


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


A     :   Union[str, Any]			    =			logging.get_logger(__name__)

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

A     :   Union[str, 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     :   str			    =			{
    """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     :   str			    =			{
    """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     :   Any			    =			{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
A     :   List[str]			    =			{
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
A     :   Tuple			    =			{
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


A     :   Optional[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     :   Optional[Any]			    =			{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class   _UpperCamelCase  (   UpperCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[int]				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    List[str]				      =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    Optional[Any]				      =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    int				      =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase						:    List[str]				      =DPRContextEncoderTokenizer
class   _UpperCamelCase  (   UpperCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[str]				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    Optional[int]				      =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    Any				      =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    Any				      =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase						:    str				      =DPRQuestionEncoderTokenizer
A     :   int			    =			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     :   Dict			    =			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   _UpperCamelCase  :
							'''simple docstring'''







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

										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 			snake_case				(     self    ,   __a    ,   __a    ,   __a = 16    ,   __a = 64    ,   __a = 4    ,   ):
										__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 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   ):
										__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 __a	: 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(UpperCAmelCase__		)
class   _UpperCamelCase  (   UpperCAmelCase__       ,UpperCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Dict				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    Any				      =READER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    Dict				      =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    Optional[int]				      =READER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase						:    int				      =["""input_ids""", """attention_mask"""]
							__UpperCAmelCase						:    Optional[int]				      =DPRReaderTokenizer










def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       int:



       """simple docstring"""

       return int(input_a == input_a == 0						)







def SCREAMING_SNAKE_CASE				(       )   ->       None:



       """simple docstring"""

       print('''Truth Table of NOR Gate:'''						)
       print('''|   Input 1   |  Input 2  | Output |'''						)
       print(f"""|      0      |    0      |  {nor_gate(0 ,				0						)}     |"""						)
       print(f"""|      0      |    1      |  {nor_gate(0 ,				1						)}     |"""						)
       print(f"""|      1      |    0      |  {nor_gate(1 ,				0						)}     |"""						)
       print(f"""|      1      |    1      |  {nor_gate(1 ,				1						)}     |"""						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


