





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


_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 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Any											=       VOCAB_FILES_NAMES
	a    :				Optional[Any]											=       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	a    :				List[Any]											=       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				List[str]											=       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				str											=       VOCAB_FILES_NAMES
	a    :				List[str]											=       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	a    :				Any											=       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				List[Any]											=       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'''
    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)

    Returns:
        `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(lowerCAmelCase__						)
class 							__lowercase       :







	'''simple docstring'''

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







				'''simple docstring'''





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


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







				'''simple docstring'''





				__lowercase										=							reader_input['''input_ids''']
				__lowercase						,		__lowercase						,		__lowercase										=							reader_output[:3]
				__lowercase										=							len(_lowerCamelCase )
				__lowercase										=							sorted(range(_lowerCamelCase )     ,reverse=_lowerCamelCase     ,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(_lowerCamelCase )

							__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=_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 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							List[DPRSpanPrediction]:







				'''simple docstring'''





				__lowercase										=							[]
				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) )
				__lowercase										=							sorted(_lowerCamelCase     ,key=lambda _lowerCamelCase : x[1]     ,reverse=_lowerCamelCase )
				__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(_lowerCamelCase ) == top_spans:
										break
				return chosen_span_intervals






@add_end_docstrings(lowerCAmelCase__						)
class 							__lowercase       (      lowerCAmelCase__ ,      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Tuple											=       VOCAB_FILES_NAMES
	a    :				List[str]											=       READER_PRETRAINED_VOCAB_FILES_MAP
	a    :				Optional[int]											=       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				Union[str, Any]											=       READER_PRETRAINED_INIT_CONFIGURATION
	a    :				int											=       ["input_ids", "attention_mask"]






'''simple docstring'''



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = "▁"     ,_lowerCamelCase = True     ,_lowerCamelCase = "<unk>"     ,_lowerCamelCase = "</s>"     ,_lowerCamelCase = "<pad>"     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							{
				    '''pad''': {'''id''': 0, '''token''': pad_token},
				    '''eos''': {'''id''': 1, '''token''': eos_token},
				    '''unk''': {'''id''': 2, '''token''': unk_token},
				}

				__lowercase										=							[None] * len(self.special_tokens )
				for token_dict in self.special_tokens.values():
							__lowercase										=							token_dict['''token''']

				__lowercase										=							Tokenizer(Unigram() )

				__lowercase										=							normalizers.Sequence(
				    [
				        normalizers.Nmt(),
				        normalizers.NFKC(),
				        normalizers.Replace(Regex(''' {2,}''' )     ,''' ''' ),
				        normalizers.Lowercase(),
				    ] )
				__lowercase										=							pre_tokenizers.Sequence(
				    [
				        pre_tokenizers.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase ),
				        pre_tokenizers.Digits(individual_digits=_lowerCamelCase ),
				        pre_tokenizers.Punctuation(),
				    ] )
				__lowercase										=							decoders.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase )

				__lowercase										=							TemplateProcessing(
				    single=f"$A {self.special_tokens['eos']['token']}"     ,special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]     ,)

				__lowercase										=							{
				    '''model''': '''SentencePieceUnigram''',
				    '''replacement''': replacement,
				    '''add_prefix_space''': add_prefix_space,
				}

				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							[files]
				self._tokenizer.train(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				self._tokenizer.train_from_iterator(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							json.loads(self._tokenizer.to_str() )

				__lowercase										=							self.special_tokens['''unk''']['''id''']

				__lowercase										=							Tokenizer.from_str(json.dumps(_lowerCamelCase ) )





