





"""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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowerCamelCase__						= logging.get_logger(__name__)

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

lowerCamelCase__						= {
    "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"
        ),
    },
}
lowerCamelCase__						= {
    "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"
        ),
    },
}
lowerCamelCase__						= {
    "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"
        ),
    },
}

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


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



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Union[str, Any]						  =       VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				:int						  =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__				:List[str]						  =       DPRContextEncoderTokenizer





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				:Union[str, Any]						  =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				:Any						  =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				:Union[str, Any]						  =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__				:Any						  =       DPRQuestionEncoderTokenizer





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

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


lowerCamelCase__						= 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   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


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

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

              _UpperCamelCase   :						Dict    =							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  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							List[int]       , __a :							List[int]       , __a :							int       , __a :							int       , )		->					List[DPRSpanPrediction]:
        _UpperCamelCase   :						Optional[Any]    =							[]
        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)						)
        _UpperCamelCase   :						str    =							sorted(__a       , key=lambda __a						: x[1]       , reverse=__a						)
        _UpperCamelCase   :						Tuple    =							[]
        for (start_index, end_index), score in scores:
              assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
              _UpperCamelCase   :						Tuple    =							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(__a						) == top_spans:
                    break
        return chosen_span_intervals





@add_end_docstrings(_UpperCamelCase )
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	_UpperCamelCase ):







  '''simple docstring'''


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







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
      return topk_filled_outputs


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

print(fill_mask(masked_input, model, tokenizer, topk=3))
