






def    __lowerCAmelCase   (    __snake_case = 10       ,			__snake_case = 1000       ,			__snake_case = True							):
     assert (
         isinstance(__snake_case       ,			__snake_case							)
         and isinstance(__snake_case       ,			__snake_case							)
         and isinstance(__snake_case       ,			__snake_case							)
     ), "Invalid type of value(s) specified to function!"

     if min_val > max_val:
          raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"							)
     return min_val if option else max_val





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     return int((number_a + number_a) / 2							)





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     assert (
         isinstance(__snake_case       ,			__snake_case							) and isinstance(__snake_case       ,			__snake_case							) and isinstance(__snake_case       ,			__snake_case							)
     ), 'argument values must be type of "int"'

     if lower > higher:
          raise ValueError("argument value for lower and higher must be(lower > higher)"							)

     if not lower < to_guess < higher:
          raise ValueError(
              "guess value must be within the range of lower and higher value"							)

     def answer(__snake_case							) -> str:
          if number > to_guess:
               return "high"
          elif number < to_guess:
               return "low"
          else:
               return "same"

     print("started..."							)

     __lowerCAmelCase             =       lower
     __lowerCAmelCase             =       higher

     __lowerCAmelCase             =       []

     while True:
          __lowerCAmelCase             =       get_avg(__snake_case       ,			__snake_case							)
          last_numbers.append(__snake_case							)

          if answer(__snake_case							) == "low":
               __lowerCAmelCase             =       number
          elif answer(__snake_case							) == "high":
               __lowerCAmelCase             =       number
          else:
               break

     print(F"""guess the number : {last_numbers[-1]}"""							)
     print(F"""details : {last_numbers!s}"""							)





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       int(input("Enter lower value : "							).strip()							)
     __lowerCAmelCase             =       int(input("Enter high value : "							).strip()							)
     __lowerCAmelCase             =       int(input("Enter value to guess : "							).strip()							)
     guess_the_number(__snake_case       ,			__snake_case       ,			__snake_case							)


if __name__ == "__main__":
 main()














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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowerCamelCase      :       int    =						logging.get_logger(__name__)

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

lowerCamelCase      :       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'''
        ),
    },
}
lowerCamelCase      :       int    =						{
    '''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      :       Optional[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'''
        ),
    },
}

lowerCamelCase      :       str    =						{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
lowerCamelCase      :       List[str]    =						{
    '''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      :       Dict    =						{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase      :       Union[str, Any]    =						{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase      :       Optional[Any]    =						{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				VOCAB_FILES_NAMES
      snake_case_	=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      snake_case_	=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_	=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      snake_case_	=				DPRContextEncoderTokenizer



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				VOCAB_FILES_NAMES
      snake_case_	=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      snake_case_	=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_	=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      snake_case_	=				DPRQuestionEncoderTokenizer

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

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


lowerCamelCase      :       Optional[int]    =						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)

    Return:
        `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(a_      )
class 				_UpperCamelCase      :


      def __call__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						BatchEncoding:
           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
           assert len(__UpperCamelCase						) == len(
               __UpperCamelCase						), 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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase = 1_6       ,    __UpperCamelCase = 6_4       ,    __UpperCamelCase = 4       ,    )->						List[DPRSpanPrediction]:
           __lowerCAmelCase             =       reader_input["input_ids"]
           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    )->						List[DPRSpanPrediction]:
           __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 __UpperCamelCase						: x[1]       ,    reverse=__UpperCamelCase						)
           __lowerCAmelCase             =       []
           for (start_index, end_index), score in scores:
                assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
                __lowerCAmelCase             =       end_index - start_index + 1
                assert length <= max_answer_length, 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(a_      )
class 				_UpperCamelCase      (a_				,		a_      ):
      snake_case_	=				VOCAB_FILES_NAMES
      snake_case_	=				READER_PRETRAINED_VOCAB_FILES_MAP
      snake_case_	=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_	=				READER_PRETRAINED_INIT_CONFIGURATION
      snake_case_	=				["""input_ids""", """attention_mask"""]
      snake_case_	=				DPRReaderTokenizer






