



'''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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :List[Any]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Union[str, Any]						   =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :List[str]						   =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Optional[int]						   =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Dict						   =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :Dict						   =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Optional[int]						   =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





SCREAMING_SNAKE_CASE__      					=     collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

SCREAMING_SNAKE_CASE__      					=     collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


SCREAMING_SNAKE_CASE__      					=     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 				snake_case :



       def __call__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = False     ,UpperCAmelCase_ = False     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,**UpperCAmelCase_     ,)  ->							BatchEncoding:

           '''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:
               lowercase__		       =						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_     ,)
           lowercase__		       =						titles if not isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ) else [titles]
           lowercase__		       =						texts if not isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ) else [texts]
           lowercase__		       =						len(UpperCAmelCase_      )
           lowercase__		       =						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.'''      )
           lowercase__		       =						super().__call__(UpperCAmelCase_     ,UpperCAmelCase_     ,padding=UpperCAmelCase_     ,truncation=UpperCAmelCase_      )["input_ids"]
           lowercase__		       =						super().__call__(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_     ,padding=UpperCAmelCase_     ,truncation=UpperCAmelCase_      )["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(UpperCAmelCase_     ,UpperCAmelCase_      )
               ]
           }
           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(UpperCAmelCase_     ,padding=UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,return_tensors=UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = 16     ,UpperCAmelCase_ = 64     ,UpperCAmelCase_ = 4     ,)  ->							List[DPRSpanPrediction]:

           '''simple docstring'''





           lowercase__		       =						reader_input["input_ids"]
           lowercase__       ,       lowercase__       ,       lowercase__		       =						reader_output[:3]
           lowercase__		       =						len(UpperCAmelCase_      )
           lowercase__		       =						sorted(range(UpperCAmelCase_      )     ,reverse=UpperCAmelCase_     ,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(UpperCAmelCase_      )

               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=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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							List[DPRSpanPrediction]:

           '''simple docstring'''





           lowercase__		       =						[]
           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)      )
           lowercase__		       =						sorted(UpperCAmelCase_     ,key=lambda UpperCAmelCase_      : x[1]     ,reverse=UpperCAmelCase_      )
           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(UpperCAmelCase_      ) == top_spans:
                   break
           return chosen_span_intervals





@add_end_docstrings(UpperCamelCase   )
class 				snake_case (UpperCamelCase		,      UpperCamelCase   ):
       lowerCAmelCase__  :int						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Tuple						   =					READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :Optional[Any]						   =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Union[str, Any]						   =					READER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__  :Tuple						   =					["input_ids", "attention_mask"]









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						list[int]     ,_snake_case  :						list[int]       ):





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    print("The following activities are selected:"       )

    # The first activity is always selected
    lowercase__		       =						0
    print(_snake_case     ,end=","       )

    # Consider rest of the activities
    for j in range(_snake_case       ):
        # If this activity has start time greater than
        # or equal to the finish time of previously
        # selected activity, then select it
        if start[j] >= finish[i]:
            print(_snake_case     ,end=","       )
            lowercase__		       =						j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE__      					=     [1, 3, 0, 5, 8, 5]
 SCREAMING_SNAKE_CASE__      					=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)




