





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


__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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      VOCAB_FILES_NAMES
 lowercase_		=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      VOCAB_FILES_NAMES
 lowercase_		=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=      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(__SCREAMING_SNAKE_CASE   )
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __call__(self    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[str] = None	,			UpperCAmelCase_    :       Optional[str] = None	,			UpperCAmelCase_    :       Union[bool, str] = False	,			UpperCAmelCase_    :       Union[bool, str] = False	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Optional[Union[str, TensorType]] = None	,			UpperCAmelCase_    :       Optional[bool] = None	,			**UpperCAmelCase_    :       int	,			)	->BatchEncoding:



   '''simple docstring'''



   if titles is None and texts is None:
     return super().__call__(
         UpperCAmelCase_	,			padding=UpperCAmelCase_	,			truncation=UpperCAmelCase_	,			max_length=UpperCAmelCase_	,			return_tensors=UpperCAmelCase_	,			return_attention_mask=UpperCAmelCase_	,			**UpperCAmelCase_	,			)
   elif titles is None or texts is None:
     lowerCamelCase__:				Union[str, Any]        =titles if texts is None else texts
     return super().__call__(
         UpperCAmelCase_	,			UpperCAmelCase_	,			padding=UpperCAmelCase_	,			truncation=UpperCAmelCase_	,			max_length=UpperCAmelCase_	,			return_tensors=UpperCAmelCase_	,			return_attention_mask=UpperCAmelCase_	,			**UpperCAmelCase_	,			)
   lowerCamelCase__:				Dict        =titles if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_) else [titles]
   lowerCamelCase__:				List[Any]        =texts if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_) else [texts]
   lowerCamelCase__:				str        =len(UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =questions if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_) else [questions] * n_passages
   if len(UpperCAmelCase_) != len(UpperCAmelCase_):
     raise ValueError(
         F"""There should be as many titles than texts but got {len(UpperCAmelCase_)} titles and {len(UpperCAmelCase_)} texts.""")
   lowerCamelCase__:				Any        =super().__call__(UpperCAmelCase_	,			UpperCAmelCase_	,			padding=UpperCAmelCase_	,			truncation=UpperCAmelCase_)["input_ids"]
   lowerCamelCase__:				List[str]        =super().__call__(UpperCAmelCase_	,			add_special_tokens=UpperCAmelCase_	,			padding=UpperCAmelCase_	,			truncation=UpperCAmelCase_)["input_ids"]
   lowerCamelCase__:				List[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(UpperCAmelCase_	,			UpperCAmelCase_)
       ]
   }
   if return_attention_mask is not False:
     lowerCamelCase__:				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])
     lowerCamelCase__:				List[Any]        =attention_mask
   return self.pad(UpperCAmelCase_	,			padding=UpperCAmelCase_	,			max_length=UpperCAmelCase_	,			return_tensors=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       BatchEncoding	,			UpperCAmelCase_    :       DPRReaderOutput	,			UpperCAmelCase_    :       int = 16	,			UpperCAmelCase_    :       int = 64	,			UpperCAmelCase_    :       int = 4	,			)	->List[DPRSpanPrediction]:



   '''simple docstring'''



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

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       List[int]	,			UpperCAmelCase_    :       List[int]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			)	->List[DPRSpanPrediction]:



   '''simple docstring'''



   lowerCamelCase__:				str        =[]
   for start_index, start_score in enumerate(UpperCAmelCase_):
     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__:				str        =sorted(UpperCAmelCase_	,			key=lambda UpperCAmelCase_: x[1]	,			reverse=UpperCAmelCase_)
   lowerCamelCase__:				Dict        =[]
   for (start_index, end_index), score in scores:
     if start_index > end_index:
       raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
     lowerCamelCase__:				Optional[int]        =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(UpperCAmelCase_) == top_spans:
       break
   return chosen_span_intervals



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







 '''simple docstring'''
 lowercase_		=      VOCAB_FILES_NAMES
 lowercase_		=      READER_PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=      READER_PRETRAINED_INIT_CONFIGURATION
 lowercase_		=      ["input_ids", "attention_mask"]








import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A     =    logging.getLogger(__name__)

if __name__ == "__main__":
     __A     =    argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0522, type=int)
     __A     =    parser.parse_args()

     logger.info(f'Loading data from {args.data_file}')
     with open(args.data_file, "rb") as fp:
          __A     =    pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     __A     =    Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     __A     =    [0] * args.vocab_size
     for k, v in counter.items():
          __A     =    v

     logger.info(f'Dump to {args.token_counts_dump}')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)

