


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_ :Tuple  							=			logging.get_logger(__name__)

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

a_ :Optional[int]  							=			{
    '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_ :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'
        ),
    },
}
a_ :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'
        ),
    },
}

a_ :Optional[int]  							=			{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
a_ :List[Any]  							=			{
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
a_ :Tuple  							=			{
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


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

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Optional[Any]      = VOCAB_FILES_NAMES
     lowerCamelCase			:			int      = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase			:			List[str]      = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase			:			int      = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Optional[int]      = VOCAB_FILES_NAMES
     lowerCamelCase			:			Union[str, Any]      = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase			:			Optional[Any]      = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase			:			Optional[int]      = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




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

a_ :Optional[int]  							=			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


a_ :Tuple  							=			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(_UpperCAmelCase			)
class 							lowercase  :







     def __call__(						self :						List[Any]   ,   _lowercase :						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 :						str   ,   ):
           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:
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		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   ,   )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		titles if not isinstance(_lowercase   ,   _lowercase      ) else [titles]
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		texts if not isinstance(_lowercase   ,   _lowercase      ) else [texts]
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		len(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  str						     =		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."""      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		super().__call__(_lowercase   ,   _lowercase   ,   padding=_lowercase   ,   truncation=_lowercase      )['''input_ids''']
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		super().__call__(_lowercase   ,   add_special_tokens=_lowercase   ,   padding=_lowercase   ,   truncation=_lowercase      )['''input_ids''']
           SCREAMING_SNAKE_CASE__       :  Optional[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(_lowercase   ,   _lowercase      )
               ]
           }
           if return_attention_mask is not False:
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[]
                 for input_ids in encoded_inputs["input_ids"]:
                       attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		attention_mask
           return self.pad(_lowercase   ,   padding=_lowercase   ,   max_length=_lowercase   ,   return_tensors=_lowercase      )







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

                 SCREAMING_SNAKE_CASE__       :  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 							lowercase__       (						self :						Dict   ,   _lowercase :						List[int]   ,   _lowercase :						List[int]   ,   _lowercase :						int   ,   _lowercase :						int   ,   ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[]
           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)      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		sorted(_lowercase   ,   key=lambda _lowercase      : x[1]   ,   reverse=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[]
           for (start_index, end_index), score in scores:
                 if start_index > end_index:
                       raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""      )
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		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(_UpperCAmelCase			)
class 							lowercase  (						_UpperCAmelCase    ,       _UpperCAmelCase			):
     lowerCamelCase			:			Dict      = VOCAB_FILES_NAMES
     lowerCamelCase			:			Union[str, Any]      = READER_PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase			:			List[str]      = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase			:			str      = READER_PRETRAINED_INIT_CONFIGURATION
     lowerCamelCase			:			List[Any]      = ['''input_ids''', '''attention_mask''']









'''simple docstring'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


if __name__ == "__main__":
							print(solution())





