
"""simple docstring"""




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


_A     :			Optional[Any]  						=			logging.get_logger(__name__)

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

_A     :			Optional[int]  						=			{
    """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"""
        ),
    },
}
_A     :			Optional[int]  						=			{
    """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"""
        ),
    },
}
_A     :			int  						=			{
    """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"""
        ),
    },
}

_A     :			Tuple  						=			{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
_A     :			str  						=			{
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
_A     :			Union[str, Any]  						=			{
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


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

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


_A     :			List[Any]  						=			collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_A     :			Dict  						=			collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_A     :			str  						=			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(a_		)
class 						a__		:
   def __call__(     self      ,       _a      ,       _a = None      ,       _a = None      ,       _a = False      ,       _a = False      ,       _a = None      ,       _a = None      ,       _a = None      ,       **_a      ,       ):
          if titles is None and texts is None:
                 return super().__call__(
                     _a      ,       padding=_a      ,       truncation=_a      ,       max_length=_a      ,       return_tensors=_a      ,       return_attention_mask=_a      ,       **_a      ,       )
          elif titles is None or texts is None:
                 lowercase       :							str									=				titles if texts is None else texts
                 return super().__call__(
                     _a      ,       _a      ,       padding=_a      ,       truncation=_a      ,       max_length=_a      ,       return_tensors=_a      ,       return_attention_mask=_a      ,       **_a      ,       )
          lowercase       :							str									=				titles if not isinstance(_a      ,       _a							) else [titles]
          lowercase       :							str									=				texts if not isinstance(_a      ,       _a							) else [texts]
          lowercase       :							Optional[int]									=				len(_a							)
          lowercase       :							Union[str, Any]									=				questions if not isinstance(_a      ,       _a							) else [questions] * n_passages
          if len(_a							) != len(_a							):
                 raise ValueError(
                     f"""There should be as many titles than texts but got {len(_a							)} titles and {len(_a							)} texts."""							)
          lowercase       :							str									=				super().__call__(_a      ,       _a      ,       padding=_a      ,       truncation=_a							)["input_ids"]
          lowercase       :							Tuple									=				super().__call__(_a      ,       add_special_tokens=_a      ,       padding=_a      ,       truncation=_a							)["input_ids"]
          lowercase       :							str									=				{
              "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(_a      ,       _a							)
              ]
          }
          if return_attention_mask is not False:
                 lowercase       :							Tuple									=				[]
                 for input_ids in encoded_inputs["input_ids"]:
                        attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
                 lowercase       :							Optional[int]									=				attention_mask
          return self.pad(_a      ,       padding=_a      ,       max_length=_a      ,       return_tensors=_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a = 16      ,       _a = 64      ,       _a = 4      ,       ):
          lowercase       :							Tuple									=				reader_input["input_ids"]
          lowercase						,       lowercase						,       lowercase       :							Optional[Any]									=				reader_output[:3]
          lowercase       :							List[Any]									=				len(_a							)
          lowercase       :							Optional[Any]									=				sorted(range(_a							)      ,       reverse=_a      ,       key=relevance_logits.__getitem__							)
          lowercase       :							List[DPRReaderOutput]									=				[]
          for doc_id in sorted_docs:
                 lowercase       :							str									=				list(input_ids[doc_id]							)
                 # assuming question & title information is at the beginning of the sequence
                 lowercase       :							Optional[int]									=				sequence_ids.index(self.sep_token_id      ,       2							) + 1  # second sep id
                 if sequence_ids[-1] == self.pad_token_id:
                        lowercase       :							Optional[Any]									=				sequence_ids.index(self.pad_token_id							)
                 else:
                        lowercase       :							str									=				len(_a							)

                 lowercase       :							Tuple									=				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=_a      ,       top_spans=_a      ,       )
                 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=_a      ,       start_index=_a      ,       end_index=_a      ,       text=self.decode(sequence_ids[start_index : end_index + 1]							)      ,       )							)
                 if len(_a							) >= num_spans:
                        break
          return nbest_spans_predictions[:num_spans]

