


"""simple docstring"""







import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


__a      			= datasets.utils.logging.get_logger(__name__)
@dataclass
class 				lowerCamelCase							(      datasets.BuilderConfig			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   None




class 				lowerCamelCase							(      datasets.ArrowBasedBuilder			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   ArrowConfig






							def        lowerCAmelCase  (    self			:  List[str]					):
													return datasets.DatasetInfo(features=self.config.features					)






							def        lowerCAmelCase  (    self			:  str		,			lowerCamelCase_			:  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}"""					)
													UpperCAmelCase__												=     dl_manager.download_and_extract(self.config.data_files					)
													if isinstance(lowerCamelCase_		,			(str, list, tuple)					):
																			UpperCAmelCase__												=     data_files
																			if isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			UpperCAmelCase__												=     [dl_manager.iter_files(lowerCamelCase_					) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,			gen_kwargs={'''files''': files}					)]
													UpperCAmelCase__												=     []
													for split_name, files in data_files.items():
																			if isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			UpperCAmelCase__												=     [dl_manager.iter_files(lowerCamelCase_					) for file in files]
																			# Infer features is they are stoed in the arrow schema
																			if self.info.features is None:
																									for file in itertools.chain.from_iterable(lowerCamelCase_					):
																															with open(lowerCamelCase_		,			'''rb'''					) as f:
																																					UpperCAmelCase__												=     datasets.Features.from_arrow_schema(pa.ipc.open_stream(lowerCamelCase_					).schema					)
																															break
																			splits.append(datasets.SplitGenerator(name=lowerCamelCase_		,			gen_kwargs={'''files''': files}					)					)
													return splits






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  pa.Table					):
													if self.info.features is not None:
																			# more expensive cast to support nested features with keys in a different order
																			# allows str <-> int/float or str to Audio for example
																			UpperCAmelCase__												=     table_cast(lowerCamelCase_		,			self.info.features.arrow_schema					)
													return pa_table






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  str					):
													for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase_					)					):
																			with open(lowerCamelCase_		,			'''rb'''					) as f:
																									try:
																															for batch_idx, record_batch in enumerate(pa.ipc.open_stream(lowerCamelCase_					)					):
																																					UpperCAmelCase__												=     pa.Table.from_batches([record_batch]					)
																																					# 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 f"""{file_idx}_{batch_idx}""", self._cast_table(lowerCamelCase_					)
																									except ValueError as e:
																															logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase_					)}: {e}"""					)
																															raise



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizerFast"]


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez import BarthezTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez_fast import BarthezTokenizerFast

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)