



"""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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Union[str, 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      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=[]
   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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=[]
   token.append((f'''cvt.encoder.stages.{idx}.cls_token''', """stage2.cls_token""") )
   return token





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Tuple     			=[]
   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      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			="""imagenet-1k-id2label.json"""
   SCREAMING_SNAKE_CASE_: int     			=1000

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

   SCREAMING_SNAKE_CASE_: str     			=idalabel
   SCREAMING_SNAKE_CASE_: Optional[Any]     			={v: k for k, v in idalabel.items()}

   SCREAMING_SNAKE_CASE_: List[str]     			=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":
      SCREAMING_SNAKE_CASE_: Any     			=[1, 2, 10]

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

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

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=CvtForImageClassification(lowercase__ )
   SCREAMING_SNAKE_CASE_: List[str]     			=AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=image_size
   SCREAMING_SNAKE_CASE_: str     			=torch.load(lowercase__							,			map_location=torch.device("""cpu""" ) )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=OrderedDict()
   SCREAMING_SNAKE_CASE_: Optional[int]     			=[]

   for idx in range(len(config.depth ) ):
      if config.cls_token[idx]:
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=list_of_state_dict + cls_token(lowercase__ )
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=list_of_state_dict + embeddings(lowercase__ )
      for cnt in range(config.depth[idx] ):
         SCREAMING_SNAKE_CASE_: str     			=list_of_state_dict + attention(lowercase__							,			lowercase__ )

   SCREAMING_SNAKE_CASE_: Optional[int]     			=list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(lowercase__ )
   for i in range(len(lowercase__ ) ):
      SCREAMING_SNAKE_CASE_: Any     			=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     :   Dict     =			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."""
   )

   _UpperCAmelCase     :   Union[str, Any]     =			parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)








"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier





def      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

   # Display the confusion matrix of the classifier with both training and test sets
   ConfusionMatrixDisplay.from_estimator(
       lowercase							,			lowercase							,			lowercase							,			display_labels=lowercase							,			cmap="""Blues"""							,			normalize="""true"""							,			)
   plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
   plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



