
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


_A		     =    logging.get_logger(__name__)

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

_A		     =    {
    '''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'''
        ),
    },
}
_A		     =    {
    '''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'''
        ),
    },
}
_A		     =    {
    '''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'''
        ),
    },
}

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


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

class 						lowercase_				(				__lowerCamelCase ):
 A__  : int      					=			VOCAB_FILES_NAMES
 A__  : Any      					=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 A__  : Optional[int]      					=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__  : Any      					=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class 						lowercase_				(				__lowerCamelCase ):
 A__  : Union[str, Any]      					=			VOCAB_FILES_NAMES
 A__  : str      					=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 A__  : Any      					=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__  : Optional[int]      					=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

_A		     =    collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


_A		     =    R'''\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '''

@add_start_docstrings(__lowerCamelCase )
class 						lowercase_				:

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




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase = 1_6   ,		__UpperCamelCase = 6_4   ,		__UpperCamelCase = 4   ,		):




        """simple docstring"""


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

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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		):




        """simple docstring"""


        UpperCamelCase_											=				[]
        for start_index, start_score in enumerate(__UpperCamelCase   ):
               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)   )
        UpperCamelCase_											=				sorted(__UpperCamelCase   ,		key=lambda __UpperCamelCase   : x[1]   ,		reverse=__UpperCamelCase   )
        UpperCamelCase_											=				[]
        for (start_index, end_index), score in scores:
               if start_index > end_index:
                      raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''   )
               UpperCamelCase_											=				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(__UpperCamelCase   ) == top_spans:
                      break
        return chosen_span_intervals



@add_end_docstrings(__lowerCamelCase )
class 						lowercase_				(				__lowerCamelCase							,		__lowerCamelCase ):
 A__  : Tuple      					=			VOCAB_FILES_NAMES
 A__  : Union[str, Any]      					=			READER_PRETRAINED_VOCAB_FILES_MAP
 A__  : str      					=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__  : Optional[int]      					=			READER_PRETRAINED_INIT_CONFIGURATION
 A__  : List[Any]      					=			["input_ids", "attention_mask"]










from typing import TYPE_CHECKING

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


UpperCAmelCase_     							= {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= ['ConvBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ConvBertForMaskedLM',
      'ConvBertForMultipleChoice',
      'ConvBertForQuestionAnswering',
      'ConvBertForSequenceClassification',
      'ConvBertForTokenClassification',
      'ConvBertLayer',
      'ConvBertModel',
      'ConvBertPreTrainedModel',
      'load_tf_weights_in_convbert',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFConvBertForMaskedLM',
      'TFConvBertForMultipleChoice',
      'TFConvBertForQuestionAnswering',
      'TFConvBertForSequenceClassification',
      'TFConvBertForTokenClassification',
      'TFConvBertLayer',
      'TFConvBertModel',
      'TFConvBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
  from .tokenization_convbert import ConvBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_convbert_fast import ConvBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convbert import (
        CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvBertForMaskedLM,
        ConvBertForMultipleChoice,
        ConvBertForQuestionAnswering,
        ConvBertForSequenceClassification,
        ConvBertForTokenClassification,
        ConvBertLayer,
        ConvBertModel,
        ConvBertPreTrainedModel,
        load_tf_weights_in_convbert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convbert import (
        TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertLayer,
        TFConvBertModel,
        TFConvBertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

