





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


_snake_case           =			logging.get_logger(__name__)

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

_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"""
        ),
    },
}
_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"""
        ),
    },
}
_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"""
        ),
    },
}

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


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





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_snake_case           =			collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

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


_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(lowercase_ )
class  lowerCAmelCase							:







  def __call__(      self  :Dict   ,							_lowercase  :Dict   ,							_lowercase  :Optional[str] = None   ,							_lowercase  :Optional[str] = None   ,							_lowercase  :Union[bool, str] = False   ,							_lowercase  :Union[bool, str] = False   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Optional[Union[str, TensorType]] = None   ,							_lowercase  :Optional[bool] = None   ,							**_lowercase  :List[Any]   ,							):






     '''simple docstring'''





     if titles is None and texts is None:
        return super().__call__(
            _lowercase   ,							padding=_lowercase   ,							truncation=_lowercase   ,							max_length=_lowercase   ,							return_tensors=_lowercase   ,							return_attention_mask=_lowercase   ,							**_lowercase   ,							)
     elif titles is None or texts is None:
        lowercase__			       =  titles if texts is None else texts
        return super().__call__(
            _lowercase   ,							_lowercase   ,							padding=_lowercase   ,							truncation=_lowercase   ,							max_length=_lowercase   ,							return_tensors=_lowercase   ,							return_attention_mask=_lowercase   ,							**_lowercase   ,							)
     lowercase__			       =  titles if not isinstance(_lowercase   ,							_lowercase ) else [titles]
     lowercase__			       =  texts if not isinstance(_lowercase   ,							_lowercase ) else [texts]
     lowercase__			       =  len(_lowercase )
     lowercase__			       =  questions if not isinstance(_lowercase   ,							_lowercase ) else [questions] * n_passages
     if len(_lowercase ) != len(_lowercase ):
        raise ValueError(
            f'''There should be as many titles than texts but got {len(_lowercase )} titles and {len(_lowercase )} texts.''' )
     lowercase__			       =  super().__call__(_lowercase   ,							_lowercase   ,							padding=_lowercase   ,							truncation=_lowercase )["input_ids"]
     lowercase__			       =  super().__call__(_lowercase   ,							add_special_tokens=_lowercase   ,							padding=_lowercase   ,							truncation=_lowercase )["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(_lowercase   ,							_lowercase )
         ]
     }
     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(_lowercase   ,							padding=_lowercase   ,							max_length=_lowercase   ,							return_tensors=_lowercase )







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :BatchEncoding   ,							_lowercase  :DPRReaderOutput   ,							_lowercase  :int = 16   ,							_lowercase  :int = 64   ,							_lowercase  :int = 4   ,							):






     '''simple docstring'''





     lowercase__			       =  reader_input["input_ids"]
     lowercase__       ,							lowercase__       ,							lowercase__			       =  reader_output[:3]
     lowercase__			       =  len(_lowercase )
     lowercase__			       =  sorted(range(_lowercase )   ,							reverse=_lowercase   ,							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(_lowercase )

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







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :List[int]   ,							_lowercase  :List[int]   ,							_lowercase  :int   ,							_lowercase  :int   ,							):






     '''simple docstring'''





     lowercase__			       =  []
     for start_index, start_score in enumerate(_lowercase ):
        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(_lowercase   ,							key=lambda _lowercase : x[1]   ,							reverse=_lowercase )
     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(_lowercase ) == top_spans:
           break
     return chosen_span_intervals





@add_end_docstrings(lowercase_ )
class  lowerCAmelCase							(       lowercase_							,							lowercase_ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      READER_PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      READER_PRETRAINED_INIT_CONFIGURATION
  __lowerCamelCase		=      ['input_ids', 'attention_mask']













import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
  from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm


def _A       (   __magic_name__	):
   if is_torch_version("<"  , "2.0.0"	) or not hasattr(__magic_name__  , "_dynamo"	):
      return False
   return isinstance(__magic_name__  , torch._dynamo.eval_frame.OptimizedModule	)


def _A       (   __magic_name__  , __magic_name__ = True	):
   lowercase__			       =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

   lowercase__			       =  is_compiled_module(__magic_name__	)
   if is_compiled:
      lowercase__			       =  model
      lowercase__			       =  model._orig_mod

   if is_deepspeed_available():
      options += (DeepSpeedEngine,)

   while isinstance(__magic_name__  , __magic_name__	):
      lowercase__			       =  model.module

   if not keep_fpaa_wrapper:
      lowercase__			       =  getattr(__magic_name__  , "forward"	)
      lowercase__			       =  model.__dict__.pop("_original_forward"  , __magic_name__	)
      if original_forward is not None:
         while hasattr(__magic_name__  , "__wrapped__"	):
            lowercase__			       =  forward.__wrapped__
            if forward == original_forward:
               break
         lowercase__			       =  forward
      if getattr(__magic_name__  , "_converted_to_transformer_engine"  , __magic_name__	):
         convert_model(__magic_name__  , to_transformer_engine=__magic_name__	)

   if is_compiled:
      lowercase__			       =  model
      lowercase__			       =  compiled_model

   return model


def _A       (   ):
   PartialState().wait_for_everyone()


def _A       (   __magic_name__  , __magic_name__	):
   if PartialState().distributed_type == DistributedType.TPU:
      xm.save(__magic_name__  , __magic_name__	)
   elif PartialState().local_process_index == 0:
      torch.save(__magic_name__  , __magic_name__	)


@contextmanager
def _A       (   **__magic_name__	):
   for key, value in kwargs.items():
      lowercase__			       =  str(__magic_name__	)

   yield

   for key in kwargs:
      if key.upper() in os.environ:
         del os.environ[key.upper()]


def _A       (   __magic_name__	):
   if not hasattr(__magic_name__  , "__qualname__"	) and not hasattr(__magic_name__  , "__name__"	):
      lowercase__			       =  getattr(__magic_name__  , "__class__"  , __magic_name__	)
   if hasattr(__magic_name__  , "__qualname__"	):
      return obj.__qualname__
   if hasattr(__magic_name__  , "__name__"	):
      return obj.__name__
   return str(__magic_name__	)


def _A       (   __magic_name__  , __magic_name__	):
   for key, value in source.items():
      if isinstance(__magic_name__  , __magic_name__	):
         lowercase__			       =  destination.setdefault(__magic_name__  , {}	)
         merge_dicts(__magic_name__  , __magic_name__	)
      else:
         lowercase__			       =  value

   return destination







def _A       (   __magic_name__ = None	):
   if port is None:
      lowercase__			       =  2_9500
   with socket.socket(socket.AF_INET  , socket.SOCK_STREAM	) as s:
      return s.connect_ex(("localhost", port)	) == 0






