


'''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_					(  _UpperCAmelCase	:	Any  )  ->			List[str]:
       _a    : Optional[Any]     					=[]
       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_					(  _UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	str  )  ->			Optional[int]:
       _a    : Optional[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 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			int:
       _a    : Union[str, Any]     					=[]
       token.append((F"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")  )
       return token


def 	SCREAMING_SNAKE_CASE_					(  )  ->			int:
       _a    : str     					=[]
       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_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	Optional[int]  )  ->			Tuple:
       _a    : Optional[int]     					="""imagenet-1k-id2label.json"""
       _a    : str     					=1000

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

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

       _a    : Optional[Any]     					=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":
              _a    : int     					=[1, 2, 10]

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

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

       _a    : Union[str, Any]     					=CvtForImageClassification(_UpperCAmelCase  )
       _a    : str     					=AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""  )
       _a    : Any     					=image_size
       _a    : Tuple     					=torch.load(_UpperCAmelCase    ,map_location=torch.device("""cpu"""  )  )

       _a    : Optional[Any]     					=OrderedDict()
       _a    : List[Any]     					=[]

       for idx in range(len(config.depth  )  ):
              if config.cls_token[idx]:
                     _a    : Union[str, Any]     					=list_of_state_dict + cls_token(_UpperCAmelCase  )
              _a    : List[Any]     					=list_of_state_dict + embeddings(_UpperCAmelCase  )
              for cnt in range(config.depth[idx]  ):
                     _a    : Tuple     					=list_of_state_dict + attention(_UpperCAmelCase    ,_UpperCAmelCase  )

       _a    : Tuple     					=list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(_UpperCAmelCase  )
       for i in range(len(_UpperCAmelCase  )  ):
              _a    : List[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__":
   A__:    str					    =       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.'''
   )

   A__:    int					    =       parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






'''simple docstring'''







class A__       :






      def __init__( self			:List[Any]	)       ->					None:




             '''simple docstring'''
             _a    : dict[str, TrieNode]     					={}  # Mapping from char to TrieNode
             _a    : List[str]     					=False






      def __UpperCAmelCase       ( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:list[str]	)       ->					None:




             '''simple docstring'''
             for word in words:
                    self.insert(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					None:




             '''simple docstring'''
             _a    : str     					=self
             for char in word:
                    if char not in curr.nodes:
                           _a    : Dict     					=TrieNode()
                    _a    : List[Any]     					=curr.nodes[char]
             _a    : int     					=True






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					bool:




             '''simple docstring'''
             _a    : int     					=self
             for char in word:
                    if char not in curr.nodes:
                           return False
                    _a    : List[Any]     					=curr.nodes[char]
             return curr.is_leaf






      def __UpperCAmelCase       ( self			:Dict     ,						SCREAMING_SNAKE_CASE			:str	)       ->					None:




             '''simple docstring'''

             def _delete(SCREAMING_SNAKE_CASE			:TrieNode     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:int	) -> bool:
                    if index == len(SCREAMING_SNAKE_CASE	):
                           # If word does not exist
                           if not curr.is_leaf:
                                  return False
                           _a    : Any     					=False
                           return len(curr.nodes	) == 0
                    _a    : int     					=word[index]
                    _a    : int     					=curr.nodes.get(SCREAMING_SNAKE_CASE	)
                    # If char not in current trie node
                    if not char_node:
                           return False
                    # Flag to check if node can be deleted
                    _a    : List[Any]     					=_delete(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						index + 1	)
                    if delete_curr:
                           del curr.nodes[char]
                           return len(curr.nodes	) == 0
                    return delete_curr

             _delete(self     ,						SCREAMING_SNAKE_CASE     ,						0	)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	TrieNode    ,_UpperCAmelCase	:	str  )  ->			None:
       if node.is_leaf:
              print(_UpperCAmelCase    ,end=""" """  )

       for key, value in node.nodes.items():
              print_words(_UpperCAmelCase    ,word + key  )


def 	SCREAMING_SNAKE_CASE_					(  )  ->			bool:
       _a    : List[str]     					="""banana bananas bandana band apple all beast""".split()
       _a    : List[Any]     					=TrieNode()
       root.insert_many(_UpperCAmelCase  )
       # print_words(root, "")
       assert all(root.find(_UpperCAmelCase  ) for word in words  )
       assert root.find("""banana"""  )
       assert not root.find("""bandanas"""  )
       assert not root.find("""apps"""  )
       assert root.find("""apple"""  )
       assert root.find("""all"""  )
       root.delete("""all"""  )
       assert not root.find("""all"""  )
       root.delete("""banana"""  )
       assert not root.find("""banana"""  )
       assert root.find("""bananas"""  )
       return True


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	bool  )  ->			None:
       print(str(_UpperCAmelCase  )    ,"""works!""" if passes else """doesn't work :("""  )


def 	SCREAMING_SNAKE_CASE_					(  )  ->			None:
       assert test_trie()


def 	SCREAMING_SNAKE_CASE_					(  )  ->			None:
       print_results("""Testing trie functionality"""    ,test_trie()  )


if __name__ == "__main__":
   main()


