




'''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	:    str      						=['image_processor', 'tokenizer']
       lowercase	:    Union[str, Any]      						='LayoutLMv3ImageProcessor'
       lowercase	:    List[str]      						=('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')






       def __init__(							self,		lowerCAmelCase=None,		lowerCAmelCase=None,		**lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                         ''' instead.''',		UpperCamelCase__,		)
                     lowerCamelCase_							=kwargs.pop('''feature_extractor'''  )

              lowerCamelCase_							=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__(UpperCamelCase__,		UpperCamelCase__  )






       def __call__(							self,		lowerCAmelCase,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		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,		):



              """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.'''  )

              # first, apply the image processor
              lowerCamelCase_							=self.image_processor(images=UpperCamelCase__,		return_tensors=UpperCamelCase__  )

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

              lowerCamelCase_							=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=UpperCamelCase__,		add_special_tokens=UpperCamelCase__,		padding=UpperCamelCase__,		truncation=UpperCamelCase__,		max_length=UpperCamelCase__,		stride=UpperCamelCase__,		pad_to_multiple_of=UpperCamelCase__,		return_token_type_ids=UpperCamelCase__,		return_attention_mask=UpperCamelCase__,		return_overflowing_tokens=UpperCamelCase__,		return_special_tokens_mask=UpperCamelCase__,		return_offsets_mapping=UpperCamelCase__,		return_length=UpperCamelCase__,		verbose=UpperCamelCase__,		return_tensors=UpperCamelCase__,		**UpperCamelCase__,		)

              # add pixel values
              lowerCamelCase_							=features.pop('''pixel_values'''  )
              if return_overflowing_tokens is True:
                     lowerCamelCase_							=self.get_overflowing_images(UpperCamelCase__,		encoded_inputs['''overflow_to_sample_mapping''']  )
              lowerCamelCase_							=images

              return encoded_inputs






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=[]
              for sample_idx in overflow_to_sample_mapping:
                     images_with_overflow.append(images[sample_idx]  )

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

              return images_with_overflow






       def      lowercase__     (							self,		*lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







              return self.tokenizer.batch_decode(*UpperCamelCase__,		**UpperCamelCase__  )






       def      lowercase__     (							self,		*lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







              return self.tokenizer.decode(*UpperCamelCase__,		**UpperCamelCase__  )






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return ["input_ids", "bbox", "attention_mask", "pixel_values"]






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







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






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







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






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCAmelCase			:     Any           =   [
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def a__    (       A_=True					):






  '''simple docstring'''





  if with_config:
    return [
        {
            "testcase_name": d["dataset"] + "/" + d["config_name"],
            "dataset": d["dataset"],
            "config_name": d["config_name"],
        }
        for d in DATASETS_ON_HF_GCP
    ]
  else:
    return [
        {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
    ]




@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_A     )     )
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		None
  a__           =		None
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    with TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       dataset_module_factory(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )

      __magic_name__				     =       import_main_class(dataset_module.module_path			,							dataset=UpperCamelCase__    )

      __magic_name__				     =       builder_cls(
          cache_dir=UpperCamelCase__			,							config_name=UpperCamelCase__			,							hash=dataset_module.hash			,							)

      __magic_name__				     =       """/""".join(
          [
              HF_GCP_BASE_URL,
              builder_instance._relative_data_dir(with_hash=UpperCamelCase__    ).replace(os.sep			,							"""/"""    ),
              config.DATASET_INFO_FILENAME,
          ]    )
      __magic_name__				     =       cached_path(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )
      self.assertTrue(os.path.exists(UpperCamelCase__    )    )







@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
  __magic_name__				     =       None
  builder_instance.download_and_prepare()
  __magic_name__				     =       builder_instance.as_dataset()
  assert ds





@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path,     dataset=A_					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  __magic_name__				     =       builder_instance.as_streaming_dataset()
  assert ds
  assert isinstance(A_,     A_					)
  assert "train" in ds
  assert isinstance(ds["""train"""],     A_					)
  assert next(iter(ds["""train"""]					)					)

