


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A      :    List[Any]		=					datasets.utils.logging.get_logger(__name__)







@dataclass
class 			_lowercase (							datasets.BuilderConfig):


       """simple docstring"""




       A__											=			1_00_00
       A__											=			None
       A__											=			None







class 			_lowercase (							datasets.ArrowBasedBuilder):


       """simple docstring"""




       A__											=			ParquetConfig





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return datasets.DatasetInfo(features=self.config.features       )





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			Tuple       ):


             '''simple docstring'''



             if not self.config.data_files:
                   raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"       )
             lowerCamelCase__		:				Any    =  dl_manager.download_and_extract(self.config.data_files       )
             if isinstance(__lowerCamelCase					,      (str, list, tuple)       ):
                   lowerCamelCase__		:				Optional[int]    =  data_files
                   if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                         lowerCamelCase__		:				int    =  [files]
                   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                   lowerCamelCase__		:				Optional[int]    =  [dl_manager.iter_files(__lowerCamelCase       ) for file in files]
                   return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,      gen_kwargs={"files": files}       )]
             lowerCamelCase__		:				Dict    =  []
             for split_name, files in data_files.items():
                   if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                         lowerCamelCase__		:				Optional[Any]    =  [files]
                   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                   lowerCamelCase__		:				Union[str, Any]    =  [dl_manager.iter_files(__lowerCamelCase       ) for file in files]
                   # Infer features is they are stoed in the arrow schema
                   if self.info.features is None:
                         for file in itertools.chain.from_iterable(__lowerCamelCase       ):
                               with open(__lowerCamelCase					,      "rb"       ) as f:
                                     lowerCamelCase__		:				Tuple    =  datasets.Features.from_arrow_schema(pq.read_schema(__lowerCamelCase       )       )
                               break
                   splits.append(datasets.SplitGenerator(name=__lowerCamelCase					,      gen_kwargs={"files": files}       )       )
             return splits





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			pa.Table       ):


             '''simple docstring'''



             if self.info.features is not None:
                   # more expensive cast to support nested features with keys in a different order
                   # allows str <-> int/float or str to Audio for example
                   lowerCamelCase__		:				str    =  table_cast(__lowerCamelCase					,      self.info.features.arrow_schema       )
             return pa_table





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  self.info.features.arrow_schema if self.info.features is not None else None
             if self.info.features is not None and self.config.columns is not None:
                   if sorted(field.name for field in schema       ) != sorted(self.config.columns       ):
                         raise ValueError(
                             f"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"       )
             for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase       )       ):
                   with open(__lowerCamelCase					,      "rb"       ) as f:
                         lowerCamelCase__		:				List[Any]    =  pq.ParquetFile(__lowerCamelCase       )
                         try:
                               for batch_idx, record_batch in enumerate(
                                   parquet_file.iter_batches(batch_size=self.config.batch_size					,      columns=self.config.columns       )       ):
                                     lowerCamelCase__		:				str    =  pa.Table.from_batches([record_batch]       )
                                     # Uncomment for debugging (will print the Arrow table size and elements)
                                     # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                     # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                     yield f"{file_idx}_{batch_idx}", self._cast_table(__lowerCamelCase       )
                         except ValueError as e:
                               logger.error(f"Failed to read file '{file}' with error {type(__lowerCamelCase       )}: {e}"       )
                               raise






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A      :    List[str]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			Optional[int]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             warnings.warn(
                 "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use BeitImageProcessor instead."					,      __lowerCamelCase					,      )
             super().__init__(*__lowerCamelCase					,      **__lowerCamelCase       )


