





'''simple docstring'''




import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


__UpperCAmelCase         =   datasets.utils.logging.get_logger(__name__)




@dataclass
class       UpperCamelCase__			(			datasets.BuilderConfig	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					None
					SCREAMING_SNAKE_CASE__						=					"utf-8"
					SCREAMING_SNAKE_CASE__						=					None
					SCREAMING_SNAKE_CASE__						=					10 << 20  # 10MB
					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					"line"







class       UpperCamelCase__			(			datasets.ArrowBasedBuilder	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					TextConfig


					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							return datasets.DatasetInfo(features=self.config.features		)


					def lowerCamelCase_	(		self							:	Optional[Any]     ,		lowerCamelCase_							:	str		):

							'''simple docstring'''



							if not self.config.data_files:
									raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''		)
							SCREAMING_SNAKE_CASE				:							List[str]						=	dl_manager.download_and_extract(self.config.data_files		)
							if isinstance(lowerCamelCase_     ,		(str, list, tuple)		):
									SCREAMING_SNAKE_CASE				:							int						=	data_files
									if isinstance(lowerCamelCase_     ,		lowerCamelCase_		):
											SCREAMING_SNAKE_CASE				:							List[Any]						=	[files]
									SCREAMING_SNAKE_CASE				:							Tuple						=	[dl_manager.iter_files(lowerCamelCase_		) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,		gen_kwargs={"""files""": files}		)]
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[]
							for split_name, files in data_files.items():
									if isinstance(lowerCamelCase_     ,		lowerCamelCase_		):
											SCREAMING_SNAKE_CASE				:							int						=	[files]
									SCREAMING_SNAKE_CASE				:							Tuple						=	[dl_manager.iter_files(lowerCamelCase_		) for file in files]
									splits.append(datasets.SplitGenerator(name=lowerCamelCase_     ,		gen_kwargs={"""files""": files}		)		)
							return splits


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	pa.Table		):

							'''simple docstring'''



							if self.config.features is not None:
									SCREAMING_SNAKE_CASE				:							Dict						=	self.config.features.arrow_schema
									if all(not require_storage_cast(lowerCamelCase_		) for feature in self.config.features.values()		):
											# cheaper cast
											SCREAMING_SNAKE_CASE				:							Any						=	pa_table.cast(lowerCamelCase_		)
									else:
											# more expensive cast; allows str <-> int/float or str to Audio for example
											SCREAMING_SNAKE_CASE				:							int						=	table_cast(lowerCamelCase_     ,		lowerCamelCase_		)
									return pa_table
							else:
									return pa_table.cast(pa.schema({"""text""": pa.string()}		)		)



					def lowerCamelCase_	(		self							:	List[Any]     ,		lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	list(self.config.features		) if self.config.features is not None else ["""text"""]
							for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase_		)		):
									# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
									with open(lowerCamelCase_     ,		encoding=self.config.encoding     ,		errors=self.config.errors		) as f:
											if self.config.sample_by == "line":
													SCREAMING_SNAKE_CASE				:							Any						=	0
													while True:
															SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	f.read(self.config.chunksize		)
															if not batch:
																	break
															batch += f.readline()  # finish current line
															# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
															SCREAMING_SNAKE_CASE				:							Any						=	StringIO(lowerCamelCase_		).readlines()
															if not self.config.keep_linebreaks:
																	SCREAMING_SNAKE_CASE				:							Any						=	[line.rstrip("""\n"""		) for line in batch]
															SCREAMING_SNAKE_CASE				:							Optional[Any]						=	pa.Table.from_arrays([pa.array(lowerCamelCase_		)]     ,		names=lowerCamelCase_		)
															# Uncomment for debugging (will print the Arrow table size and elements)
															# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
															# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
															yield (file_idx, batch_idx), self._cast_table(lowerCamelCase_		)
															batch_idx += 1
											elif self.config.sample_by == "paragraph":
													SCREAMING_SNAKE_CASE				:							Any						=	0
													SCREAMING_SNAKE_CASE				:							int						=	""""""
													while True:
															SCREAMING_SNAKE_CASE				:							List[str]						=	f.read(self.config.chunksize		)
															if not new_batch:
																	break
															batch += new_batch
															batch += f.readline()  # finish current line
															SCREAMING_SNAKE_CASE				:							List[Any]						=	batch.split("""\n\n"""		)
															SCREAMING_SNAKE_CASE				:							str						=	pa.Table.from_arrays(
															    [pa.array([example for example in batch[:-1] if example]		)]     ,		names=lowerCamelCase_		)
															# Uncomment for debugging (will print the Arrow table size and elements)
															# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
															# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
															yield (file_idx, batch_idx), self._cast_table(lowerCamelCase_		)
															batch_idx += 1
															SCREAMING_SNAKE_CASE				:							int						=	batch[-1]
													if batch:
															SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	pa.Table.from_arrays([pa.array([batch]		)]     ,		names=lowerCamelCase_		)
															yield (file_idx, batch_idx), self._cast_table(lowerCamelCase_		)
											elif self.config.sample_by == "document":
													SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	f.read()
													SCREAMING_SNAKE_CASE				:							Optional[int]						=	pa.Table.from_arrays([pa.array([text]		)]     ,		names=lowerCamelCase_		)
													yield file_idx, self._cast_table(lowerCamelCase_		)










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__UpperCAmelCase         =   logging.get_logger(__name__)




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					def __init__(		self							:	Dict     ,		*lowerCamelCase_							:	Optional[int]     ,		**lowerCamelCase_							:	Optional[int]		):

							'''simple docstring'''



							warnings.warn(
							    """The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use DetrImageProcessor instead."""     ,		lowerCamelCase_     ,		)
							super().__init__(*lowerCamelCase_     ,		**lowerCamelCase_		)



