






'''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': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
__lowerCAmelCase        =							{
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
__lowerCAmelCase        =							{
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


__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"""


  _a  : Any      			=     VOCAB_FILES_NAMES
  _a  : Any      			=     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _a  : List[Any]      			=     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : int      			=     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  _a  : Dict      			=     DPRContextEncoderTokenizer





class   __SCREAMING_SNAKE_CASE       (UpperCAmelCase__       ):


  """simple docstring"""


  _a  : List[str]      			=     VOCAB_FILES_NAMES
  _a  : Dict      			=     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _a  : Tuple      			=     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : str      			=     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  _a  : Tuple      			=     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   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """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:
                a_						     =			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   ,	)
         a_						     =			titles if not isinstance(_UpperCAmelCase   ,	_UpperCAmelCase	) else [titles]
         a_						     =			texts if not isinstance(_UpperCAmelCase   ,	_UpperCAmelCase	) else [texts]
         a_						     =			len(_UpperCAmelCase	)
         a_						     =			questions if not isinstance(_UpperCAmelCase   ,	_UpperCAmelCase	) else [questions] * n_passages
         assert len(_UpperCAmelCase	) == len(
             _UpperCAmelCase	), f'There should be as many titles than texts but got {len(_UpperCAmelCase	)} titles and {len(_UpperCAmelCase	)} texts.'
         a_						     =			super().__call__(_UpperCAmelCase   ,	_UpperCAmelCase   ,	padding=_UpperCAmelCase   ,	truncation=_UpperCAmelCase	)['input_ids']
         a_						     =			super().__call__(_UpperCAmelCase   ,	add_special_tokens=_UpperCAmelCase   ,	padding=_UpperCAmelCase   ,	truncation=_UpperCAmelCase	)['input_ids']
         a_						     =			{
             '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:
                a_						     =			[]
                for input_ids in encoded_inputs["input_ids"]:
                       attention_mask.append([int(input_id != self.pad_token_id	) for input_id in input_ids]	)
                a_						     =			attention_mask
         return self.pad(_UpperCAmelCase   ,	padding=_UpperCAmelCase   ,	max_length=_UpperCAmelCase   ,	return_tensors=_UpperCAmelCase	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = 16   ,	UpperCamelCase__ = 64   ,	UpperCamelCase__ = 4   ,	):






         """simple docstring"""


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

                a_						     =			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    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			[]
         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)	)
         a_						     =			sorted(_UpperCAmelCase   ,	key=lambda UpperCamelCase__	: x[1]   ,	reverse=_UpperCAmelCase	)
         a_						     =			[]
         for (start_index, end_index), score in scores:
                assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
                a_						     =			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(_UpperCAmelCase	) == top_spans:
                       break
         return chosen_span_intervals





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


  """simple docstring"""


  _a  : Union[str, Any]      			=     VOCAB_FILES_NAMES
  _a  : List[str]      			=     READER_PRETRAINED_VOCAB_FILES_MAP
  _a  : str      			=     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : Union[str, Any]      			=     READER_PRETRAINED_INIT_CONFIGURATION
  _a  : Optional[Any]      			=     ['''input_ids''', '''attention_mask''']
  _a  : Optional[Any]      			=     DPRReaderTokenizer








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_pegasus import PegasusTokenizer
else:
	A			:						List[Any]        =  None


A			:						Optional[Any]        =  logging.get_logger(__name__)


A			:						str        =  '▁'

A			:						Optional[Any]        =  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A			:						Any        =  {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

A			:						Optional[int]        =  {
    'google/pegasus-xsum': 5_1_2,
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					PRETRAINED_VOCAB_FILES_MAP
					A__     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					PegasusTokenizer
					A__     =					['''input_ids''', '''attention_mask''']

					def __init__(self       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any]=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		Union[str, Any]="<pad>" ,   _UpperCAmelCase       :		int="</s>" ,   _UpperCAmelCase       :		int="<unk>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_2>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_1>" ,   _UpperCAmelCase       :		Optional[Any]=None ,   _UpperCAmelCase       :		Dict=103 ,   **_UpperCAmelCase       :		Dict ,   )		->  Dict:




							"""simple docstring"""


							lowercase__	     =						offset

							if additional_special_tokens is not None:
									if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											raise TypeError(
											    f'''additional_special_tokens should be of type {type(_UpperCAmelCase						)}, but is'''
											    f''' {type(_UpperCAmelCase						)}'''						)

									lowercase__	     =						(
									    ([mask_token_sent] + additional_special_tokens)
									    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
									    else additional_special_tokens
									)
									# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
									additional_special_tokens_extended += [
									    f'''<unk_{i}>''' for i in range(len(_UpperCAmelCase						) ,   self.offset - 1						)
									]

									if len(set(_UpperCAmelCase						)						) != len(_UpperCAmelCase						):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''						)
									lowercase__	     =						additional_special_tokens_extended
							else:
									lowercase__	     =						[mask_token_sent] if mask_token_sent is not None else []
									additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 ,   self.offset						)]

							super().__init__(
							    _UpperCAmelCase ,   tokenizer_file=_UpperCAmelCase ,   pad_token=_UpperCAmelCase ,   eos_token=_UpperCAmelCase ,   unk_token=_UpperCAmelCase ,   mask_token=_UpperCAmelCase ,   mask_token_sent=_UpperCAmelCase ,   offset=_UpperCAmelCase ,   additional_special_tokens=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						vocab_file
							lowercase__	     =						False if not self.vocab_file else True

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Tuple						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						set(self.all_special_ids						)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special

							if all_special_ids != set(range(len(self.additional_special_tokens						) + 3						)						):
									raise ValueError(
									    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
									    f''' {len(self.additional_special_tokens						)} additional_special_tokens, but got {all_special_ids}'''						)

							return [1 if x in all_special_ids else 0 for x in seq]

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List ,   _UpperCAmelCase       :		Optional[List] = None ,   _UpperCAmelCase       :		bool = False						)		->  List[int]:




							"""simple docstring"""


							if already_has_special_tokens:
									return self._special_token_mask(_UpperCAmelCase						)
							elif token_ids_a is None:
									return self._special_token_mask(_UpperCAmelCase						) + [1]
							else:
									return self._special_token_mask(token_ids_a + token_ids_a						) + [1]

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any]=None						)		->  List[int]:




							"""simple docstring"""


							if token_ids_a is None:
									return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]


					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
									    """tokenizer."""						)

							if not os.path.isdir(_UpperCAmelCase						):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
									return
							lowercase__	     =						os.path.join(
							    _UpperCAmelCase ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
									copyfile(self.vocab_file ,   _UpperCAmelCase						)

							return (out_vocab_file,)

