


"""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


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

lowercase__					:						List[Any]												=			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__					:						str												=			{
    '''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'''
        ),
    },
}
lowercase__					:						List[str]												=			{
    '''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'''
        ),
    },
}
lowercase__					:						Optional[Any]												=			{
    '''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'''
        ),
    },
}

lowercase__					:						str												=			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
lowercase__					:						Dict												=			{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
lowercase__					:						int												=			{
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


lowercase__					:						Optional[Any]												=			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowercase__					:						int												=			{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowercase__					:						int												=			{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}




class  _UpperCAmelCase (    UpperCAmelCase__):
 _lowerCAmelCase     :    Any	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    List[str]	      =	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    Any	      =	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    Optional[Any]	      =	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class  _UpperCAmelCase (    UpperCAmelCase__):
 _lowerCAmelCase     :    str	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    List[str]	      =	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    Optional[Any]	      =	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    Any	      =	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





lowercase__					:						Optional[int]												=			collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowercase__					:						Union[str, Any]												=			collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowercase__					:						List[Any]												=			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 :

 def __call__(  self	: Tuple						,  lowercase_	: List[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_	: int						,  ):






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

 def 		_snake_case      (  self	: Optional[int]						,  lowercase_	: BatchEncoding						,  lowercase_	: DPRReaderOutput						,  lowercase_	: int = 16						,  lowercase_	: int = 64						,  lowercase_	: int = 4						,  ):
     snake_case_				:  Tuple       =			reader_input['''input_ids''']
     snake_case_,			snake_case_,			snake_case_				:  Tuple       =			reader_output[:3]
     snake_case_				:  int       =			len(__lowerCAmelCase       )
     snake_case_				:  List[Any]       =			sorted(range(__lowerCAmelCase       )						,  reverse=__lowerCAmelCase						,  key=relevance_logits.__getitem__       )
     snake_case_				:  Dict       =			[]
     for doc_id in sorted_docs:
         snake_case_				:  List[str]       =			list(input_ids[doc_id]       )
         # assuming question & title information is at the beginning of the sequence
         snake_case_				:  List[Any]       =			sequence_ids.index(self.sep_token_id						,  2       ) + 1  # second sep id
         if sequence_ids[-1] == self.pad_token_id:
             snake_case_				:  Any       =			sequence_ids.index(self.pad_token_id       )
         else:
             snake_case_				:  List[str]       =			len(__lowerCAmelCase       )

         snake_case_				:  Optional[Any]       =			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=__lowerCAmelCase						,  top_spans=__lowerCAmelCase						,  )
         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=__lowerCAmelCase						,  start_index=__lowerCAmelCase						,  end_index=__lowerCAmelCase						,  text=self.decode(sequence_ids[start_index : end_index + 1]       )						,  )       )
         if len(__lowerCAmelCase       ) >= num_spans:
             break
     return nbest_spans_predictions[:num_spans]






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





@add_end_docstrings(UpperCAmelCase__)
class  _UpperCAmelCase (    UpperCAmelCase__					,  UpperCAmelCase__):
 _lowerCAmelCase     :    Optional[int]	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    List[str]	      =	READER_PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    Dict	      =	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    Optional[int]	      =	READER_PRETRAINED_INIT_CONFIGURATION
 _lowerCAmelCase     :    List[Any]	      =	["""input_ids""", """attention_mask"""]










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





