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	(  __snake_case						):
					_UpperCamelCase			     =     np.inf

					def set_batch_size(__snake_case						) -> None:
										nonlocal batch_size
										if isinstance(__snake_case  ,     __snake_case						):
															_UpperCamelCase			     =     min(__snake_case  ,     config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
										elif isinstance(__snake_case  ,     __snake_case						):
															_UpperCamelCase			     =     min(__snake_case  ,     config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS						)
										elif isinstance(__snake_case  ,     __snake_case						) and feature.dtype == "binary":
															_UpperCamelCase			     =     min(__snake_case  ,     config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS						)

					_visit(__snake_case  ,     __snake_case						)

					return None if batch_size is np.inf else batch_size
class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     List[str] ,			_A      :     NestedDataStructureLike[PathLike] ,			_A      :     Optional[NamedSplit] = None ,			_A      :     Optional[Features] = None ,			_A      :     str = None ,			_A      :     bool = False ,			_A      :     bool = False ,			_A      :     Optional[int] = None ,			**_A      :     Dict ,			):
												super().__init__(
												    _A ,			split=_A ,			features=_A ,			cache_dir=_A ,			keep_in_memory=_A ,			streaming=_A ,			num_proc=_A ,			**_A ,			)
												_UpperCamelCase			     =     path_or_paths if isinstance(_A ,			_A		) else {self.split: path_or_paths}
												_UpperCamelCase			     =     _PACKAGED_DATASETS_MODULES['''parquet'''][1]
												_UpperCamelCase			     =     Parquet(
												    cache_dir=_A ,			data_files=_A ,			features=_A ,			hash=_A ,			**_A ,			)







							def  UpperCamelCase_						(   self      :     List[str]		):
												# Build iterable dataset
												if self.streaming:
																	_UpperCamelCase			     =     self.builder.as_streaming_dataset(split=self.split		)
												# Build regular (map-style) dataset
												else:
																	_UpperCamelCase			     =     None
																	_UpperCamelCase			     =     None
																	_UpperCamelCase			     =     None
																	_UpperCamelCase			     =     None

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




class     lowerCAmelCase_     :







							def __init__(   self      :     Optional[Any] ,			_A      :     Dataset ,			_A      :     Union[PathLike, BinaryIO] ,			_A      :     Optional[int] = None ,			**_A      :     List[str] ,			):
												_UpperCamelCase			     =     dataset
												_UpperCamelCase			     =     path_or_buf
												_UpperCamelCase			     =     batch_size or get_writer_batch_size(dataset.features		)
												_UpperCamelCase			     =     parquet_writer_kwargs







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     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:
																						_UpperCamelCase			     =     self._write(file_obj=_A ,			batch_size=_A ,			**self.parquet_writer_kwargs		)
												else:
																	_UpperCamelCase			     =     self._write(file_obj=self.path_or_buf ,			batch_size=_A ,			**self.parquet_writer_kwargs		)
												return written







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     BinaryIO ,			_A      :     int ,			**_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     0
												_UpperCamelCase			     =     parquet_writer_kwargs.pop('''path_or_buf''' ,			_A		)
												_UpperCamelCase			     =     self.dataset.features.arrow_schema

												_UpperCamelCase			     =     pq.ParquetWriter(_A ,			schema=_A ,			**_A		)

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

import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version
def 					_snake_case	(  *__snake_case  ,     __snake_case = None  ,     __snake_case=True  ,     __snake_case=2						):
					from .. import __version__

					_UpperCamelCase			     =     take_from
					_UpperCamelCase			     =     ()
					if not isinstance(args[0]  ,     __snake_case						):
										_UpperCamelCase			     =     (args,)

					for attribute, version_name, message in args:
										if version.parse(version.parse(__snake_case						).base_version						) >= version.parse(__snake_case						):
															raise ValueError(
															    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
															    f""" version {__version__} is >= {version_name}"""						)

										_UpperCamelCase			     =     None
										if isinstance(__snake_case  ,     __snake_case						) and attribute in deprecated_kwargs:
															values += (deprecated_kwargs.pop(__snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
										elif hasattr(__snake_case  ,     __snake_case						):
															values += (getattr(__snake_case  ,     __snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
										elif deprecated_kwargs is None:
															_UpperCamelCase			     =     f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

										if warning is not None:
															_UpperCamelCase			     =     warning + ''' ''' if standard_warn else ''''''
															warnings.warn(warning + message  ,     __snake_case  ,     stacklevel=__snake_case						)

					if isinstance(__snake_case  ,     __snake_case						) and len(__snake_case						) > 0:
										_UpperCamelCase			     =     inspect.getouterframes(inspect.currentframe()						)[1]
										_UpperCamelCase			     =     call_frame.filename
										_UpperCamelCase			     =     call_frame.lineno
										_UpperCamelCase			     =     call_frame.function
										_UpperCamelCase     ,    _UpperCamelCase			     =     next(iter(deprecated_kwargs.items()						)						)
										raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""						)

					if len(__snake_case						) == 0:
										return
					elif len(__snake_case						) == 1:
										return values[0]
					return values
