




from collections import namedtuple

UpperCamelCase								=					namedtuple('from_to', 'from_ to')

UpperCamelCase								=					{
    'cubicmeter': from_to(1, 1),
    'litre': from_to(0.001, 1000),
    'kilolitre': from_to(1, 1),
    'gallon': from_to(0.00_454, 264.172),
    'cubicyard': from_to(0.76_455, 1.30_795),
    'cubicfoot': from_to(0.028, 35.3_147),
    'cup': from_to(0.000_236_588, 4_226.75),
}



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     if from_type not in METRIC_CONVERSION:
          raise ValueError(
              F'Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'
              + ", ".join(a_      )      )
     if to_type not in METRIC_CONVERSION:
          raise ValueError(
              F'Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'
              + ", ".join(a_      )      )
     return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
     import doctest

     doctest.testmod()






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


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

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


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





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRContextEncoderTokenizer






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRQuestionEncoderTokenizer






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

UpperCamelCase								=					collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


UpperCamelCase								=					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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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 			__lowerCamelCase	:




    """simple docstring"""

    def __call__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       ,     )				->	BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         elif titles is None or texts is None:
              lowerCAmelCase__          =			titles if texts is None else texts
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			titles if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [titles]
         lowerCAmelCase__          =			texts if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [texts]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			questions if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [questions] * n_passages
         assert len(SCREAMING_SNAKE_CASE__       ) == len(
             SCREAMING_SNAKE_CASE__       ), f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__       )} titles and {len(SCREAMING_SNAKE_CASE__       )} texts.'
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["input_ids"]
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     add_special_tokens=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
             ]
         }
         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(SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					BatchEncoding       ,     SCREAMING_SNAKE_CASE__					:					DPRReaderOutput       ,     SCREAMING_SNAKE_CASE__					:					int = 16       ,     SCREAMING_SNAKE_CASE__					:					int = 64       ,     SCREAMING_SNAKE_CASE__					:					int = 4       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			reader_input["input_ids"]
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			reader_output[:3]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			sorted(range(SCREAMING_SNAKE_CASE__       )       ,     reverse=SCREAMING_SNAKE_CASE__       ,     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(SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			[]
         for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__       ):
              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(SCREAMING_SNAKE_CASE__       ,     key=lambda SCREAMING_SNAKE_CASE__       : x[1]       ,     reverse=SCREAMING_SNAKE_CASE__       )
         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(SCREAMING_SNAKE_CASE__       ) == top_spans:
                   break
         return chosen_span_intervals






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




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				READER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				READER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				["input_ids", "attention_mask"]
    snake_case__ =				DPRReaderTokenizer
