





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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase 				=     {
    """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 				=     {
    """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 				=     {
    """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 				=     {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowerCAmelCase 				=     {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowerCAmelCase 				=     {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_lowerCAmelCase 				=     {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase 				=     {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase 				=     {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a						       =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      a						       =							DPRContextEncoderTokenizer






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a						       =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      a						       =							DPRQuestionEncoderTokenizer






_lowerCAmelCase 				=     collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_lowerCAmelCase 				=     collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_lowerCAmelCase 				=     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)

    Return:
        `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(_lowerCamelCase     )
class    _UpperCAmelCase							:
      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:
            A_     :				List[Any]			      =	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__      ,       )
         A_     :				str			      =	titles if not isinstance(a__      ,       a__       ) else [titles]
         A_     :				List[Any]			      =	texts if not isinstance(a__      ,       a__       ) else [texts]
         A_     :				str			      =	len(a__       )
         A_     :				List[Any]			      =	questions if not isinstance(a__      ,       a__       ) else [questions] * n_passages
         assert len(a__       ) == len(
             a__       ), F"""There should be as many titles than texts but got {len(a__       )} titles and {len(a__       )} texts."""
         A_     :				int			      =	super().__call__(a__      ,       a__      ,       padding=a__      ,       truncation=a__       )["""input_ids"""]
         A_     :				Union[str, Any]			      =	super().__call__(a__      ,       add_special_tokens=a__      ,       padding=a__      ,       truncation=a__       )["""input_ids"""]
         A_     :				List[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:
            A_     :				Any			      =	[]
            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_     :				Union[str, Any]			      =	attention_mask
         return self.pad(a__      ,       padding=a__      ,       max_length=a__      ,       return_tensors=a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = 16      ,       a__ = 64      ,       a__ = 4      ,       ):
         A_     :				Dict			      =	reader_input["""input_ids"""]
         A_      ,				A_      ,				A_     :				List[Any]			      =	reader_output[:3]
         A_     :				List[str]			      =	len(a__       )
         A_     :				Optional[Any]			      =	sorted(range(a__       )      ,       reverse=a__      ,       key=relevance_logits.__getitem__       )
         A_     :				List[DPRReaderOutput]			      =	[]
         for doc_id in sorted_docs:
            A_     :				str			      =	list(input_ids[doc_id]       )
            # assuming question & title information is at the beginning of the sequence
            A_     :				Any			      =	sequence_ids.index(self.sep_token_id      ,       2       ) + 1  # second sep id
            if sequence_ids[-1] == self.pad_token_id:
               A_     :				Optional[Any]			      =	sequence_ids.index(self.pad_token_id       )
            else:
               A_     :				List[Any]			      =	len(a__       )

            A_     :				str			      =	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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       ):
         A_     :				Union[str, 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)       )
         A_     :				List[str]			      =	sorted(a__      ,       key=lambda a__       : x[1]      ,       reverse=a__       )
         A_     :				List[str]			      =	[]
         for (start_index, end_index), score in scores:
            assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
            A_     :				Optional[Any]			      =	end_index - start_index + 1
            assert length <= max_answer_length, 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(_lowerCamelCase     )
class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							READER_PRETRAINED_VOCAB_FILES_MAP
      a						       =							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							READER_PRETRAINED_INIT_CONFIGURATION
      a						       =							['''input_ids''', '''attention_mask''']
      a						       =							DPRReaderTokenizer













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






