



"""simple docstring"""
import string


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						str:







  '''simple docstring'''


  lowercase_								= """"""
  for i in sequence:
    lowercase_								= ord(__lowerCAmelCase							)
    if 65 <= extract <= 90:
      output += chr(1_55 - extract							)
    elif 97 <= extract <= 1_22:
      output += chr(2_19 - extract							)
    else:
      output += i
  return output


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						str:







  '''simple docstring'''


  lowercase_								= string.ascii_letters
  lowercase_								= string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(__lowerCAmelCase							)] if c in letters else c for c in sequence							)


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  from timeit import timeit

  print("""Running performance benchmarks..."""							)
  lowercase_								= """from string import printable ; from __main__ import atbash, atbash_slow"""
  print(F'''> atbash_slow(): {timeit("atbash_slow(printable)" ,  setup=__lowerCAmelCase							)} seconds'''							)
  print(F'''>      atbash(): {timeit("atbash(printable)" ,  setup=__lowerCAmelCase							)} seconds'''							)


if __name__ == "__main__":
       for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
              print(F"{example} encrypted in atbash: {atbash(example)}")
       benchmark()






"""simple docstring"""
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


UpperCAmelCase						:		Any			= logging.get_logger(__name__)

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

UpperCAmelCase						:		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"
        ),
    },
}
UpperCAmelCase						:		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"
        ),
    },
}
UpperCAmelCase						:		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"
        ),
    },
}

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


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


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







UpperCAmelCase						:		Union[str, Any]			= collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


UpperCAmelCase						:		Any			= 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    SCREAMING_SNAKE_CASE__ :

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







      """simple docstring"""







      if titles is None and texts is None:
        return super().__call__(
            lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )
      elif titles is None or texts is None:
        lowercase_								= titles if texts is None else texts
        return super().__call__(
            lowerCAmelCase_ ,  lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )
      lowercase_								= titles if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_) else [titles]
      lowercase_								= texts if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_) else [texts]
      lowercase_								= len(lowerCAmelCase_)
      lowercase_								= questions if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_) else [questions] * n_passages
      if len(lowerCAmelCase_) != len(lowerCAmelCase_):
        raise ValueError(
            F'''There should be as many titles than texts but got {len(lowerCAmelCase_)} titles and {len(lowerCAmelCase_)} texts.''')
      lowercase_								= super().__call__(lowerCAmelCase_ ,  lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_)["""input_ids"""]
      lowercase_								= super().__call__(lowerCAmelCase_ ,  add_special_tokens=lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_)["""input_ids"""]
      lowercase_								= {
          """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(lowerCAmelCase_ ,  lowerCAmelCase_)
          ]
      }
      if return_attention_mask is not False:
        lowercase_								= []
        for input_ids in encoded_inputs["input_ids"]:
          attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
        lowercase_								= attention_mask
      return self.pad(lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				BatchEncoding ,  lowerCAmelCase_			:				DPRReaderOutput ,  lowerCAmelCase_			:				int = 1_6 ,  lowerCAmelCase_			:				int = 6_4 ,  lowerCAmelCase_			:				int = 4 ,  ):







      """simple docstring"""







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

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




    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				int ,  ):







      """simple docstring"""







      lowercase_								= []
      for start_index, start_score in enumerate(lowerCAmelCase_):
        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))
      lowercase_								= sorted(lowerCAmelCase_ ,  key=lambda lowerCAmelCase_: x[1] ,  reverse=lowerCAmelCase_)
      lowercase_								= []
      for (start_index, end_index), score in scores:
        if start_index > end_index:
          raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''')
        lowercase_								= 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(lowerCAmelCase_) == top_spans:
          break
      return chosen_span_intervals







@add_end_docstrings(__UpperCAmelCase   )
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   READER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   READER_PRETRAINED_INIT_CONFIGURATION
    lowercase__      =   ["input_ids", "attention_mask"]

