'''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


_A							=   logging.get_logger(__name__)

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

_A							=   {
    '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							=   {
    '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							=   {
    '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							=   {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
_A							=   {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
_A							=   {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


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


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Union[str, Any]     =    VOCAB_FILES_NAMES
   A							:       Optional[int]     =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   A							:       List[Any]     =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       str     =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Union[str, Any]     =    VOCAB_FILES_NAMES
   A							:       List[Any]     =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   A							:       Dict     =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       Optional[int]     =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

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


_A							=   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       UpperCAmelCase__  :






   """simple docstring"""
   def __call__(self       ,				_a       ,				_a = None       ,				_a = None       ,				_a = False       ,				_a = False       ,				_a = None       ,				_a = None       ,				_a = None       ,				**_a       ,				)    ->     BatchEncoding:
        if titles is None and texts is None:
             return super().__call__(
                 _a       ,				padding=_a       ,				truncation=_a       ,				max_length=_a       ,				return_tensors=_a       ,				return_attention_mask=_a       ,				**_a       ,				)
        elif titles is None or texts is None:
             lowercase_					:       Union[str, Any]     				=					titles if texts is None else texts
             return super().__call__(
                 _a       ,				_a       ,				padding=_a       ,				truncation=_a       ,				max_length=_a       ,				return_tensors=_a       ,				return_attention_mask=_a       ,				**_a       ,				)
        lowercase_					:       List[str]     				=					titles if not isinstance(_a       ,				_a				) else [titles]
        lowercase_					:       List[str]     				=					texts if not isinstance(_a       ,				_a				) else [texts]
        lowercase_					:       Union[str, Any]     				=					len(_a				)
        lowercase_					:       Dict     				=					questions if not isinstance(_a       ,				_a				) else [questions] * n_passages
        if len(_a				) != len(_a				):
             raise ValueError(
                 f'''There should be as many titles than texts but got {len(_a				)} titles and {len(_a				)} texts.'''				)
        lowercase_					:       List[Any]     				=					super().__call__(_a       ,				_a       ,				padding=_a       ,				truncation=_a				)['input_ids']
        lowercase_					:       Tuple     				=					super().__call__(_a       ,				add_special_tokens=_a       ,				padding=_a       ,				truncation=_a				)['input_ids']
        lowercase_					:       Tuple     				=					{
            '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(_a       ,				_a				)
            ]
        }
        if return_attention_mask is not False:
             lowercase_					:       List[str]     				=					[]
             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_					:       Any     				=					attention_mask
        return self.pad(_a       ,				padding=_a       ,				max_length=_a       ,				return_tensors=_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a = 16       ,				_a = 64       ,				_a = 4       ,				)    ->     List[DPRSpanPrediction]:
        lowercase_					:       List[str]     				=					reader_input['input_ids']
        lowercase_					:       Dict     				=					reader_output[:3]
        lowercase_					:       str     				=					len(_a				)
        lowercase_					:       Optional[Any]     				=					sorted(range(_a				)       ,				reverse=_a       ,				key=relevance_logits.__getitem__				)
        lowercase_					:       List[DPRReaderOutput]     				=					[]
        for doc_id in sorted_docs:
             lowercase_					:       Tuple     				=					list(input_ids[doc_id]				)
             # assuming question & title information is at the beginning of the sequence
             lowercase_					:       Optional[Any]     				=					sequence_ids.index(self.sep_token_id       ,				2				) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                  lowercase_					:       str     				=					sequence_ids.index(self.pad_token_id				)
             else:
                  lowercase_					:       Dict     				=					len(_a				)

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





   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				)    ->     List[DPRSpanPrediction]:
        lowercase_					:       List[str]     				=					[]
        for start_index, start_score in enumerate(_a				):
             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_					:       Tuple     				=					sorted(_a       ,				key=lambda _a				: x[1]       ,				reverse=_a				)
        lowercase_					:       Union[str, Any]     				=					[]
        for (start_index, end_index), score in scores:
             if start_index > end_index:
                  raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''				)
             lowercase_					:       List[str]     				=					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(_a				) == top_spans:
                  break
        return chosen_span_intervals



@add_end_docstrings(_snake_case			)
class       UpperCAmelCase__  (       _snake_case					,     _snake_case			):






   """simple docstring"""
   A							:       Union[str, Any]     =    VOCAB_FILES_NAMES
   A							:       List[Any]     =    READER_PRETRAINED_VOCAB_FILES_MAP
   A							:       str     =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       Dict     =    READER_PRETRAINED_INIT_CONFIGURATION
   A							:       Dict     =    ['''input_ids''', '''attention_mask''']





'''simple docstring'''




class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					[
            [],
            [],
            [],
        ]
   def 		_lowerCamelCase (self       ,				_a       ,				_a				)    ->     None:
        try:
             if len(self.queues[priority]				) >= 100:
                  raise OverflowError('Maximum queue size is 100'				)
             self.queues[priority].append(_a				)
        except IndexError:
             raise ValueError('Valid priorities are 0, 1, and 2'				)
   def 		_lowerCamelCase (self				)    ->     int:
        for queue in self.queues:
             if queue:
                  return queue.pop(0				)
        raise UnderFlowError('All queues are empty'				)





   def __str__(self				)    ->     str:
        return "\n".join(f'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     str:
        lowercase_					:       List[Any]     				=					[]
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        if len(self.queue				) == 100:
             raise OverFlowError('Maximum queue size is 100'				)
        self.queue.append(_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        if not self.queue:
             raise UnderFlowError('The queue is empty'				)
        else:
             lowercase_					:       Tuple     				=					min(self.queue				)
             self.queue.remove(_a				)
             return data





   def __str__(self				)    ->     str:
        return str(self.queue				)



def _UpperCamelCase   (       ):
     lowercase_					:       Any     				=					FixedPriorityQueue()
     fpq.enqueue(0  ,       10							)
     fpq.enqueue(1  ,       70							)
     fpq.enqueue(0  ,       100							)
     fpq.enqueue(2  ,       1							)
     fpq.enqueue(2  ,       5							)
     fpq.enqueue(1  ,       7							)
     fpq.enqueue(2  ,       4							)
     fpq.enqueue(1  ,       64							)
     fpq.enqueue(0  ,       128							)
     print(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)

def _UpperCamelCase   (       ):
     lowercase_					:       Optional[int]     				=					ElementPriorityQueue()
     epq.enqueue(10							)
     epq.enqueue(70							)
     epq.enqueue(100							)
     epq.enqueue(1							)
     epq.enqueue(5							)
     epq.enqueue(7							)
     epq.enqueue(4							)
     epq.enqueue(64							)
     epq.enqueue(128							)
     print(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)


if __name__ == "__main__":
 fixed_priority_queue()
 element_priority_queue()




