





"""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       __A (     a_  :Features)						->						Optional[int]:
				__a	:  List[str]         =					np.inf

				def set_batch_size(a_  :FeatureType) -> None:
								nonlocal batch_size
								if isinstance(a_				,    a_):
												__a	:  str         =					min(a_				,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS)
								elif isinstance(a_				,    a_):
												__a	:  int         =					min(a_				,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS)
								elif isinstance(a_				,    a_) and feature.dtype == "binary":
												__a	:  List[Any]         =					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 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = False		,     _UpperCAmelCase = False		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									super().__init__(
									    _UpperCAmelCase		,     split=_UpperCAmelCase		,     features=_UpperCAmelCase		,     cache_dir=_UpperCAmelCase		,     keep_in_memory=_UpperCAmelCase		,     streaming=_UpperCAmelCase		,     num_proc=_UpperCAmelCase		,     **_UpperCAmelCase		,     )
									__a	:  List[Any]         =					path_or_paths if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else {self.split: path_or_paths}
									__a	:  str         =					_PACKAGED_DATASETS_MODULES['''parquet'''][1]
									__a	:  int         =					Parquet(
									    cache_dir=_UpperCAmelCase		,     data_files=_UpperCAmelCase		,     features=_UpperCAmelCase		,     hash=_UpperCAmelCase		,     **_UpperCAmelCase		,     )







					def 	_lowerCamelCase						(  self       ):
									# Build iterable dataset
									if self.streaming:
													__a	:  Optional[int]         =					self.builder.as_streaming_dataset(split=self.split       )
									# Build regular (map-style) dataset
									else:
													__a	:  Dict         =					None
													__a	:  Tuple         =					None
													__a	:  Any         =					None
													__a	:  List[str]         =					None

													self.builder.download_and_prepare(
													    download_config=_UpperCAmelCase		,     download_mode=_UpperCAmelCase		,     verification_mode=_UpperCAmelCase		,     base_path=_UpperCAmelCase		,     num_proc=self.num_proc		,     )
													__a	:  List[str]         =					self.builder.as_dataset(
													    split=self.split		,     verification_mode=_UpperCAmelCase		,     in_memory=self.keep_in_memory       )
									return dataset






class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									__a	:  str         =					dataset
									__a	:  Union[str, Any]         =					path_or_buf
									__a	:  Union[str, Any]         =					batch_size or get_writer_batch_size(dataset.features       )
									__a	:  List[Any]         =					parquet_writer_kwargs







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					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:
																	__a	:  Optional[int]         =					self._write(file_obj=_UpperCAmelCase		,     batch_size=_UpperCAmelCase		,     **self.parquet_writer_kwargs       )
									else:
													__a	:  List[str]         =					self._write(file_obj=self.path_or_buf		,     batch_size=_UpperCAmelCase		,     **self.parquet_writer_kwargs       )
									return written







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):
									__a	:  Optional[int]         =					0
									__a	:  Tuple         =					parquet_writer_kwargs.pop('''path_or_buf'''		,     _UpperCAmelCase       )
									__a	:  Union[str, Any]         =					self.dataset.features.arrow_schema

									__a	:  List[Any]         =					pq.ParquetWriter(_UpperCAmelCase		,     schema=_UpperCAmelCase		,     **_UpperCAmelCase       )

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






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	     =					{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InstructBlipQFormerModel''',
		    '''InstructBlipPreTrainedModel''',
		    '''InstructBlipForConditionalGeneration''',
		    '''InstructBlipVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    InstructBlipConfig,
		    InstructBlipQFormerConfig,
		    InstructBlipVisionConfig,
		)
		from .processing_instructblip import InstructBlipProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InstructBlipForConditionalGeneration,
				    InstructBlipPreTrainedModel,
				    InstructBlipQFormerModel,
				    InstructBlipVisionModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)