






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   :							List[str]						= logging.get_logger(__name__)

__a   :							Tuple						= {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__a   :							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   :							Any						= {
    "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   :							Dict						= {
    "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   :							int						= {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__a   :							str						= {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__a   :							List[str]						= {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__a   :							Any						= collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

__a   :							List[Any]						= collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


__a   :							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(lowercase_	)
class    __lowercase   :







     '''simple docstring'''




     def __call__(					self							:      List[str]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[str] = None			,						UpperCamelCase_							:      Optional[str] = None			,						UpperCamelCase_							:      Union[bool, str] = False			,						UpperCamelCase_							:      Union[bool, str] = False			,						UpperCamelCase_							:      Optional[int] = None			,						UpperCamelCase_							:      Optional[Union[str, TensorType]] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						**UpperCamelCase_							:      List[Any]			,						):


         """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:
             __A       =						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_			,						)
         __A       =						titles if not isinstance(UpperCamelCase_			,						UpperCamelCase_					) else [titles]
         __A       =						texts if not isinstance(UpperCamelCase_			,						UpperCamelCase_					) else [texts]
         __A       =						len(UpperCamelCase_					)
         __A       =						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."					)
         __A       =						super().__call__(UpperCamelCase_			,						UpperCamelCase_			,						padding=UpperCamelCase_			,						truncation=UpperCamelCase_					)["""input_ids"""]
         __A       =						super().__call__(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_			,						padding=UpperCamelCase_			,						truncation=UpperCamelCase_					)["""input_ids"""]
         __A       =						{
             """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:
             __A       =						[]
             for input_ids in encoded_inputs["input_ids"]:
                 attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
             __A       =						attention_mask
         return self.pad(UpperCamelCase_			,						padding=UpperCamelCase_			,						max_length=UpperCamelCase_			,						return_tensors=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      BatchEncoding			,						UpperCamelCase_							:      DPRReaderOutput			,						UpperCamelCase_							:      int = 16			,						UpperCamelCase_							:      int = 64			,						UpperCamelCase_							:      int = 4			,						):


         """simple docstring"""



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

             __A       =						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 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						[]
         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)					)
         __A       =						sorted(UpperCamelCase_			,						key=lambda UpperCamelCase_					: x[1]			,						reverse=UpperCamelCase_					)
         __A       =						[]
         for (start_index, end_index), score in scores:
             if start_index > end_index:
                 raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"					)
             __A       =						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(lowercase_	)
class    __lowercase   (						lowercase_      ,   lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					READER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					READER_PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]












from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




