




import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__      :			str           =					datasets.utils.logging.get_logger(__name__)




@dataclass
class 			a__       (					datasets.BuilderConfig      ):
     a		:			Optional[datasets.Features]							=	None







class 			a__       (					datasets.ArrowBasedBuilder      ):
     a		:			str							=	ArrowConfig







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Optional[Any]:



          '''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}''' )
          a      =      dl_manager.download_and_extract(self.config.data_files )
          if isinstance(A  ,		(str, list, tuple) ):
               a      =      data_files
               if isinstance(A  ,		A ):
                    a      =      [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               a      =      [dl_manager.iter_files(A ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,		gen_kwargs={"files": files} )]
          a      =      []
          for split_name, files in data_files.items():
               if isinstance(A  ,		A ):
                    a      =      [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               a      =      [dl_manager.iter_files(A ) 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(A ):
                         with open(A  ,		"rb" ) as f:
                              a      =      datasets.Features.from_arrow_schema(pa.ipc.open_stream(A ).schema )
                         break
               splits.append(datasets.SplitGenerator(name=A  ,		gen_kwargs={"files": files} ) )
          return splits







     def 			lowerCAmelCase_				(	self  ,		A )  ->    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
               a      =      table_cast(A  ,		self.info.features.arrow_schema )
          return pa_table







     def 			lowerCAmelCase_				(	self  ,		A )  ->    int:



          '''simple docstring'''



          for file_idx, file in enumerate(itertools.chain.from_iterable(A ) ):
               with open(A  ,		"rb" ) as f:
                    try:
                         for batch_idx, record_batch in enumerate(pa.ipc.open_stream(A ) ):
                              a      =      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(A )
                    except ValueError as e:
                         logger.error(F'''Failed to read file \'{file}\' with error {type(A )}: {e}''' )
                         raise







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase__      :			Union[str, Any]           =					logging.get_logger(__name__)




class 			a__       (					UpperCamelCase__      ):







     def __init__(	self  ,		*A  ,		**A )  ->    None:



          '''simple docstring'''



          warnings.warn(
              "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV2ImageProcessor instead."  ,		A  ,		)
          super().__init__(*A  ,		**A )

