'''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


_lowercase		:					int													=		datasets.utils.logging.get_logger(__name__)





@dataclass
class        __magic_name__		(       datasets.BuilderConfig):
			UpperCamelCase__							=			None
			UpperCamelCase__							=			"utf-8"
			UpperCamelCase__							=			None
			UpperCamelCase__							=			10 << 20  # 10MB
			UpperCamelCase__							=			False
			UpperCamelCase__							=			"line"





class        __magic_name__		(       datasets.ArrowBasedBuilder):
			UpperCamelCase__							=			TextConfig







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										return datasets.DatasetInfo(features=self.config.features       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Optional[Any]       ):
										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}'''       )
										lowercase_   :     str									=			dl_manager.download_and_extract(self.config.data_files       )
										if isinstance(lowercase_							,     (str, list, tuple)       ):
																	lowercase_   :     Dict									=			data_files
																	if isinstance(lowercase_							,     lowercase_       ):
																								lowercase_   :     str									=			[files]
																	lowercase_   :     Any									=			[dl_manager.iter_files(lowercase_       ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,     gen_kwargs={"""files""": files}       )]
										lowercase_   :     List[Any]									=			[]
										for split_name, files in data_files.items():
																	if isinstance(lowercase_							,     lowercase_       ):
																								lowercase_   :     int									=			[files]
																	lowercase_   :     str									=			[dl_manager.iter_files(lowercase_       ) for file in files]
																	splits.append(datasets.SplitGenerator(name=lowercase_							,     gen_kwargs={"""files""": files}       )       )
										return splits







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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    Tuple       ):
										lowercase_   :     Optional[int]									=			list(self.config.features       ) if self.config.features is not None else ["""text"""]
										for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_       )       ):
																	# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
																	with open(lowercase_							,     encoding=self.config.encoding							,     errors=self.config.errors       ) as f:
																								if self.config.sample_by == "line":
																															lowercase_   :     Union[str, Any]									=			0
																															while True:
																																						lowercase_   :     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)
																																						lowercase_   :     List[Any]									=			StringIO(lowercase_       ).readlines()
																																						if not self.config.keep_linebreaks:
																																													lowercase_   :     Optional[Any]									=			[line.rstrip("""\n"""       ) for line in batch]
																																						lowercase_   :     Any									=			pa.Table.from_arrays([pa.array(lowercase_       )]							,     names=lowercase_       )
																																						# 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(lowercase_       )
																																						batch_idx += 1
																								elif self.config.sample_by == "paragraph":
																															lowercase_   :     Dict									=			0
																															lowercase_   :     Optional[int]									=			""""""
																															while True:
																																						lowercase_   :     List[Any]									=			f.read(self.config.chunksize       )
																																						if not new_batch:
																																													break
																																						batch += new_batch
																																						batch += f.readline()  # finish current line
																																						lowercase_   :     Union[str, Any]									=			batch.split("""\n\n"""       )
																																						lowercase_   :     Dict									=			pa.Table.from_arrays(
																																						    [pa.array([example for example in batch[:-1] if example]       )]							,     names=lowercase_       )
																																						# 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(lowercase_       )
																																						batch_idx += 1
																																						lowercase_   :     Any									=			batch[-1]
																															if batch:
																																						lowercase_   :     Optional[int]									=			pa.Table.from_arrays([pa.array([batch]       )]							,     names=lowercase_       )
																																						yield (file_idx, batch_idx), self._cast_table(lowercase_       )
																								elif self.config.sample_by == "document":
																															lowercase_   :     List[str]									=			f.read()
																															lowercase_   :     Optional[Any]									=			pa.Table.from_arrays([pa.array([text]       )]							,     names=lowercase_       )
																															yield file_idx, self._cast_table(lowercase_       )






'''simple docstring'''




_lowercase		:					List[str]													=		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

_lowercase		:					Union[str, Any]													=		[{"type": "code", "content": INSTALL_CONTENT}]





