
"""simple docstring"""





# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__magic_name__       =			"2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
							raise ImportWarning(
							    "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
							)

if version.parse(pyarrow.__version__).major < 8:
							raise ImportWarning(
							    "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
							    "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
							)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__magic_name__       =			concatenate_datasets
__magic_name__       =			DownloadConfig
__magic_name__       =			DownloadManager
__magic_name__       =			DownloadMode
__magic_name__       =			DownloadConfig
__magic_name__       =			DownloadMode
__magic_name__       =			DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager






"""simple docstring"""





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  List[Any]						   =		'''BridgeTowerImageProcessor'''
		__lowercase							:  Tuple						   =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				super().__init__(lowerCAmelCase__							,  lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = self.tokenizer(
				    text=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  max_length=lowerCAmelCase__							,  stride=lowerCAmelCase__							,  pad_to_multiple_of=lowerCAmelCase__							,  return_token_type_ids=lowerCAmelCase__							,  return_attention_mask=lowerCAmelCase__							,  return_overflowing_tokens=lowerCAmelCase__							,  return_special_tokens_mask=lowerCAmelCase__							,  return_offsets_mapping=lowerCAmelCase__							,  return_length=lowerCAmelCase__							,  verbose=lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__							,  )
				# add pixel_values + pixel_mask
				__SCREAMING_SNAKE_CASE            = self.image_processor(
				    lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  do_normalize=lowerCAmelCase__							,  do_center_crop=lowerCAmelCase__							,  **lowerCAmelCase__)
				encoding.update(lowerCAmelCase__)

				return encoding




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.tokenizer.model_input_names
				__SCREAMING_SNAKE_CASE            = self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




