




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


_lowerCamelCase	:		List[str]							       =   logging.get_logger(__name__)

_lowerCamelCase	:		Optional[int]							       =   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase	:		Optional[Any]							       =   {
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_lowerCamelCase	:		Dict							       =   {
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_lowerCamelCase	:		str							       =   {
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCamelCase	:		Any							       =   {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		Dict							       =   {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		Optional[int]							       =   {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_lowerCamelCase	:		Optional[Any]							       =   {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCamelCase	:		List[str]							       =   {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCamelCase	:		Tuple							       =   {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase	:		List[str]							       =   collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_lowerCamelCase	:		str							       =   collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_lowerCamelCase	:		Optional[int]							       =   r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """


@add_start_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    :

    '''simple docstring'''






    def __call__(			self      :	Union[str, Any]					, UpperCAmelCase__      :	List[Any]					, UpperCAmelCase__      :	Optional[str] = None					, UpperCAmelCase__      :	Optional[str] = None					, UpperCAmelCase__      :	Union[bool, str] = False					, UpperCAmelCase__      :	Union[bool, str] = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase__      :	Optional[bool] = None					, **UpperCAmelCase__      :	List[str]					, )						->BatchEncoding:





           '''simple docstring'''

           if titles is None and texts is None:
                  return super().__call__(
                      UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, max_length=UpperCAmelCase__					, return_tensors=UpperCAmelCase__					, return_attention_mask=UpperCAmelCase__					, **UpperCAmelCase__					, )
           elif titles is None or texts is None:
                  A__       =    titles if texts is None else texts
                  return super().__call__(
                      UpperCAmelCase__					, UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, max_length=UpperCAmelCase__					, return_tensors=UpperCAmelCase__					, return_attention_mask=UpperCAmelCase__					, **UpperCAmelCase__					, )
           A__       =    titles if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [titles]
           A__       =    texts if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [texts]
           A__       =    len(UpperCAmelCase__)
           A__       =    questions if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [questions] * n_passages
           if len(UpperCAmelCase__) != len(UpperCAmelCase__):
                  raise ValueError(
                      f"""There should be as many titles than texts but got {len(UpperCAmelCase__)} titles and {len(UpperCAmelCase__)} texts.""")
           A__       =    super().__call__(UpperCAmelCase__					, UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__)['''input_ids''']
           A__       =    super().__call__(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__)['''input_ids''']
           A__       =    {
               '''input_ids''': [
                   (encoded_question_and_title + encoded_text)[:max_length]
                   if max_length is not None and truncation
                   else encoded_question_and_title + encoded_text
                   for encoded_question_and_title, encoded_text in zip(UpperCAmelCase__					, UpperCAmelCase__)
               ]
           }
           if return_attention_mask is not False:
                  A__       =    []
                  for input_ids in encoded_inputs["input_ids"]:
                         attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
                  A__       =    attention_mask
           return self.pad(UpperCAmelCase__					, padding=UpperCAmelCase__					, max_length=UpperCAmelCase__					, return_tensors=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	BatchEncoding					, UpperCAmelCase__      :	DPRReaderOutput					, UpperCAmelCase__      :	int = 16					, UpperCAmelCase__      :	int = 64					, UpperCAmelCase__      :	int = 4					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

           A__       =    reader_input['''input_ids''']
           A__ ,							A__ ,							A__       =    reader_output[:3]
           A__       =    len(UpperCAmelCase__)
           A__       =    sorted(range(UpperCAmelCase__)					, reverse=UpperCAmelCase__					, key=relevance_logits.__getitem__)
           A__       =    []
           for doc_id in sorted_docs:
                  A__       =    list(input_ids[doc_id])
                  # assuming question & title information is at the beginning of the sequence
                  A__       =    sequence_ids.index(self.sep_token_id					, 2) + 1  # second sep id
                  if sequence_ids[-1] == self.pad_token_id:
                         A__       =    sequence_ids.index(self.pad_token_id)
                  else:
                         A__       =    len(UpperCAmelCase__)

