




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__       (						A__       :					Any	):





       '''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 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					Dict	):





       '''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 					lowerCamelCase__       (						A__       :					List[Any]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						):





       '''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 					lowerCamelCase__       (						A__       :					str  ,			A__       :					Tuple  ,			A__       :					Any  ,			A__       :					Any	):





       '''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(__lowercase  ,			__lowercase  ,			repo_type="""dataset"""	)	)  ,			"""r"""	)	)
       __lowerCamelCase   =						{int(__lowercase	): v for k, v in idalabel.items()}

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

       __lowerCamelCase   =						__lowerCamelCase   =						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":
              __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(__lowercase	)
       __lowerCamelCase   =						AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""	)
       __lowerCamelCase   =						image_size
       __lowerCamelCase   =						torch.load(__lowercase  ,			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(__lowercase	)
              __lowerCamelCase   =						list_of_state_dict + embeddings(__lowercase	)
              for cnt in range(config.depth[idx]	):
                     __lowerCamelCase   =						list_of_state_dict + attention(__lowercase  ,			__lowercase	)

       __lowerCamelCase   =						list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(__lowercase	)
       for i in range(len(__lowercase	)	):
              __lowerCamelCase   =						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__":
       UpperCAmelCase_									=	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_									=	parser.parse_args()
       convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)








'''simple docstring'''
from __future__ import annotations

from typing import Any

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 pass






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[Any]							,	__A       :							Any	):
        __UpperCamelCase       =  data
        __UpperCamelCase       =  None






 def __iter__(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self
        __UpperCamelCase       =  []
        while node:
               if node in visited:
                      raise ContainsLoopError
               visited.append(__A	)
               yield node.data
               __UpperCamelCase       =  node.next_node






 @property
 def        _lowerCamelCase					(       self       :							List[str]	):
        try:
               list(self	)
               return False
        except ContainsLoopError:
               return True


if __name__ == "__main__":
    a__							:							Dict =Node(1)
    a__							:							Optional[int] =Node(2)
    a__							:							List[str] =Node(3)
    a__							:							Optional[int] =Node(4)
    print(root_node.has_loop)  # False
    a__							:							str =root_node.next_node
    print(root_node.has_loop)  # True

    a__							:							Optional[int] =Node(5)
    a__							:							List[Any] =Node(6)
    a__							:							int =Node(5)
    a__							:							Tuple =Node(6)
    print(root_node.has_loop)  # False

    a__							:							str =Node(1)
    print(root_node.has_loop)  # False
