






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_						:						List[Any]						      =					{
    """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_						:						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"""
        ),
    },
}

A_						:						str						      =					{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
A_						:						int						      =					{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
A_						:						Optional[int]						      =					{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


A_						:						List[Any]						      =					{
    """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_						:						List[str]						      =					{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}




class 							_lowerCAmelCase(			__SCREAMING_SNAKE_CASE   ):





       """simple docstring"""







       a							:						Union[str, Any]										=VOCAB_FILES_NAMES
       a							:						Any										=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a							:						List[Any]										=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						Optional[int]										=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 							_lowerCAmelCase(			__SCREAMING_SNAKE_CASE   ):





       """simple docstring"""







       a							:						List[str]										=VOCAB_FILES_NAMES
       a							:						Dict										=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a							:						Dict										=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						Union[str, Any]										=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

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


A_						:						Optional[Any]						      =					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(__SCREAMING_SNAKE_CASE   )
class 							_lowerCAmelCase:





       """simple docstring"""







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

         if titles is None and texts is None:
           return super().__call__(
               A_    ,     padding=A_    ,     truncation=A_    ,     max_length=A_    ,     return_tensors=A_    ,     return_attention_mask=A_    ,     **A_    ,     )
         elif titles is None or texts is None:
           UpperCamelCase_:   Dict        =							titles if texts is None else texts
           return super().__call__(
               A_    ,     A_    ,     padding=A_    ,     truncation=A_    ,     max_length=A_    ,     return_tensors=A_    ,     return_attention_mask=A_    ,     **A_    ,     )
         UpperCamelCase_:   Tuple        =							titles if not isinstance(A_    ,     A_				) else [titles]
         UpperCamelCase_:   Dict        =							texts if not isinstance(A_    ,     A_				) else [texts]
         UpperCamelCase_:   Optional[int]        =							len(A_				)
         UpperCamelCase_:   Dict        =							questions if not isinstance(A_    ,     A_				) else [questions] * n_passages
         if len(A_				) != len(A_				):
           raise ValueError(
               f'''There should be as many titles than texts but got {len(A_				)} titles and {len(A_				)} texts.'''				)
         UpperCamelCase_:   Union[str, Any]        =							super().__call__(A_    ,     A_    ,     padding=A_    ,     truncation=A_				)['input_ids']
         UpperCamelCase_:   Dict        =							super().__call__(A_    ,     add_special_tokens=A_    ,     padding=A_    ,     truncation=A_				)['input_ids']
         UpperCamelCase_:   Optional[int]        =							{
             '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(A_    ,     A_				)
             ]
         }
         if return_attention_mask is not False:
           UpperCamelCase_:   List[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]				)
           UpperCamelCase_:   Optional[Any]        =							attention_mask
         return self.pad(A_    ,     padding=A_    ,     max_length=A_    ,     return_tensors=A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = 1_6    ,     _lowerCamelCase = 6_4    ,     _lowerCamelCase = 4    ,     ):
         UpperCamelCase_:   int        =							reader_input['input_ids']
         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							reader_output[:3]
         UpperCamelCase_:   Tuple        =							len(A_				)
         UpperCamelCase_:   int        =							sorted(range(A_				)    ,     reverse=A_    ,     key=relevance_logits.__getitem__				)
         UpperCamelCase_:   str        =							[]
         for doc_id in sorted_docs:
           UpperCamelCase_:   Any        =							list(input_ids[doc_id]				)
           # assuming question & title information is at the beginning of the sequence
           UpperCamelCase_:   int        =							sequence_ids.index(self.sep_token_id    ,     2				) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
             UpperCamelCase_:   List[Any]        =							sequence_ids.index(self.pad_token_id				)
           else:
             UpperCamelCase_:   int        =							len(A_				)

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





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     ):
         UpperCamelCase_:   List[Any]        =							[]
         for start_index, start_score in enumerate(A_				):
           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_:   Union[str, Any]        =							sorted(A_    ,     key=lambda _lowerCamelCase				: x[1]    ,     reverse=A_				)
         UpperCamelCase_:   Union[str, Any]        =							[]
         for (start_index, end_index), score in scores:
           if start_index > end_index:
             raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''				)
           UpperCamelCase_:   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(A_				) == top_spans:
             break
         return chosen_span_intervals







@add_end_docstrings(__SCREAMING_SNAKE_CASE   )
class 							_lowerCAmelCase(			__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE   ):





       """simple docstring"""







       a							:						Any										=VOCAB_FILES_NAMES
       a							:						Dict										=READER_PRETRAINED_VOCAB_FILES_MAP
       a							:						Any										=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						Optional[Any]										=READER_PRETRAINED_INIT_CONFIGURATION
       a							:						str										=["""input_ids""", """attention_mask"""]






import operator as op


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			lambda lowerCAmelCase_ ,							lowerCAmelCase_	: int(x / y	)  # noqa: E731 integer division operation
    SCREAMING_SNAKE_CASE__											=			{
        '''^''': op.pow,
        '''*''': op.mul,
        '''/''': div,
        '''+''': op.add,
        '''-''': op.sub,
    }  # operators & their respective operation

    # print table header
    print('''Symbol'''.center(8	) ,							'''Action'''.center(1_2	) ,							'''Stack''' ,							sep=''' | '''	)
    print('''-''' * (3_0 + len(lowerCAmelCase_	))	)

    for x in post_fix:
        if x.isdigit():  # if x in digit
            stack.append(lowerCAmelCase_	)  # append x to stack
            # output in tabular format
            print(x.rjust(8	) ,							('''push(''' + x + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)
        else:
            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + a + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            stack.append(
                str(opr[x](int(lowerCAmelCase_	) ,							int(lowerCAmelCase_	)	)	)	)  # evaluate the 2 values popped from stack & push result to stack
            # output in tabular format
            print(
                x.rjust(8	) ,							('''push(''' + a + x + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | ''' ,							)

    return int(stack[0]	)


if __name__ == "__main__":
  _A					:				Dict				=			input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
  print("""\n\tResult = """, solve(Postfix))
