

import argparse
import os
import re


_UpperCAmelCase	:     Optional[Any]		       = """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase	:     Dict		       = re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
_UpperCAmelCase	:     List[Any]		       = re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__ = False   ):







							'''simple docstring'''






							with open(UpperCamelCase__     ,							'r'     ,							encoding='utf-8'   ) as f:
														snake_case_           =					f.read()

							snake_case_           =					content.split('\n'   )
							snake_case_           =					[]
							snake_case_           =					0
							while line_idx < len(UpperCamelCase__   ):
														if _re_intro_mapping.search(lines[line_idx]   ) is not None:
																					snake_case_           =					len(re.search(r'^(\s*)\S'     ,							lines[line_idx]   ).groups()[0]   ) + 8
																					# Start of a new mapping!
																					while not lines[line_idx].startswith(' ' * indent + '('   ):
																												new_lines.append(lines[line_idx]   )
																												line_idx += 1

																					snake_case_           =					[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			snake_case_           =					line_idx
																																			while not lines[line_idx].startswith(' ' * indent + ')'   ):
																																										line_idx += 1
																																			blocks.append('\n'.join(lines[start_idx : line_idx + 1]   )   )
																												else:
																																			blocks.append(lines[line_idx]   )
																												line_idx += 1

																					# Sort blocks by their identifiers
																					snake_case_           =					sorted(UpperCamelCase__     ,							key=lambda UpperCamelCase__   : _re_identifier.search(UpperCamelCase__   ).groups()[0]   )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]   )
																					line_idx += 1

							if overwrite:
														with open(UpperCamelCase__     ,							'w'     ,							encoding='utf-8'   ) as f:
																					f.write('\n'.join(UpperCamelCase__   )   )
							elif "\n".join(UpperCamelCase__   ) != content:
														return True





def       __lowerCamelCase			(				UpperCamelCase__ = False   ):







							'''simple docstring'''






							snake_case_           =					[os.path.join(UpperCamelCase__     ,							UpperCamelCase__   ) for f in os.listdir(UpperCamelCase__   ) if f.endswith('.py'   )]
							snake_case_           =					[sort_auto_mapping(UpperCamelCase__     ,							overwrite=UpperCamelCase__   ) for fname in fnames]

							if not overwrite and any(UpperCamelCase__   ):
														snake_case_           =					[f for f, d in zip(UpperCamelCase__     ,							UpperCamelCase__   ) if d]
														raise ValueError(
														    F'''The following files have auto mappings that need sorting: {", ".join(UpperCamelCase__   )}. Run `make style` to fix'''
														    ' this.'   )


if __name__ == "__main__":
					_UpperCAmelCase	:     List[str]		       = argparse.ArgumentParser()
					parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
					_UpperCAmelCase	:     Union[str, Any]		       = parser.parse_args()

					sort_all_auto_mappings(not args.check_only)







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			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.inf

							def set_batch_size(UpperCamelCase__   ) -> None:
														nonlocal batch_size
														if isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) and feature.dtype == "binary":
																					snake_case_           =					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 				lowercase       ( lowercase_    ):




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




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

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







class 				lowercase       :




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




				def 					a							(      self ):
											snake_case_           =					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_           =					self._write(file_obj=snake_case   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											else:
																		snake_case_           =					self._write(file_obj=self.path_or_buf   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											return written




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     **snake_case ):
											snake_case_           =					0
											snake_case_           =					parquet_writer_kwargs.pop('path_or_buf'   ,     snake_case )
											snake_case_           =					self.dataset.features.arrow_schema

											snake_case_           =					pq.ParquetWriter(snake_case   ,     schema=snake_case   ,     **snake_case )

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