                  A__       =    self._get_best_spans(
                      start_logits=start_logits[doc_id][passage_offset:sequence_len]					, end_logits=end_logits[doc_id][passage_offset:sequence_len]					, max_answer_length=UpperCAmelCase__					, top_spans=UpperCAmelCase__					, )
                  for start_index, end_index in best_spans:
                         start_index += passage_offset
                         end_index += passage_offset
                         nbest_spans_predictions.append(
                             DPRSpanPrediction(
                                 span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]					, relevance_score=relevance_logits[doc_id]					, doc_id=UpperCAmelCase__					, start_index=UpperCAmelCase__					, end_index=UpperCAmelCase__					, text=self.decode(sequence_ids[start_index : end_index + 1])					, ))
                  if len(UpperCAmelCase__) >= num_spans:
                         break
           return nbest_spans_predictions[:num_spans]





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	int					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

           A__       =    []
           for start_index, start_score in enumerate(UpperCAmelCase__):
                  for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                         scores.append(((start_index, start_index + answer_length), start_score + end_score))
           A__       =    sorted(UpperCAmelCase__					, key=lambda UpperCAmelCase__: x[1]					, reverse=UpperCAmelCase__)
           A__       =    []
           for (start_index, end_index), score in scores:
                  if start_index > end_index:
                         raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""")
                  A__       =    end_index - start_index + 1
                  if length > max_answer_length:
                         raise ValueError(f"""Span is too long: {length} > {max_answer_length}""")
                  if any(
                      start_index <= prev_start_index <= prev_end_index <= end_index
                      or prev_start_index <= start_index <= end_index <= prev_end_index
                      for (prev_start_index, prev_end_index) in chosen_span_intervals):
                         continue
                  chosen_span_intervals.append((start_index, end_index))

                  if len(UpperCAmelCase__) == top_spans:
                         break
           return chosen_span_intervals


@add_end_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      READER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      READER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase__							  =      ['''input_ids''', '''attention_mask''']








import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_lowerCamelCase	:		Optional[Any]							       =   datasets.utils.logging.get_logger(__name__)


@dataclass
class 				UpperCamelCase_    ( datasets.BuilderConfig     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      "utf-8"
    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      True  # deprecated
    UpperCAmelCase__							  =      None  # deprecated
    UpperCAmelCase__							  =      10 << 20  # 10MB
    UpperCAmelCase__							  =      None


class 				UpperCamelCase_    ( datasets.ArrowBasedBuilder     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      JsonConfig


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->str:





           '''simple docstring'''

           if self.config.block_size is not None:
                  logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''')
                  A__       =    self.config.block_size
           if self.config.use_threads is not True:
                  logger.warning(
                      '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''')
           if self.config.newlines_in_values is not None:
                  raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''')
           return datasets.DatasetInfo(features=self.config.features)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[Any])						->Dict:





           '''simple docstring'''

           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}""")
           A__       =    dl_manager.download_and_extract(self.config.data_files)
           if isinstance(UpperCAmelCase__					, (str, list, tuple)):
                  A__       =    data_files
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={'''files''': files})]
           A__       =    []
           for split_name, files in data_files.items():
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  splits.append(datasets.SplitGenerator(name=UpperCAmelCase__					, gen_kwargs={'''files''': files}))
           return splits


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	pa.Table)						->pa.Table:





           '''simple docstring'''

           if self.config.features is not None:
                  # adding missing columns
                  for column_name in set(self.config.features) - set(pa_table.column_names):
                         A__       =    self.config.features.arrow_schema.field(UpperCAmelCase__).type
                         A__       =    pa_table.append_column(UpperCAmelCase__					, pa.array([None] * len(UpperCAmelCase__)					, type=UpperCAmelCase__))
                  # more expensive cast to support nested structures with keys in a different order
                  # allows str <-> int/float or str to Audio for example
                  A__       =    table_cast(UpperCAmelCase__					, self.config.features.arrow_schema)
           return pa_table





