



import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int	)->  Optional[int]:
    A__		 =					np.inf

    def set_batch_size(UpperCamelCase_				:       List[Any]	) -> None:
        nonlocal batch_size
        if isinstance(__UpperCamelCase ,       __UpperCamelCase	):
            A__		 =					min(__UpperCamelCase ,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS	)
        elif isinstance(__UpperCamelCase ,       __UpperCamelCase	):
            A__		 =					min(__UpperCamelCase ,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS	)
        elif isinstance(__UpperCamelCase ,       __UpperCamelCase	) and feature.dtype == "binary":
            A__		 =					min(__UpperCamelCase ,       config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS	)

    _visit(__UpperCamelCase ,       __UpperCamelCase	)

    return None if batch_size is np.inf else batch_size



class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = False ,						a__ = False ,						a__ = None ,						**a__ ,						):
         super().__init__(
             _SCREAMING_SNAKE_CASE ,						split=_SCREAMING_SNAKE_CASE ,						features=_SCREAMING_SNAKE_CASE ,						cache_dir=_SCREAMING_SNAKE_CASE ,						keep_in_memory=_SCREAMING_SNAKE_CASE ,						streaming=_SCREAMING_SNAKE_CASE ,						num_proc=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE ,						)
         A__		 =					path_or_paths if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
         A__		 =					_PACKAGED_DATASETS_MODULES['''parquet'''][1]
         A__		 =					Parquet(
             cache_dir=_SCREAMING_SNAKE_CASE ,						data_files=_SCREAMING_SNAKE_CASE ,						features=_SCREAMING_SNAKE_CASE ,						hash=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE ,						)




     def 		snake_case_		(				self):






         if self.streaming:
             A__		 =					self.builder.as_streaming_dataset(split=self.split)
         # Build regular (map-style) dataset
         else:
             A__		 =					None
             A__		 =					None
             A__		 =					None
             A__		 =					None

             self.builder.download_and_prepare(
                 download_config=_SCREAMING_SNAKE_CASE ,						download_mode=_SCREAMING_SNAKE_CASE ,						verification_mode=_SCREAMING_SNAKE_CASE ,						base_path=_SCREAMING_SNAKE_CASE ,						num_proc=self.num_proc ,						)
             A__		 =					self.builder.as_dataset(
                 split=self.split ,						verification_mode=_SCREAMING_SNAKE_CASE ,						in_memory=self.keep_in_memory)
         return dataset







class     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         A__		 =					dataset
         A__		 =					path_or_buf
         A__		 =					batch_size or get_writer_batch_size(dataset.features)
         A__		 =					parquet_writer_kwargs




     def 		snake_case_		(				self):
         A__		 =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf ,						(str, bytes, os.PathLike)):
             with open(self.path_or_buf ,						'''wb+''') as buffer:
                 A__		 =					self._write(file_obj=_SCREAMING_SNAKE_CASE ,						batch_size=_SCREAMING_SNAKE_CASE ,						**self.parquet_writer_kwargs)
         else:
             A__		 =					self._write(file_obj=self.path_or_buf ,						batch_size=_SCREAMING_SNAKE_CASE ,						**self.parquet_writer_kwargs)
         return written




     def 		snake_case_		(				self ,						a__ ,						a__ ,						**a__):
         A__		 =					0
         A__		 =					parquet_writer_kwargs.pop('''path_or_buf''' ,						_SCREAMING_SNAKE_CASE)
         A__		 =					self.dataset.features.arrow_schema

         A__		 =					pq.ParquetWriter(_SCREAMING_SNAKE_CASE ,						schema=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)

         for offset in logging.tqdm(
             range(0 ,						len(self.dataset) ,						_SCREAMING_SNAKE_CASE) ,						unit='''ba''' ,						disable=not logging.is_progress_bar_enabled() ,						desc='''Creating parquet from Arrow format''' ,						):
             A__		 =					query_table(
                 table=self.dataset._data ,						key=slice(_SCREAMING_SNAKE_CASE ,						offset + batch_size) ,						indices=self.dataset._indices if self.dataset._indices is not None else None ,						)
             writer.write_table(_SCREAMING_SNAKE_CASE)
             written += batch.nbytes
         writer.close()
         return written





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__    (					UpperCamelCase_				:       dict	)->  tuple:
    return (data["data"], data["target"])



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  XGBClassifier:
    A__		 =					XGBClassifier()
    classifier.fit(UpperCamelCase_ ,       UpperCamelCase_	)
    return classifier



def 		lowerCAmelCase__    (					)->  None:
    A__		 =					load_iris()
    A__ ,	A__		 =					data_handling(UpperCamelCase_	)
    A__ ,	A__ ,	A__ ,	A__		 =					train_test_split(
        UpperCamelCase_ ,       UpperCamelCase_ ,       test_size=0.25	)

    A__		 =					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    A__		 =					xgboost(UpperCamelCase_ ,       UpperCamelCase_	)

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


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()
