



"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					''
  UpperCamelCase	:				Dict     						=					'hf-legacy'  # "hf://"" is reserved for hffs



  def __init__(						self :					Any       ,		lowerCAmelCase :					Optional[DatasetInfo] = None       ,		lowerCAmelCase :					Optional[str] = None       ,		**lowerCAmelCase :					Union[str, Any]       ,		)     ->					Dict:


     '''simple docstring'''




     super().__init__(self       ,		**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=repo_info
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=token
     SCREAMING_SNAKE_CASE_: int     			=None



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Dict:


     '''simple docstring'''




     if self.dir_cache is None:
        SCREAMING_SNAKE_CASE_: List[str]     			={}
        for hf_file in self.repo_info.siblings:
           # TODO(QL): add sizes
           SCREAMING_SNAKE_CASE_: Tuple     			={
               """name""": hf_file.rfilename,
               """size""": None,
               """type""": """file""",
           }
           self.dir_cache.update(
               {
                   str(lowerCAmelCase		): {"""name""": str(lowerCAmelCase		), """size""": None, """type""": """directory"""}
                   for d in list(PurePosixPath(hf_file.rfilename		).parents		)[:-1]
               }		)



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					str = "rb"       ,		**lowerCAmelCase :					str       ,		)     ->					List[str]:


     '''simple docstring'''




     if not isinstance(self.repo_info       ,		lowerCAmelCase		):
        raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=hf_hub_url(self.repo_info.id       ,		lowerCAmelCase       ,		revision=self.repo_info.sha		)
     return fsspec.open(
         lowerCAmelCase       ,		mode=lowerCAmelCase       ,		headers=get_authentication_headers_for_url(lowerCAmelCase       ,		use_auth_token=self.token		)       ,		client_kwargs={"""trust_env""": True}       ,		).open()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Tuple       ,		**lowerCAmelCase :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     self._get_dirs()
     SCREAMING_SNAKE_CASE_: str     			=self._strip_protocol(lowerCAmelCase		)
     if path in self.dir_cache:
        return self.dir_cache[path]
     else:
        raise FileNotFoundError(lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Tuple=False       ,		**lowerCAmelCase :					Optional[Any]		)     ->					Tuple:


     '''simple docstring'''




     self._get_dirs()
     SCREAMING_SNAKE_CASE_: int     			=PurePosixPath(path.strip("""/"""		)		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			={}
     for p, f in self.dir_cache.items():
        SCREAMING_SNAKE_CASE_: int     			=PurePosixPath(p.strip("""/"""		)		)
        SCREAMING_SNAKE_CASE_: Any     			=p.parent
        if root == path:
           SCREAMING_SNAKE_CASE_: Optional[Any]     			=f
     SCREAMING_SNAKE_CASE_: str     			=list(paths.values()		)
     if detail:
        return out
     else:
        return sorted(f["""name"""] for f in out		)








"""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()



