




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				:		int     =   logging.get_logger(__name__)

lowerCamelCase				:		int     =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:		Dict     =   {
    "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				:		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"
        ),
    },
}
lowerCamelCase				:		Union[str, 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				:		int     =   {
    "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				:		List[Any]     =   {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowerCamelCase				:		Optional[int]     =   {
    "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(						__SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class   A(						__SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

lowerCamelCase				:		Union[str, 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				:		List[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(__SCREAMING_SNAKE_CASE   )
class   A:



 '''simple docstring'''


 def __call__(				self					:  Optional[int]						,							A_					:  str						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = None						,							A_					:  Union[bool, str] = False						,							A_					:  Union[bool, str] = False						,							A_					:  Optional[int] = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  Optional[bool] = None						,							**A_					:  Any						,							)   ->	BatchEncoding:

       """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:
             lowerCamelCase_		     =    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_						,							)
       lowerCamelCase_		     =    titles if not isinstance(UpperCAmelCase_						,							UpperCAmelCase_  ) else [titles]
       lowerCamelCase_		     =    texts if not isinstance(UpperCAmelCase_						,							UpperCAmelCase_  ) else [texts]
       lowerCamelCase_		     =    len(UpperCAmelCase_  )
       lowerCamelCase_		     =    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."""  )
       lowerCamelCase_		     =    super().__call__(UpperCAmelCase_						,							UpperCAmelCase_						,							padding=UpperCAmelCase_						,							truncation=UpperCAmelCase_  )["input_ids"]
       lowerCamelCase_		     =    super().__call__(UpperCAmelCase_						,							add_special_tokens=UpperCAmelCase_						,							padding=UpperCAmelCase_						,							truncation=UpperCAmelCase_  )["input_ids"]
       lowerCamelCase_		     =    {
           "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:
             lowerCamelCase_		     =    []
             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_		     =    attention_mask
       return self.pad(UpperCAmelCase_						,							padding=UpperCAmelCase_						,							max_length=UpperCAmelCase_						,							return_tensors=UpperCAmelCase_  )

 def 				a__ (				self					:  List[Any]						,							A_					:  BatchEncoding						,							A_					:  DPRReaderOutput						,							A_					:  int = 16						,							A_					:  int = 64						,							A_					:  int = 4						,							)   ->	List[DPRSpanPrediction]:

       """simple docstring"""






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

             lowerCamelCase_		     =    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 				a__ (				self					:  Union[str, Any]						,							A_					:  List[int]						,							A_					:  List[int]						,							A_					:  int						,							A_					:  int						,							)   ->	List[DPRSpanPrediction]:

       """simple docstring"""






       lowerCamelCase_		     =    []
       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)  )
       lowerCamelCase_		     =    sorted(UpperCAmelCase_						,							key=lambda A_  : x[1]						,							reverse=UpperCAmelCase_  )
       lowerCamelCase_		     =    []
       for (start_index, end_index), score in scores:
             if start_index > end_index:
                   raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""  )
             lowerCamelCase_		     =    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(__SCREAMING_SNAKE_CASE   )
class   A(						__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						READER_PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						READER_PRETRAINED_INIT_CONFIGURATION
 UpperCamelCase						   =						['''input_ids''', '''attention_mask''']










from typing import Any





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		list:







  """simple docstring"""





  _validation(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  # Creates data structures and fill initial step
  lowerCamelCase__:      dict			  ={}
  lowerCamelCase__:      dict			  ={}
  for state in states_space:
    lowerCamelCase__:      Optional[Any]			  =observations_space[0]
    lowerCamelCase__:      List[Any]			  =(
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    lowerCamelCase__:      int			  =None

  # Fills the data structure with the probabilities of
  # different transitions and pointers to previous states
  for o in range(1	,    len(__a		)		):
    lowerCamelCase__:      Tuple			  =observations_space[o]
    lowerCamelCase__:      Optional[Any]			  =observations_space[o - 1]
    for state in states_space:
      # Calculates the argmax for probability function
      lowerCamelCase__:      Tuple			  =""
      lowerCamelCase__:      Optional[Any]			  =-1
      for k_state in states_space:
        lowerCamelCase__:      int			  =(
            probabilities[(k_state, prior_observation)]
            * transition_probabilities[k_state][state]
            * emission_probabilities[state][observation]
        )
        if probability > max_probability:
          lowerCamelCase__:      List[str]			  =probability
          lowerCamelCase__:      int			  =k_state

            # Update probabilities and pointers dicts
      lowerCamelCase__:      Any			  =(
          probabilities[(arg_max, prior_observation)]
          * transition_probabilities[arg_max][state]
          * emission_probabilities[state][observation]
      )

      lowerCamelCase__:      int			  =arg_max

    # The final observation
  lowerCamelCase__:      Any			  =observations_space[len(__a		) - 1]

  # argmax for given final observation
  lowerCamelCase__:      Optional[Any]			  =""
  lowerCamelCase__:      int			  =-1
  for k_state in states_space:
    lowerCamelCase__:      Tuple			  =probabilities[(k_state, final_observation)]
    if probability > max_probability:
      lowerCamelCase__:      List[Any]			  =probability
      lowerCamelCase__:      Dict			  =k_state
  lowerCamelCase__:      str			  =arg_max

  # Process pointers backwards
  lowerCamelCase__:      Union[str, Any]			  =last_state
  lowerCamelCase__:      List[str]			  =[]
  for o in range(len(__a		) - 1	,    -1	,    -1		):
    result.append(__a		)
    lowerCamelCase__:      Union[str, Any]			  =pointers[previous, observations_space[o]]
  result.reverse()

  return result





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_not_empty(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  _validate_lists(__a	,    __a		)
  _validate_dicts(
      __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]		):
    raise ValueError("There's an empty parameter"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_list(__a	,    "observations_space"		)
  _validate_list(__a	,    "states_space"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} must be a list"""
    raise ValueError(__a		)
  else:
    for x in _object:
      if not isinstance(__a	,    __a		):
        lowerCamelCase__:      str			  =F"""{var_name} must be a list of strings"""
        raise ValueError(__a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_dict(__a	,    "initial_probabilities"	,    __a		)
  _validate_nested_dict(__a	,    "transition_probabilities"		)
  _validate_nested_dict(__a	,    "emission_probabilities"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_dict(_object	,    __a	,    __a		)
  for x in _object.values():
    _validate_dict(__a	,    __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Optional[int]			  =F"""{var_name} must be a dict"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} all keys must be strings"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object.values()		):
    lowerCamelCase__:      Dict			  ="nested dictionary " if nested else ""
    lowerCamelCase__:      List[str]			  =F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
    raise ValueError(__a		)


if __name__ == "__main__":
     from doctest import testmod

     testmod()

