
"""simple docstring"""






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__			(		__magic_name__      )						->Optional[int]:
			__lowercase       =       np.inf

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

			_visit(__magic_name__			,   __magic_name__      )

			return None if batch_size is np.inf else batch_size

class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False  ,    _lowerCamelCase = False  ,    _lowerCamelCase = None  ,    **_lowerCamelCase  ,    )			->							Tuple:




										'''simple docstring'''
										super().__init__(
										    _lowerCamelCase  ,    split=_lowerCamelCase  ,    features=_lowerCamelCase  ,    cache_dir=_lowerCamelCase  ,    keep_in_memory=_lowerCamelCase  ,    streaming=_lowerCamelCase  ,    num_proc=_lowerCamelCase  ,    **_lowerCamelCase  ,    )
										__lowercase       =       path_or_paths if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else {self.split: path_or_paths}
										__lowercase       =       _PACKAGED_DATASETS_MODULES["parquet"][1]
										__lowercase       =       Parquet(
										    cache_dir=_lowerCamelCase  ,    data_files=_lowerCamelCase  ,    features=_lowerCamelCase  ,    hash=_lowerCamelCase  ,    **_lowerCamelCase  ,    )





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




										'''simple docstring'''
										# Build iterable dataset
										if self.streaming:
													__lowercase       =       self.builder.as_streaming_dataset(split=self.split       )
										# Build regular (map-style) dataset
										else:
													__lowercase       =       None
													__lowercase       =       None
													__lowercase       =       None
													__lowercase       =       None

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





class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    **_lowerCamelCase  ,    )			->							int:




										'''simple docstring'''
										__lowercase       =       dataset
										__lowercase       =       path_or_buf
										__lowercase       =       batch_size or get_writer_batch_size(dataset.features       )
										__lowercase       =       parquet_writer_kwargs
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										__lowercase       =       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:
																__lowercase       =       self._write(file_obj=_lowerCamelCase  ,    batch_size=_lowerCamelCase  ,    **self.parquet_writer_kwargs       )
										else:
													__lowercase       =       self._write(file_obj=self.path_or_buf  ,    batch_size=_lowerCamelCase  ,    **self.parquet_writer_kwargs       )
										return written





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       0
										__lowercase       =       parquet_writer_kwargs.pop("path_or_buf"  ,    _lowerCamelCase       )
										__lowercase       =       self.dataset.features.arrow_schema

										__lowercase       =       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"  ,    ):
													__lowercase       =       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







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase							=				{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				['''Mask2FormerImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Mask2FormerForUniversalSegmentation''',
					    '''Mask2FormerModel''',
					    '''Mask2FormerPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure)





