





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

lowerCAmelCase					:     Any												={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase					:     List[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					:     Tuple												={
    '''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					:     str												={
    '''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					:     str												={
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


lowerCAmelCase					:     Any												={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase					:     Dict												={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase					:     Union[str, Any]												={
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class       a_   (				_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       a_   (				_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					:     Optional[int]												=collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowerCAmelCase					:     Tuple												=collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCAmelCase					:     Tuple												=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       a_   :





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





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					BatchEncoding	,      lowercase		:					DPRReaderOutput	,      lowercase		:					int = 16	,      lowercase		:					int = 64	,      lowercase		:					int = 4	,      ):





        """simple docstring"""




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

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







     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					List[int]	,      lowercase		:					List[int]	,      lowercase		:					int	,      lowercase		:					int	,      ):





        """simple docstring"""




        lowercase_			:List[str]    			= []
        for start_index, start_score in enumerate(lowercase						):
           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_			:Union[str, Any]    			= sorted(lowercase	,      key=lambda lowercase						: x[1]	,      reverse=lowercase						)
        lowercase_			:Dict    			= []
        for (start_index, end_index), score in scores:
           assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
           lowercase_			:Union[str, 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(lowercase						) == top_spans:
              break
        return chosen_span_intervals







@add_end_docstrings(_lowerCAmelCase							)
class       a_   (				_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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     Optional[Any]												={
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NllbMoeForConditionalGeneration''',
     '''NllbMoeModel''',
     '''NllbMoePreTrainedModel''',
     '''NllbMoeTop2Router''',
     '''NllbMoeSparseMLP''',
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 lowerCAmelCase					:     Tuple												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


