


"""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[int]				)			->		int:


				"""simple docstring"""





				snake_case					:   Tuple         =					np.inf

				def set_batch_size(lowercase						:						Dict				) -> None:
								nonlocal batch_size
								if isinstance(UpperCamelCase__		,							UpperCamelCase__				):
												snake_case					:   Optional[Any]         =					min(UpperCamelCase__		,							config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
								elif isinstance(UpperCamelCase__		,							UpperCamelCase__				):
												snake_case					:   str         =					min(UpperCamelCase__		,							config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
								elif isinstance(UpperCamelCase__		,							UpperCamelCase__				) and feature.dtype == "binary":
												snake_case					:   int         =					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 _lowerCAmelCase       (   lowercase_			):







			def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = False		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	List[Any]:

							'''simple docstring'''




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







			def 				lowerCamelCase	(			self		)			->	Optional[Any]:

							'''simple docstring'''




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

											self.builder.download_and_prepare(
											    download_config=UpperCamelCase__		,			download_mode=UpperCamelCase__		,			verification_mode=UpperCamelCase__		,			base_path=UpperCamelCase__		,			num_proc=self.num_proc		,			)
											snake_case					:   Optional[int]         =					self.builder.as_dataset(
											    split=self.split		,			verification_mode=UpperCamelCase__		,			in_memory=self.keep_in_memory		)
							return dataset







class _lowerCAmelCase       :







			def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	List[Any]:

							'''simple docstring'''




							snake_case					:   str         =					dataset
							snake_case					:   List[Any]         =					path_or_buf
							snake_case					:   Optional[int]         =					batch_size or get_writer_batch_size(dataset.features		)
							snake_case					:   List[str]         =					parquet_writer_kwargs







			def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

							'''simple docstring'''




							snake_case					:   str         =					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:
															snake_case					:   Optional[int]         =					self._write(file_obj=UpperCamelCase__		,			batch_size=UpperCamelCase__		,			**self.parquet_writer_kwargs		)
							else:
											snake_case					:   List[Any]         =					self._write(file_obj=self.path_or_buf		,			batch_size=UpperCamelCase__		,			**self.parquet_writer_kwargs		)
							return written







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	Any:

							'''simple docstring'''




							snake_case					:   Union[str, Any]         =					0
							snake_case					:   Optional[Any]         =					parquet_writer_kwargs.pop("path_or_buf"		,			UpperCamelCase__		)
							snake_case					:   List[Any]         =					self.dataset.features.arrow_schema

							snake_case					:   List[str]         =					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"		,			):
											snake_case					:   Optional[int]         =					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"""



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__snake_case         =					subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__snake_case         =					(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

__snake_case         =					"""|""".join(sys.argv[1:])
__snake_case         =					re.compile(RF'''^({joined_dirs}).*?\.py$''')

__snake_case         =					[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