   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       ):
          lowercase       :							Any									=				[]
          for start_index, start_score in enumerate(_a							):
                 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)							)
          lowercase       :							Optional[int]									=				sorted(_a      ,       key=lambda _a							: x[1]      ,       reverse=_a							)
          lowercase       :							List[Any]									=				[]
          for (start_index, end_index), score in scores:
                 if start_index > end_index:
                        raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
                 lowercase       :							Any									=				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(_a							) == top_spans:
                        break
          return chosen_span_intervals


@add_end_docstrings(a_		)
class 						a__		( a_,     a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							READER_PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							READER_PRETRAINED_INIT_CONFIGURATION
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]




"""simple docstring"""




# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_A     :			List[str]  						=			get_logger(__name__)

class 						a__		:
   __lowerCAmelCase				=							"""dummy_data"""
   __lowerCAmelCase				=							"""datasets"""
   __lowerCAmelCase				=							False
   def __init__(     self      ,       _a      ,       _a      ,       _a      ,       _a = None      ,       _a = False      ,       _a = True      ,       _a = None      ,       ):
          lowercase       :							int									=				0
          lowercase       :							Optional[Any]									=				dataset_name
          lowercase       :							List[str]									=				cache_dir
          lowercase       :							Union[str, Any]									=				use_local_dummy_data
          lowercase       :							str									=				config
          # download_callbacks take a single url as input
          lowercase       :							List[Callable]									=				download_callbacks or []
          # if False, it doesn't load existing files and it returns the paths of the dummy files relative
          # to the dummy_data zip file root
          lowercase       :							List[Any]									=				load_existing_dummy_data

          # TODO(PVP, QL) might need to make this more general
          lowercase       :							Tuple									=				str(_a							)
          # to be downloaded
          lowercase       :							Tuple									=				None
          lowercase       :							List[Any]									=				None
   @property
   def      __magic_name__     (     self							):
          if self._dummy_file is None:
                 lowercase       :							Optional[int]									=				self.download_dummy_data()
          return self._dummy_file
   @property
   def      __magic_name__     (     self							):
          if self.config is not None:
                 # structure is dummy / config_name / version_name
                 return os.path.join("dummy"      ,       self.config.name      ,       self.version_name							)
          # structure is dummy / version_name
          return os.path.join("dummy"      ,       self.version_name							)
   @property
   def      __magic_name__     (     self							):
          return os.path.join(self.dummy_data_folder      ,       "dummy_data.zip"							)
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				(
              self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
          )

          lowercase       :							str									=				cached_path(
              _a      ,       cache_dir=self.cache_dir      ,       extract_compressed_file=_a      ,       force_extract=_a							)

          return os.path.join(_a      ,       self.dummy_file_name							)
   @property
   def      __magic_name__     (     self							):
          return os.path.join(self.datasets_scripts_dir      ,       self.dataset_name      ,       self.dummy_zip_file							)
   @property
   def      __magic_name__     (     self							):
          if self._bucket_url is None:
                 lowercase       :							Dict									=				hf_github_url(self.dataset_name      ,       self.dummy_zip_file.replace(os.sep      ,       "/"							)							)
          return self._bucket_url
   @property
   def      __magic_name__     (     self							):
          # return full path if its a dir
          if os.path.isdir(self.dummy_file							):
                 return self.dummy_file
          # else cut off path to file -> example `xsum`.
          return "/".join(self.dummy_file.replace(os.sep      ,       "/"							).split("/"							)[:-1]							)
   def      __magic_name__     (     self      ,       _a      ,       *_a							):
          if self.load_existing_dummy_data:
                 # dummy data is downloaded and tested
                 lowercase       :							Optional[int]									=				self.dummy_file
          else:
                 # dummy data cannot be downloaded and only the path to dummy file is returned
                 lowercase       :							Optional[Any]									=				self.dummy_file_name

