


"""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


_lowerCAmelCase							:    str					   =	logging.get_logger(__name__)

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

_lowerCAmelCase							:    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'''
        ),
    },
}
_lowerCAmelCase							:    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'''
        ),
    },
}
_lowerCAmelCase							:    List[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'''
        ),
    },
}

_lowerCAmelCase							:    Any					   =	{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
_lowerCAmelCase							:    Union[str, Any]					   =	{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
_lowerCAmelCase							:    Union[str, Any]					   =	{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


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


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

_lowerCAmelCase							:    List[Any]					   =	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)

    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:						List[Any]							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						Optional[str] = None							,__lowerCAmelCase:						Optional[str] = None							,__lowerCAmelCase:						Union[bool, str] = False							,__lowerCAmelCase:						Union[bool, str] = False							,__lowerCAmelCase:						Optional[int] = None							,__lowerCAmelCase:						Optional[Union[str, TensorType]] = None							,__lowerCAmelCase:						Optional[bool] = None							,**__lowerCAmelCase:						Any							,):







     '''simple docstring'''







     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:
        _lowerCamelCase   :				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							,)
     _lowerCamelCase   :				Dict							=     titles if not isinstance(__lowerCAmelCase							,__lowerCAmelCase    ) else [titles]
     _lowerCamelCase   :				List[Any]							=     texts if not isinstance(__lowerCAmelCase							,__lowerCAmelCase    ) else [texts]
     _lowerCamelCase   :				Optional[int]							=     len(__lowerCAmelCase    )
     _lowerCamelCase   :				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."""    )
     _lowerCamelCase   :				Dict							=     super().__call__(__lowerCAmelCase							,__lowerCAmelCase							,padding=__lowerCAmelCase							,truncation=__lowerCAmelCase    )["input_ids"]
     _lowerCamelCase   :				List[str]							=     super().__call__(__lowerCAmelCase							,add_special_tokens=__lowerCAmelCase							,padding=__lowerCAmelCase							,truncation=__lowerCAmelCase    )["input_ids"]
     _lowerCamelCase   :				List[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(__lowerCAmelCase							,__lowerCAmelCase    )
         ]
     }
     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   :				Union[str, Any]							=     attention_mask
     return self.pad(__lowerCAmelCase							,padding=__lowerCAmelCase							,max_length=__lowerCAmelCase							,return_tensors=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						BatchEncoding							,__lowerCAmelCase:						DPRReaderOutput							,__lowerCAmelCase:						int = 16							,__lowerCAmelCase:						int = 64							,__lowerCAmelCase:						int = 4							,):







     '''simple docstring'''







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

        _lowerCamelCase   :				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=__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 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						List[int]							,__lowerCAmelCase:						List[int]							,__lowerCAmelCase:						int							,__lowerCAmelCase:						int							,):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     []
     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)    )
     _lowerCamelCase   :				Optional[int]							=     sorted(__lowerCAmelCase							,key=lambda __lowerCAmelCase    : x[1]							,reverse=__lowerCAmelCase    )
     _lowerCamelCase   :				Dict							=     []
     for (start_index, end_index), score in scores:
        if start_index > end_index:
           raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""    )
        _lowerCamelCase   :				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(_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"""


from typing import TYPE_CHECKING

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


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
