

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      snake_case_				(__A					:						Features					)      ->     Optional[int]:
   __lowerCAmelCase  :      Optional[Any]    =						np.inf

   def set_batch_size(__A					:						FeatureType					) -> None:
      nonlocal batch_size
      if isinstance(__A ,       __A					):
         __lowerCAmelCase  :      List[str]    =						min(__A ,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
      elif isinstance(__A ,       __A					):
         __lowerCAmelCase  :      Union[str, Any]    =						min(__A ,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
      elif isinstance(__A ,       __A					) and feature.dtype == "binary":
         __lowerCAmelCase  :      Tuple    =						min(__A ,       config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

   _visit(__A ,       __A					)

   return None if batch_size is np.inf else batch_size



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Dict	,							lowerCAmelCase			:   NestedDataStructureLike[PathLike]	,							lowerCAmelCase			:   Optional[NamedSplit] = None	,							lowerCAmelCase			:   Optional[Features] = None	,							lowerCAmelCase			:   str = None	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Optional[int] = None	,							**lowerCAmelCase			:   str	,							)							->     Any:






     """simple docstring"""







     super().__init__(
         lowerCAmelCase	,							split=lowerCAmelCase	,							features=lowerCAmelCase	,							cache_dir=lowerCAmelCase	,							keep_in_memory=lowerCAmelCase	,							streaming=lowerCAmelCase	,							num_proc=lowerCAmelCase	,							**lowerCAmelCase	,							)
     __lowerCAmelCase  :      int    =						path_or_paths if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) else {self.split: path_or_paths}
     __lowerCAmelCase  :      Union[str, Any]    =						_PACKAGED_DATASETS_MODULES["""parquet"""][1]
     __lowerCAmelCase  :      Optional[Any]    =						Parquet(
         cache_dir=lowerCAmelCase	,							data_files=lowerCAmelCase	,							features=lowerCAmelCase	,							hash=lowerCAmelCase	,							**lowerCAmelCase	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     List[Any]:






     """simple docstring"""







     if self.streaming:
        __lowerCAmelCase  :      Any    =						self.builder.as_streaming_dataset(split=self.split   )
     # Build regular (map-style) dataset
     else:
        __lowerCAmelCase  :      Optional[Any]    =						None
        __lowerCAmelCase  :      Tuple    =						None
        __lowerCAmelCase  :      Optional[int]    =						None
        __lowerCAmelCase  :      Dict    =						None

        self.builder.download_and_prepare(
            download_config=lowerCAmelCase	,							download_mode=lowerCAmelCase	,							verification_mode=lowerCAmelCase	,							base_path=lowerCAmelCase	,							num_proc=self.num_proc	,							)
        __lowerCAmelCase  :      List[Any]    =						self.builder.as_dataset(
            split=self.split	,							verification_mode=lowerCAmelCase	,							in_memory=self.keep_in_memory   )
     return dataset






class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   Dataset	,							lowerCAmelCase			:   Union[PathLike, BinaryIO]	,							lowerCAmelCase			:   Optional[int] = None	,							**lowerCAmelCase			:   str	,							)							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						dataset
     __lowerCAmelCase  :      Optional[int]    =						path_or_buf
     __lowerCAmelCase  :      Optional[int]    =						batch_size or get_writer_batch_size(dataset.features   )
     __lowerCAmelCase  :      Optional[Any]    =						parquet_writer_kwargs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						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:
           __lowerCAmelCase  :      Any    =						self._write(file_obj=lowerCAmelCase	,							batch_size=lowerCAmelCase	,							**self.parquet_writer_kwargs   )
     else:
        __lowerCAmelCase  :      Union[str, Any]    =						self._write(file_obj=self.path_or_buf	,							batch_size=lowerCAmelCase	,							**self.parquet_writer_kwargs   )
     return written







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   BinaryIO	,							lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Union[str, Any]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						0
     __lowerCAmelCase  :      List[Any]    =						parquet_writer_kwargs.pop("""path_or_buf"""	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						self.dataset.features.arrow_schema

     __lowerCAmelCase  :      Union[str, Any]    =						pq.ParquetWriter(lowerCAmelCase	,							schema=lowerCAmelCase	,							**lowerCAmelCase   )

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





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







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