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Tuple)						->str:





           '''simple docstring'''

           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__)):
                  # If the file is one json object and if we need to look at the list of items in one specific field
                  if self.config.field is not None:
                         with open(UpperCAmelCase__					, encoding=self.config.encoding					, errors=self.config.encoding_errors) as f:
                                A__       =    json.load(UpperCAmelCase__)

                         # We keep only the field we are interested in
                         A__       =    dataset[self.config.field]

                         # We accept two format: a list of dicts or a dict of lists
                         if isinstance(UpperCAmelCase__					, (list, tuple)):
                                A__       =    set().union(*[row.keys() for row in dataset])
                                A__       =    {col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                         else:
                                A__       =    dataset
                         A__       =    pa.Table.from_pydict(UpperCAmelCase__)
                         yield file_idx, self._cast_table(UpperCAmelCase__)

                  # If the file has one json object per line
                  else:
                         with open(UpperCAmelCase__					, '''rb''') as f:
                                A__       =    0
                                # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                                # Set a default minimum value of 16kB if the chunk size is really small
                                A__       =    max(self.config.chunksize // 32					, 16 << 10)
                                A__       =    (
                                    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                                )
                                while True:
                                       A__       =    f.read(self.config.chunksize)
                                       if not batch:
                                              break
                                       # Finish current line
                                       try:
                                              batch += f.readline()
                                       except (AttributeError, io.UnsupportedOperation):
                                              batch += readline(UpperCAmelCase__)
                                       # PyArrow only accepts utf-8 encoded bytes
                                       if self.config.encoding != "utf-8":
                                              A__       =    batch.decode(self.config.encoding					, errors=UpperCAmelCase__).encode('''utf-8''')
                                       try:
                                              while True:
                                                     try:
                                                            A__       =    paj.read_json(
                                                                io.BytesIO(UpperCAmelCase__)					, read_options=paj.ReadOptions(block_size=UpperCAmelCase__))
                                                            break
                                                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                            if (
                                                                isinstance(UpperCAmelCase__					, pa.ArrowInvalid)
                                                                and "straddling" not in str(UpperCAmelCase__)
                                                                or block_size > len(UpperCAmelCase__)
                                                            ):
                                                                   raise
                                                            else:
                                                                   # Increase the block size in case it was too small.
                                                                   # The block size will be reset for the next file.
                                                                   logger.debug(
                                                                       f"""Batch of {len(UpperCAmelCase__)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""")
                                                                   block_size *= 2
                                       except pa.ArrowInvalid as e:
                                              try:
                                                     with open(
                                                         UpperCAmelCase__					, encoding=self.config.encoding					, errors=self.config.encoding_errors) as f:
                                                            A__       =    json.load(UpperCAmelCase__)
                                              except json.JSONDecodeError:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                     raise e
                                              # If possible, parse the file as a list of json objects and exit the loop
                                              if isinstance(UpperCAmelCase__					, UpperCAmelCase__):  # list is the only sequence type supported in JSON
                                                     try:
                                                            A__       =    set().union(*[row.keys() for row in dataset])
                                                            A__       =    {col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                                                            A__       =    pa.Table.from_pydict(UpperCAmelCase__)
                                                     except (pa.ArrowInvalid, AttributeError) as e:
                                                            logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                            raise ValueError(f"""Not able to read records in the JSON file at {file}.""") from None
                                                     yield file_idx, self._cast_table(UpperCAmelCase__)
                                                     break
                                              else:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                     raise ValueError(
                                                         f"""Not able to read records in the JSON file at {file}. """
                                                         f"""You should probably indicate the field of the JSON file containing your records. """
                                                         f"""This JSON file contain the following fields: {str(list(dataset.keys()))}. """
                                                         f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """) from None
                        # 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(UpperCAmelCase__)
                                       batch_idx += 1


