_lowerCAmelCase  	=			CvtConfig(num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

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

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

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              _lowerCAmelCase  	=			[2, 2, 20]
              _lowerCAmelCase  	=			[3, 12, 16]
              _lowerCAmelCase  	=			[192, 768, 1024]

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

       _lowerCAmelCase  	=			OrderedDict()
       _lowerCAmelCase  	=			[]

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

       _lowerCAmelCase  	=			list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(SCREAMING_SNAKE_CASE						)
       for i in range(len(SCREAMING_SNAKE_CASE						)						):
              _lowerCAmelCase  	=			original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(SCREAMING_SNAKE_CASE						)
       model.save_pretrained(SCREAMING_SNAKE_CASE						)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


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

if __name__ == "__main__":
 _snake_case          =						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.'''
 )

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








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[int(SCREAMING_SNAKE_CASE						) for i in ip_va_address.split('.'						) if i.isdigit()]
							return len(SCREAMING_SNAKE_CASE						) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE						) <= 254 for octet in octets						)


if __name__ == "__main__":
	_snake_case          =						input().strip()
	_snake_case          =						'''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
	print(f'{ip} is a {valid_or_invalid} IP v4 address.')






