'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= np.inf

				def set_batch_size(_SCREAMING_SNAKE_CASE      :Optional[Any]       ) -> None:
								nonlocal batch_size
								if isinstance(UpperCamelCase__    ,	UpperCamelCase__       ):
												SCREAMING_SNAKE_CASE  :						List[Any]      					= min(UpperCamelCase__    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS       )
								elif isinstance(UpperCamelCase__    ,	UpperCamelCase__       ):
												SCREAMING_SNAKE_CASE  :						List[str]      					= min(UpperCamelCase__    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS       )
								elif isinstance(UpperCamelCase__    ,	UpperCamelCase__       ) and feature.dtype == "binary":
												SCREAMING_SNAKE_CASE  :						List[Any]      					= min(UpperCamelCase__    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS       )

				_visit(UpperCamelCase__    ,	UpperCamelCase__       )

				return None if batch_size is np.inf else batch_size


class 	a__     (				lowercase_	):


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

					"""simple docstring"""

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






	def 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					if self.streaming:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.builder.as_streaming_dataset(split=self.split      )
					# Build regular (map-style) dataset
					else:
									SCREAMING_SNAKE_CASE  :						int      					= None
									SCREAMING_SNAKE_CASE  :						Tuple      					= None
									SCREAMING_SNAKE_CASE  :						List[str]      					= None
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= None

									self.builder.download_and_prepare(
									    download_config=__UpperCAmelCase,					download_mode=__UpperCAmelCase,					verification_mode=__UpperCAmelCase,					base_path=__UpperCAmelCase,					num_proc=self.num_proc,					)
									SCREAMING_SNAKE_CASE  :						int      					= self.builder.as_dataset(
									    split=self.split,					verification_mode=__UpperCAmelCase,					in_memory=self.keep_in_memory      )
					return dataset



class 	a__     :


	def __init__(self : Dict,					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any = None,					**__UpperCAmelCase : Tuple,					)     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= dataset
					SCREAMING_SNAKE_CASE  :						int      					= path_or_buf
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= batch_size or get_writer_batch_size(dataset.features      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= parquet_writer_kwargs


	def 				lowercase__  (self : str      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= 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:
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self._write(file_obj=__UpperCAmelCase,					batch_size=__UpperCAmelCase,					**self.parquet_writer_kwargs      )
					else:
									SCREAMING_SNAKE_CASE  :						Any      					= self._write(file_obj=self.path_or_buf,					batch_size=__UpperCAmelCase,					**self.parquet_writer_kwargs      )
					return written






	def 				lowercase__  (self : str,					__UpperCAmelCase : Dict,					__UpperCAmelCase : List[Any],					**__UpperCAmelCase : Dict      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= 0
					SCREAMING_SNAKE_CASE  :						str      					= parquet_writer_kwargs.pop('''path_or_buf''',					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.dataset.features.arrow_schema

					SCREAMING_SNAKE_CASE  :						Optional[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''',					):
									SCREAMING_SNAKE_CASE  :						List[str]      					= 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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