          # special case when data_url is a dict
          if isinstance(_a      ,       _a							):
                 return self.create_dummy_data_dict(_a      ,       _a							)
          elif isinstance(_a      ,       (list, tuple)							):
                 return self.create_dummy_data_list(_a      ,       _a							)
          else:
                 return self.create_dummy_data_single(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       *_a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       *_a      ,       **_a							):
          return path
   def      __magic_name__     (     self							):
          return {}
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				{}
          for key, single_urls in data_url.items():
                 for download_callback in self.download_callbacks:
                        if isinstance(_a      ,       _a							):
                               for single_url in single_urls:
                                      download_callback(_a							)
                        else:
                               lowercase       :							Union[str, Any]									=				single_urls
                               download_callback(_a							)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 if isinstance(_a      ,       _a							):
                        lowercase       :							Any									=				[os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							) for x in single_urls]
                 else:
                        lowercase       :							int									=				single_urls
                        lowercase       :							Tuple									=				os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							)
                 lowercase       :							List[str]									=				value

          # make sure that values are unique
          if all(isinstance(_a      ,       _a							) for i in dummy_data_dict.values()							) and len(set(dummy_data_dict.values()							)							) < len(
              dummy_data_dict.values()							):
                 # append key to value to make its name unique
                 lowercase       :							str									=				{key: value + key for key, value in dummy_data_dict.items()}

          return dummy_data_dict
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Union[str, Any]									=				[]
          # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
          lowercase       :							Any									=				all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"      ,       _a							)							) for url in data_url							)
          lowercase       :							List[Any]									=				all(
              url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"							) for url in data_url							)
          if data_url and (is_tf_records or is_pubmed_records):
                 lowercase       :							Tuple									=				[data_url[0]] * len(_a							)
          for single_url in data_url:
                 for download_callback in self.download_callbacks:
                        download_callback(_a							)
                 # we force the name of each key to be the last file / folder name of the url path
                 # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 lowercase       :							Union[str, Any]									=				os.path.join(_a      ,       urllib.parse.quote_plus(single_url.split("/"							)[-1]							)							)
                 dummy_data_list.append(_a							)
          return dummy_data_list
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          for download_callback in self.download_callbacks:
                 download_callback(_a							)
          # we force the name of each key to be the last file / folder name of the url path
          # if the url has arguments, we need to encode them with urllib.parse.quote_plus
          lowercase       :							Tuple									=				os.path.join(_a      ,       urllib.parse.quote_plus(data_url.split("/"							)[-1]							)							)
          if os.path.exists(_a							) or not self.load_existing_dummy_data:
                 return value
          else:
                 # Backward compatibility, maybe deprecate at one point.
                 # For many datasets with single url calls to dl_manager.download_and_extract,
                 # the dummy_data.zip file is actually the zipped downloaded file
                 # while now we expected the dummy_data.zip file to be a directory containing
                 # the downloaded file.
                 return path_to_dummy_data
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self      ,       _a							):
          def _iter_archive_members(_a							):
                 # this preserves the order of the members inside the ZIP archive
                 lowercase       :							Optional[int]									=				Path(self.dummy_file							).parent
                 lowercase       :							List[str]									=				path.relative_to(_a							)
                 with ZipFile(self.local_path_to_dummy_data							) as zip_file:
                        lowercase       :							List[str]									=				zip_file.namelist()
                 for member in members:
                        if member.startswith(relative_path.as_posix()							):
                               yield dummy_parent_path.joinpath(_a							)

          lowercase       :							Union[str, Any]									=				Path(_a							)
          lowercase       :							List[Any]									=				_iter_archive_members(_a							) if self.use_local_dummy_data else path.rglob("*"							)
          for file_path in file_paths:
                 if file_path.is_file() and not file_path.name.startswith((".", "__")							):
                        yield file_path.relative_to(_a							).as_posix(), file_path.open("rb"							)

   def      __magic_name__     (     self      ,       _a							):
          if not isinstance(_a      ,       _a							):
                 lowercase       :							Any									=				[paths]
          for path in paths:
                 if os.path.isfile(_a							):
                        if os.path.basename(_a							).startswith((".", "__")							):
                               return
                        yield path
                 else:
                        for dirpath, dirnames, filenames in os.walk(_a							):
                               if os.path.basename(_a							).startswith((".", "__")							):
                                      continue
                               dirnames.sort()
                               for filename in sorted(_a							):
                                      if filename.startswith((".", "__")							):
                                             continue
                                      yield os.path.join(_a      ,       _a							)


