


"""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_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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Tuple   =VOCAB_FILES_NAMES
       a  :				Any   =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a  :				Union[str, Any]   =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a  :				List[str]   =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a  :				Optional[int]   =DPRContextEncoderTokenizer


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Any   =VOCAB_FILES_NAMES
       a  :				int   =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a  :				Tuple   =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a  :				Union[str, Any]   =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a  :				Tuple   =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(lowercase				)
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __call__(						self		,     snake_case__		,     snake_case__ = None		,     snake_case__ = None		,     snake_case__ = False		,     snake_case__ = False		,     snake_case__ = None		,     snake_case__ = None		,     snake_case__ = None		,     **snake_case__		,     ):






         """simple docstring"""





         if titles is None and texts is None:
           return super().__call__(
               snake_case__		,     padding=snake_case__		,     truncation=snake_case__		,     max_length=snake_case__		,     return_tensors=snake_case__		,     return_attention_mask=snake_case__		,     **snake_case__		,     )
         elif titles is None or texts is None:
           lowerCAmelCase     :     Dict								=							titles if texts is None else texts
           return super().__call__(
               snake_case__		,     snake_case__		,     padding=snake_case__		,     truncation=snake_case__		,     max_length=snake_case__		,     return_tensors=snake_case__		,     return_attention_mask=snake_case__		,     **snake_case__		,     )
         lowerCAmelCase     :     Any								=							titles if not isinstance(snake_case__		,     snake_case__						) else [titles]
         lowerCAmelCase     :     Optional[int]								=							texts if not isinstance(snake_case__		,     snake_case__						) else [texts]
         lowerCAmelCase     :     Optional[int]								=							len(snake_case__						)
         lowerCAmelCase     :     Dict								=							questions if not isinstance(snake_case__		,     snake_case__						) else [questions] * n_passages
         assert len(snake_case__						) == len(
             snake_case__						), f"""There should be as many titles than texts but got {len(snake_case__						)} titles and {len(snake_case__						)} texts."""
         lowerCAmelCase     :     Tuple								=							super().__call__(snake_case__		,     snake_case__		,     padding=snake_case__		,     truncation=snake_case__						)["input_ids"]
         lowerCAmelCase     :     Union[str, Any]								=							super().__call__(snake_case__		,     add_special_tokens=snake_case__		,     padding=snake_case__		,     truncation=snake_case__						)["input_ids"]
         lowerCAmelCase     :     Optional[Any]								=							{
             "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(snake_case__		,     snake_case__						)
             ]
         }
         if return_attention_mask is not False:
           lowerCAmelCase     :     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]						)
           lowerCAmelCase     :     Optional[int]								=							attention_mask
         return self.pad(snake_case__		,     padding=snake_case__		,     max_length=snake_case__		,     return_tensors=snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__ = 16		,     snake_case__ = 64		,     snake_case__ = 4		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							reader_input["input_ids"]
         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Any								=							reader_output[:3]
         lowerCAmelCase     :     int								=							len(snake_case__						)
         lowerCAmelCase     :     List[str]								=							sorted(range(snake_case__						)		,     reverse=snake_case__		,     key=relevance_logits.__getitem__						)
         lowerCAmelCase     :     List[DPRReaderOutput]								=							[]
         for doc_id in sorted_docs:
           lowerCAmelCase     :     Tuple								=							list(input_ids[doc_id]						)
           # assuming question & title information is at the beginning of the sequence
           lowerCAmelCase     :     Optional[int]								=							sequence_ids.index(self.sep_token_id		,     2						) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
             lowerCAmelCase     :     int								=							sequence_ids.index(self.pad_token_id						)
           else:
             lowerCAmelCase     :     List[str]								=							len(snake_case__						)

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







       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     str								=							[]
         for start_index, start_score in enumerate(snake_case__						):
           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)						)
         lowerCAmelCase     :     Any								=							sorted(snake_case__		,     key=lambda snake_case__						: x[1]		,     reverse=snake_case__						)
         lowerCAmelCase     :     int								=							[]
         for (start_index, end_index), score in scores:
           assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
           lowerCAmelCase     :     Tuple								=							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(snake_case__						) == top_spans:
             break
         return chosen_span_intervals


@add_end_docstrings(lowercase				)
class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    lowercase				):
       """simple docstring"""


       a  :				int   =VOCAB_FILES_NAMES
       a  :				Optional[int]   =READER_PRETRAINED_VOCAB_FILES_MAP
       a  :				int   =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a  :				List[Any]   =READER_PRETRAINED_INIT_CONFIGURATION
       a  :				int   =["input_ids", "attention_mask"]
       a  :				Optional[int]   =DPRReaderTokenizer









"""simple docstring"""







import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   '''T5Config'''
def 					a__			(				SCREAMING_SNAKE_CASE    :    jnp.array				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							jnp.zeros_like(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
  lowerCAmelCase     :     List[str]								=							shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							jnp.where(shifted_input_ids == -1_0_0				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  return shifted_input_ids


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="mt5"
       a  :				Tuple   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   ="mt5"
       a  :				Optional[Any]   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="mt5"
       a  :				Dict   =MTaConfig





