


'''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 				lowerCAmelCase__	(    lowerCamelCase					:      dict       ):
       return (data["data"], data["target"])






def 				lowerCAmelCase__	(    lowerCamelCase					:      np.ndarray  ,lowerCamelCase					:      np.ndarray       ):
       _A					:     List[str]     = XGBClassifier()
       classifier.fit(lowerCamelCase  ,lowerCamelCase       )
       return classifier






def 				lowerCAmelCase__	(    ):
       _A					:     str     = load_iris()
       _A  ,			_A					:     Union[str, Any]     = data_handling(lowerCamelCase       )
       _A  ,			_A  ,			_A  ,			_A					:     int     = train_test_split(
           lowerCamelCase  ,lowerCamelCase  ,test_size=0.25       )

       _A					:     str     = iris['target_names']

       # Create an XGBoost Classifier from the training data
       _A					:     Any     = xgboost(lowerCamelCase  ,lowerCamelCase       )

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


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()










'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A				:					Optional[int]    						=			logging.get_logger(__name__)

A				:					Union[str, Any]    						=			torch.device('''cpu''')






def 				lowerCAmelCase__	(    ):
       _A					:     Tuple     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A					:     Dict     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       )
       return im






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]       )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]       )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]       )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict       ):
       _A					:     Union[str, Any]     = dct.pop(lowerCamelCase       )
       _A					:     List[str]     = val






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]       ):
       _A					:     Optional[Any]     = []
       for k in state_dict.keys():
              _A					:     Optional[int]     = k
              if ".pwconv" in k:
                     _A					:     str     = k_new.replace('.pwconv'  ,'.point_wise_conv'       )
              if ".dwconv" in k:
                     _A					:     Any     = k_new.replace('.dwconv'  ,'.depth_wise_conv'       )
              if ".Proj." in k:
                     _A					:     Optional[Any]     = k_new.replace('.Proj.'  ,'.proj.'       )
              if "patch_embed" in k_new:
                     _A					:     Optional[int]     = k_new.replace('patch_embed'  ,'swiftformer.patch_embed.patch_embedding'       )
              if "network" in k_new:
                     _A					:     Tuple     = k_new.split('.'       )
                     if ls[2].isdigit():
                            _A					:     List[Any]     = 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
                     else:
                            _A					:     List[str]     = k_new.replace('network'  ,'swiftformer.encoder.network'       )
              rename_keys.append((k, k_new)       )
       return rename_keys






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       _A					:     Any     = 1000
       _A					:     int     = 'huggingface/label-files'
       _A					:     List[Any]     = 'imagenet-1k-id2label.json'
       _A					:     Optional[int]     = json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'       )  ,'r'       )       )
       _A					:     Dict     = {int(lowerCamelCase       ): v for k, v in idalabel.items()}
       _A					:     Optional[int]     = idalabel
       _A					:     Any     = {v: k for k, v in idalabel.items()}

       # size of the architecture
       if swiftformer_name == "swiftformer_xs":
              _A					:     Optional[Any]     = [3, 3, 6, 4]
              _A					:     Optional[int]     = [48, 56, 112, 220]

       elif swiftformer_name == "swiftformer_s":
              _A					:     List[Any]     = [3, 3, 9, 6]
              _A					:     Tuple     = [48, 64, 168, 224]

       elif swiftformer_name == "swiftformer_l1":
              _A					:     int     = [4, 3, 10, 5]
              _A					:     int     = [48, 96, 192, 384]

       elif swiftformer_name == "swiftformer_l3":
              _A					:     Optional[Any]     = [4, 4, 12, 6]
              _A					:     Any     = [64, 128, 320, 512]

       # load state_dict of original model, remove and rename some keys
       if original_ckpt:
              if original_ckpt.startswith('https'       ):
                     _A					:     Optional[Any]     = torch.hub.load_state_dict_from_url(lowerCamelCase  ,map_location='cpu'  ,check_hash=lowerCamelCase       )
              else:
                     _A					:     Union[str, Any]     = torch.load(lowerCamelCase  ,map_location='cpu'       )
       _A					:     Union[str, Any]     = checkpoint

       _A					:     List[str]     = create_rename_keys(lowerCamelCase       )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

       # load HuggingFace model
       _A					:     str     = SwiftFormerForImageClassification(lowerCamelCase       ).eval()
       hf_model.load_state_dict(lowerCamelCase       )

       # prepare test inputs
       _A					:     Any     = prepare_img()
       _A					:     Optional[int]     = ViTImageProcessor.from_pretrained('preprocessor_config'       )
       _A					:     Any     = processor(images=lowerCamelCase  ,return_tensors='pt'       )

       # compare outputs from both models
       _A					:     int     = get_expected_output(lowerCamelCase       )
       _A					:     Optional[int]     = hf_model(inputs['pixel_values']       ).logits

       assert hf_logits.shape == torch.Size([1, 1000]       )
       assert torch.allclose(hf_logits[0, 0:5]  ,lowerCamelCase  ,atol=1E-3       )

       Path(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
       print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'       )
       hf_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
   A				:					Dict    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--swiftformer_name''',
       default='''swiftformer_xs''',
       choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
       type=str,
       help='''Name of the SwiftFormer model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default='''./converted_outputs/''',
       type=str,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

   A				:					List[str]    						=			parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






