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


__magic_name__								=logging.get_logger(__name__)

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

__magic_name__								={
    "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"
        ),
    },
}
__magic_name__								={
    "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"
        ),
    },
}
__magic_name__								={
    "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"
        ),
    },
}

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


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



class     _A   (    __lowerCAmelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	Any						=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	List[str]						=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	List[str]						=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE_	:	Tuple						=DPRContextEncoderTokenizer



class     _A   (    __lowerCAmelCase							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	Union[str, Any]						=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	Dict						=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE_	:	Tuple						=DPRQuestionEncoderTokenizer



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

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


__magic_name__								=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(__lowerCAmelCase							)
class     _A   :






  def __call__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      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:
            UpperCamelCase__										=      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__   ,				)
       UpperCamelCase__										=      titles if not isinstance(lowerCamelCase__   ,				lowerCamelCase__	) else [titles]
       UpperCamelCase__										=      texts if not isinstance(lowerCamelCase__   ,				lowerCamelCase__	) else [texts]
       UpperCamelCase__										=      len(lowerCamelCase__	)
       UpperCamelCase__										=      questions if not isinstance(lowerCamelCase__   ,				lowerCamelCase__	) else [questions] * n_passages
       assert len(lowerCamelCase__	) == len(
           lowerCamelCase__	), F"There should be as many titles than texts but got {len(lowerCamelCase__	)} titles and {len(lowerCamelCase__	)} texts."
       UpperCamelCase__										=      super().__call__(lowerCamelCase__   ,				lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__	)['''input_ids''']
       UpperCamelCase__										=      super().__call__(lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__	)['''input_ids''']
       UpperCamelCase__										=      {
           '''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:
            UpperCamelCase__										=      []
            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__										=      attention_mask
       return self.pad(lowerCamelCase__   ,				padding=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				return_tensors=lowerCamelCase__	)






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





       '''simple docstring'''





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

            UpperCamelCase__										=      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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				)		->      List[DPRSpanPrediction]:





       '''simple docstring'''





       UpperCamelCase__										=      []
       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)	)
       UpperCamelCase__										=      sorted(lowerCamelCase__   ,				key=lambda SCREAMING_SNAKE_CASE_	: x[1]   ,				reverse=lowerCamelCase__	)
       UpperCamelCase__										=      []
       for (start_index, end_index), score in scores:
            assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
            UpperCamelCase__										=      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(lowerCamelCase__	) == top_spans:
                 break
       return chosen_span_intervals



@add_end_docstrings(__lowerCAmelCase							)
class     _A   (    __lowerCAmelCase	, __lowerCAmelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	Union[str, Any]						=READER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	List[str]						=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=READER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE_	:	List[str]						=["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=DPRReaderTokenizer







from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 768   ,				)		->      Tuple:





       '''simple docstring'''





       super().__init__()

       UpperCamelCase__										=      nn.Parameter(torch.zeros(1   ,				SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(torch.ones(1   ,				SCREAMING_SNAKE_CASE_	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      nn.Parameter(self.mean.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(self.std.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       return self






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds - self.mean) * 1.0 / self.std
       return embeds






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds * self.std) + self.mean
       return embeds



