



"""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_(   lowercase_     :							Optional[Any]							)	->       Optional[int]:
				_lowerCamelCase        =				np.inf

				def set_batch_size(lowercase_     :							Optional[int]							) -> None:
								nonlocal batch_size
								if isinstance(__snake_case						,   __snake_case							):
												_lowerCamelCase        =				min(__snake_case						,   config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS							)
								elif isinstance(__snake_case						,   __snake_case							):
												_lowerCamelCase        =				min(__snake_case						,   config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS							)
								elif isinstance(__snake_case						,   __snake_case							) and feature.dtype == "binary":
												_lowerCamelCase        =				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_(   A__    ):





					'''simple docstring'''



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





					def 			snake_case__      (    self   ):
									# Build iterable dataset
									if self.streaming:
													_lowerCamelCase        =				self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
													_lowerCamelCase        =				None
													_lowerCamelCase        =				None
													_lowerCamelCase        =				None
													_lowerCamelCase        =				None

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


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       **lowerCamelCase__				,       ):
									_lowerCamelCase        =				dataset
									_lowerCamelCase        =				path_or_buf
									_lowerCamelCase        =				batch_size or get_writer_batch_size(dataset.features   )
									_lowerCamelCase        =				parquet_writer_kwargs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				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        =				self._write(file_obj=__a				,       batch_size=__a				,       **self.parquet_writer_kwargs   )
									else:
													_lowerCamelCase        =				self._write(file_obj=self.path_or_buf				,       batch_size=__a				,       **self.parquet_writer_kwargs   )
									return written





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				0
									_lowerCamelCase        =				parquet_writer_kwargs.pop('''path_or_buf'''				,       __a   )
									_lowerCamelCase        =				self.dataset.features.arrow_schema

									_lowerCamelCase        =				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'''				,       ):
													_lowerCamelCase        =				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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						str    							=      ['''VisionEncoderDecoderModel''']

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Dict    							=      ['''TFVisionEncoderDecoderModel''']

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
			from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
