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

__lowerCAmelCase    :	Optional[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    :	Optional[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"""
        ),
    },
}
__lowerCAmelCase    :	Union[str, 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"""
        ),
    },
}

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


__lowerCAmelCase    :	Optional[Any]						=						{
    """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    :	Dict						=						{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}


class   __lowerCAmelCase		(		UpperCamelCase__					):



      """simple docstring"""







      A__		:	Dict         =			VOCAB_FILES_NAMES
      A__		:	List[Any]         =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      A__		:	Optional[Any]         =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__		:	str         =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      A__		:	Dict         =			DPRContextEncoderTokenizer






class   __lowerCAmelCase		(		UpperCamelCase__					):



      """simple docstring"""







      A__		:	List[str]         =			VOCAB_FILES_NAMES
      A__		:	Any         =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      A__		:	int         =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__		:	Dict         =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      A__		:	str         =			DPRQuestionEncoderTokenizer






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

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


__lowerCAmelCase    :	Tuple						=						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(UpperCamelCase__					)
class   __lowerCAmelCase		:



      """simple docstring"""







      def __call__(					self						:				Union[str, Any] ,				_snake_case						:				Dict ,				_snake_case						:				Union[str, Any] = None ,				_snake_case						:				Dict = None ,				_snake_case						:				str = False ,				_snake_case						:				List[str] = False ,				_snake_case						:				int = None ,				_snake_case						:				Optional[Any] = None ,				_snake_case						:				Optional[Any] = None ,				**_snake_case						:				Optional[Any] ,				):




          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						:	Optional[Any]					=				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						:	Union[str, Any]					=				titles if not isinstance(lowerCAmelCase__ ,				lowerCAmelCase__ ) else [titles]
          __lowercase						:	List[str]					=				texts if not isinstance(lowerCAmelCase__ ,				lowerCAmelCase__ ) else [texts]
          __lowercase						:	int					=				len(lowerCAmelCase__ )
          __lowercase						:	str					=				questions if not isinstance(lowerCAmelCase__ ,				lowerCAmelCase__ ) else [questions] * n_passages
          assert len(lowerCAmelCase__ ) == len(
              lowerCAmelCase__ ), F'There should be as many titles than texts but got {len(lowerCAmelCase__ )} titles and {len(lowerCAmelCase__ )} texts.'
          __lowercase						:	int					=				super().__call__(lowerCAmelCase__ ,				lowerCAmelCase__ ,				padding=lowerCAmelCase__ ,				truncation=lowerCAmelCase__ )["input_ids"]
          __lowercase						:	str					=				super().__call__(lowerCAmelCase__ ,				add_special_tokens=lowerCAmelCase__ ,				padding=lowerCAmelCase__ ,				truncation=lowerCAmelCase__ )["input_ids"]
          __lowercase						:	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(lowerCAmelCase__ ,				lowerCAmelCase__ )
              ]
          }
          if return_attention_mask is not False:
              __lowercase						:	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] )
              __lowercase						:	Tuple					=				attention_mask
          return self.pad(lowerCAmelCase__ ,				padding=lowerCAmelCase__ ,				max_length=lowerCAmelCase__ ,				return_tensors=lowerCAmelCase__ )




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

              __lowercase						:	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=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 						snake_case_	(					self						:				Tuple ,				_snake_case						:				int ,				_snake_case						:				Any ,				_snake_case						:				str ,				_snake_case						:				List[Any] ,				):
          __lowercase						:	Optional[int]					=				[]
          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						:	str					=				sorted(lowerCAmelCase__ ,				key=lambda _snake_case : x[1] ,				reverse=lowerCAmelCase__ )
          __lowercase						:	Any					=				[]
          for (start_index, end_index), score in scores:
              assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
              __lowercase						:	List[Any]					=				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(lowerCAmelCase__ ) == top_spans:
                  break
          return chosen_span_intervals






@add_end_docstrings(UpperCamelCase__					)
class   __lowerCAmelCase		(		UpperCamelCase__       ,       UpperCamelCase__					):



      """simple docstring"""







      A__		:	List[Any]         =			VOCAB_FILES_NAMES
      A__		:	List[str]         =			READER_PRETRAINED_VOCAB_FILES_MAP
      A__		:	str         =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__		:	List[str]         =			READER_PRETRAINED_INIT_CONFIGURATION
      A__		:	str         =			["""input_ids""", """attention_mask"""]
      A__		:	List[str]         =			DPRReaderTokenizer









from math import pi
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))






