

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			:					str						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__A			:					Tuple						=			{
    '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			:					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'
        ),
    },
}
__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			:					Optional[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			:					Any						=			{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


__A			:					str						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__A			:					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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




__A			:					Optional[int]						=			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[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(__snake_case		)
class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




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





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

           SCREAMING_SNAKE_CASE										=   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					:			Optional[int]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , ):
       SCREAMING_SNAKE_CASE										=   []
       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)			)
       SCREAMING_SNAKE_CASE										=   sorted(__lowerCamelCase     , key=lambda __lowerCamelCase			: x[1]     , reverse=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   []
       for (start_index, end_index), score in scores:
           if start_index > end_index:
               raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"			)
           SCREAMING_SNAKE_CASE										=   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(__snake_case		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      READER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      READER_PRETRAINED_INIT_CONFIGURATION
   lowerCamelCase__				=      ["input_ids", "attention_mask"]




'''simple docstring'''

import unittest

from knapsack import knapsack as k






class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						str			)			->					List[Any]:
										snake_case:    Optional[Any]      							=0
										snake_case:    List[str]      							=[0]
										snake_case:    List[str]      							=[0]
										snake_case:    Union[str, Any]      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						0			)

										snake_case:    Union[str, Any]      							=[6_0]
										snake_case:    str      							=[1_0]
										snake_case:    List[str]      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						0			)


							def    UpperCamelCase     (				self   :						int			)			->					Optional[Any]:
										snake_case:    Optional[int]      							=3
										snake_case:    Dict      							=[1, 2, 3]
										snake_case:    List[Any]      							=[3, 2, 1]
										snake_case:    Tuple      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						5			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										snake_case:    Any      							=5_0
										snake_case:    str      							=[6_0, 1_0_0, 1_2_0]
										snake_case:    Any      							=[1_0, 2_0, 3_0]
										snake_case:    Tuple      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						2_2_0			)


if __name__ == "__main__":
	unittest.main()




