"""simple docstring"""







import warnings
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  UpperCamelCase	(    _a						):
   lowercase							=						["""image_processor""", """tokenizer"""]
   lowercase							=						"""LayoutLMv2ImageProcessor"""
   lowercase							=						("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
   def __init__(    self  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase    )			->   Union[str, Any]:


          '''simple docstring'''


          if "feature_extractor" in kwargs:
                 warnings.warn(
                     'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                     ' instead.'  ,snake_case_  ,)
                 lowercase_			:       List[Any]									=			kwargs.pop('feature_extractor'    )

          lowercase_			:       List[str]									=			image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
                 raise ValueError('You need to specify an `image_processor`.'    )
          if tokenizer is None:
                 raise ValueError('You need to specify a `tokenizer`.'    )

          super().__init__(snake_case_  ,snake_case_    )
   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = True  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = 0  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   BatchEncoding:


          '''simple docstring'''


          if self.image_processor.apply_ocr and (boxes is not None):
                 raise ValueError(
                     'You cannot provide bounding boxes '
                     'if you initialized the image processor with apply_ocr set to True.'    )

          if self.image_processor.apply_ocr and (word_labels is not None):
                 raise ValueError(
                     'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'    )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
                 raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'    )

          # first, apply the image processor
          lowercase_			:       Any									=			self.image_processor(images=snake_case_  ,return_tensors=snake_case_    )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
                 if isinstance(snake_case_  ,snake_case_    ):
                        lowercase_			:       List[Any]									=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
                 lowercase_			:       List[Any]									=			features['words']

          lowercase_			:       int									=			self.tokenizer(
              text=text if text is not None else features['words']  ,text_pair=text_pair if text_pair is not None else None  ,boxes=boxes if boxes is not None else features['boxes']  ,word_labels=snake_case_  ,add_special_tokens=snake_case_  ,padding=snake_case_  ,truncation=snake_case_  ,max_length=snake_case_  ,stride=snake_case_  ,pad_to_multiple_of=snake_case_  ,return_token_type_ids=snake_case_  ,return_attention_mask=snake_case_  ,return_overflowing_tokens=snake_case_  ,return_special_tokens_mask=snake_case_  ,return_offsets_mapping=snake_case_  ,return_length=snake_case_  ,verbose=snake_case_  ,return_tensors=snake_case_  ,**snake_case_  ,)

          # add pixel values
          lowercase_			:       Optional[Any]									=			features.pop('pixel_values'    )
          if return_overflowing_tokens is True:
                 lowercase_			:       Optional[Any]									=			self.get_overflowing_images(snake_case_  ,encoded_inputs['overflow_to_sample_mapping']    )
          lowercase_			:       Optional[int]									=			images

          return encoded_inputs
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   int:


          '''simple docstring'''


          lowercase_			:       Union[str, Any]									=			[]
          for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]    )

          if len(snake_case_    ) != len(snake_case_    ):
                 raise ValueError(
                     'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                     f''' {len(snake_case_    )} and {len(snake_case_    )}'''    )

          return images_with_overflow
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   int:


          '''simple docstring'''


          return self.tokenizer.batch_decode(*snake_case_  ,**snake_case_    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Optional[Any]:


          '''simple docstring'''


          return self.tokenizer.decode(*snake_case_  ,**snake_case_    )
   @property
   def     _UpperCAmelCase     (    self    )			->   Dict:


          '''simple docstring'''


          return ["input_ids", "bbox", "attention_mask", "image"]
   @property
   def     _UpperCAmelCase     (    self    )			->   Tuple:


          '''simple docstring'''


          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,snake_case_  ,)
          return self.image_processor_class







   @property
   def     _UpperCAmelCase     (    self    )			->   Dict:


          '''simple docstring'''


          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,snake_case_  ,)
          return self.image_processor


"""simple docstring"""







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

__SCREAMING_SNAKE_CASE								="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

__SCREAMING_SNAKE_CASE								=concatenate_datasets
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadManager
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager

