





'''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


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

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

UpperCAmelCase					:		Tuple		=							{
    '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'
        ),
    },
}
UpperCAmelCase					:		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'
        ),
    },
}
UpperCAmelCase					:		int		=							{
    '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'
        ),
    },
}

UpperCAmelCase					:		int		=							{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
UpperCAmelCase					:		List[Any]		=							{
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
UpperCAmelCase					:		Any		=							{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


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





class lowerCamelCase						(a__					):
  _lowercase  :	Union[str, Any]       					=				VOCAB_FILES_NAMES
  _lowercase  :	List[str]       					=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Optional[Any]       					=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Any       					=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class lowerCamelCase						(a__					):
  _lowercase  :	Any       					=				VOCAB_FILES_NAMES
  _lowercase  :	Dict       					=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Tuple       					=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	List[Any]       					=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

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


UpperCAmelCase					:		str		=							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(a__					)
class lowerCamelCase						:







  def __call__(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						BatchEncoding:






   """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:
    _snake_case		:	Optional[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__     ,     )
   _snake_case		:	Optional[Any]					     =     titles if not isinstance(lowercase__     ,     lowercase__		) else [titles]
   _snake_case		:	int					     =     texts if not isinstance(lowercase__     ,     lowercase__		) else [texts]
   _snake_case		:	Optional[int]					     =     len(lowercase__		)
   _snake_case		:	Tuple					     =     questions if not isinstance(lowercase__     ,     lowercase__		) else [questions] * n_passages
   if len(lowercase__		) != len(lowercase__		):
    raise ValueError(
        F'''There should be as many titles than texts but got {len(lowercase__		)} titles and {len(lowercase__		)} texts.'''		)
   _snake_case		:	int					     =     super().__call__(lowercase__     ,     lowercase__     ,     padding=lowercase__     ,     truncation=lowercase__		)['''input_ids''']
   _snake_case		:	List[str]					     =     super().__call__(lowercase__     ,     add_special_tokens=lowercase__     ,     padding=lowercase__     ,     truncation=lowercase__		)['''input_ids''']
   _snake_case		:	str					     =     {
       '''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:
    _snake_case		:	Tuple					     =     []
    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		:	int					     =     attention_mask
   return self.pad(lowercase__     ,     padding=lowercase__     ,     max_length=lowercase__     ,     return_tensors=lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = 16     ,     lowercase__ = 64     ,     lowercase__ = 4     ,     )		->						List[DPRSpanPrediction]:






   """simple docstring"""





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

    _snake_case		:	Union[str, 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=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 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						List[DPRSpanPrediction]:






   """simple docstring"""





   _snake_case		:	Union[str, Any]					     =     []
   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)		)
   _snake_case		:	str					     =     sorted(lowercase__     ,     key=lambda lowercase__		: x[1]     ,     reverse=lowercase__		)
   _snake_case		:	List[str]					     =     []
   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		:	Union[str, 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(lowercase__		) == top_spans:
     break
   return chosen_span_intervals





@add_end_docstrings(a__					)
class lowerCamelCase						(a__						,      a__					):
  _lowercase  :	Dict       					=				VOCAB_FILES_NAMES
  _lowercase  :	Optional[int]       					=				READER_PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	List[Any]       					=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	List[Any]       					=				READER_PRETRAINED_INIT_CONFIGURATION
  _lowercase  :	Union[str, Any]       					=				["""input_ids""", """attention_mask"""]









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		int		=							{
    'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							[
       'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PegasusXForConditionalGeneration',
       'PegasusXModel',
       'PegasusXPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pegasus_x import (
          PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
          PegasusXForConditionalGeneration,
          PegasusXModel,
          PegasusXPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


