

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					:  List[Any]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  List[Any]             =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''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					:  Dict             =      {
    '''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					:  List[str]             =      {
    '''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					:  Union[str, Any]             =      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
_SCREAMING_SNAKE_CASE					:  int             =      {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


_SCREAMING_SNAKE_CASE					:  str             =      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
_SCREAMING_SNAKE_CASE					:  List[Any]             =      {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= VOCAB_FILES_NAMES
  a									= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a									= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a									= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= VOCAB_FILES_NAMES
  a									= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a									= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a									= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

_SCREAMING_SNAKE_CASE					:  Optional[int]             =      collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


_SCREAMING_SNAKE_CASE					:  List[Any]             =      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(A__	)
class 	UpperCAmelCase__		:






  """simple docstring"""


  def __call__(							self						:		Optional[int]     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		Optional[str] = None     ,       __lowerCamelCase						:		Optional[str] = None     ,       __lowerCamelCase						:		Union[bool, str] = False     ,       __lowerCamelCase						:		Union[bool, str] = False     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[Union[str, TensorType]] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       **__lowerCamelCase						:		List[str]     ,       )		->							BatchEncoding:
       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:
            SCREAMING_SNAKE_CASE__   =   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     ,       )
       SCREAMING_SNAKE_CASE__   =   titles if not isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else [titles]
       SCREAMING_SNAKE_CASE__   =   texts if not isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else [texts]
       SCREAMING_SNAKE_CASE__   =   len(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   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.''' )
       SCREAMING_SNAKE_CASE__   =   super().__call__(__lowerCamelCase     ,       __lowerCamelCase     ,       padding=__lowerCamelCase     ,       truncation=__lowerCamelCase )['''input_ids''']
       SCREAMING_SNAKE_CASE__   =   super().__call__(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase     ,       padding=__lowerCamelCase     ,       truncation=__lowerCamelCase )['''input_ids''']
       SCREAMING_SNAKE_CASE__   =   {
           '''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:
            SCREAMING_SNAKE_CASE__   =   []
            for input_ids in encoded_inputs["input_ids"]:
                 attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
            SCREAMING_SNAKE_CASE__   =   attention_mask
       return self.pad(__lowerCamelCase     ,       padding=__lowerCamelCase     ,       max_length=__lowerCamelCase     ,       return_tensors=__lowerCamelCase )






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

            SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		List[int]     ,       __lowerCamelCase						:		List[int]     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		int     ,       )		->							List[DPRSpanPrediction]:
       SCREAMING_SNAKE_CASE__   =   []
       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) )
       SCREAMING_SNAKE_CASE__   =   sorted(__lowerCamelCase     ,       key=lambda __lowerCamelCase : x[1]     ,       reverse=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   []
       for (start_index, end_index), score in scores:
            if start_index > end_index:
                 raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]''' )
            SCREAMING_SNAKE_CASE__   =   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(A__	)
class 	UpperCAmelCase__		(	A__						,					A__	):






  """simple docstring"""


  a									= VOCAB_FILES_NAMES
  a									= READER_PRETRAINED_VOCAB_FILES_MAP
  a									= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a									= READER_PRETRAINED_INIT_CONFIGURATION
  a									= ["input_ids", "attention_mask"]





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "transfo-xl"
  a									= ["mems"]
  a									= {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Any     ,       __lowerCamelCase						:		int=26_7735     ,       __lowerCamelCase						:		Any=[2_0000, 4_0000, 20_0000]     ,       __lowerCamelCase						:		Dict=1024     ,       __lowerCamelCase						:		Optional[int]=1024     ,       __lowerCamelCase						:		List[Any]=16     ,       __lowerCamelCase						:		Union[str, Any]=64     ,       __lowerCamelCase						:		Dict=4096     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Tuple=18     ,       __lowerCamelCase						:		Optional[int]=1600     ,       __lowerCamelCase						:		str=1000     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		int=-1     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		str="normal"     ,       __lowerCamelCase						:		List[str]=0.01     ,       __lowerCamelCase						:		Any=0.01     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		List[str]=1e-5     ,       __lowerCamelCase						:		Union[str, Any]=0     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   []
       self.cutoffs.extend(__lowerCamelCase )
       if proj_share_all_but_first:
            SCREAMING_SNAKE_CASE__   =   [False] + [True] * len(self.cutoffs )
       else:
            SCREAMING_SNAKE_CASE__   =   [False] + [False] * len(self.cutoffs )
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   d_embed
       SCREAMING_SNAKE_CASE__   =   d_head
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   div_val
       SCREAMING_SNAKE_CASE__   =   pre_lnorm
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   same_length
       SCREAMING_SNAKE_CASE__   =   attn_type
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   sample_softmax
       SCREAMING_SNAKE_CASE__   =   adaptive
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   dropatt
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   init
       SCREAMING_SNAKE_CASE__   =   init_range
       SCREAMING_SNAKE_CASE__   =   proj_init_std
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       super().__init__(eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		str )		->							Dict:
       # Message copied from Transformer-XL documentation
       logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1







  @max_position_embeddings.setter
  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Any )		->							List[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


