



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


_UpperCAmelCase       :	Optional[int]            =logging.get_logger(__name__)

_UpperCAmelCase       :	str            ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCAmelCase       :	Optional[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'
        ),
    },
}
_UpperCAmelCase       :	Union[str, Any]            ={
    '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'
        ),
    },
}
_UpperCAmelCase       :	List[Any]            ={
    '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'
        ),
    },
}

_UpperCAmelCase       :	List[Any]            ={
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
_UpperCAmelCase       :	Dict            ={
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
_UpperCAmelCase       :	Tuple            ={
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


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



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
		snake_case_			=DPRContextEncoderTokenizer



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
		snake_case_			=DPRQuestionEncoderTokenizer


_UpperCAmelCase       :	Optional[int]            =collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

_UpperCAmelCase       :	Union[str, Any]            =collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_UpperCAmelCase       :	List[Any]            =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  lowerCamelCase__    :




		'''simple docstring'''



		def __call__(self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = False	,__lowerCamelCase = False	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						if titles is None and texts is None:
										return super().__call__(
										    __A	,padding=__A	,truncation=__A	,max_length=__A	,return_tensors=__A	,return_attention_mask=__A	,**__A	,)
						elif titles is None or texts is None:
										lowerCAmelCase__  :       str				=  titles if texts is None else texts
										return super().__call__(
										    __A	,__A	,padding=__A	,truncation=__A	,max_length=__A	,return_tensors=__A	,return_attention_mask=__A	,**__A	,)
						lowerCAmelCase__  :       int				=  titles if not isinstance(__A	,__A				) else [titles]
						lowerCAmelCase__  :       List[Any]				=  texts if not isinstance(__A	,__A				) else [texts]
						lowerCAmelCase__  :       Tuple				=  len(__A				)
						lowerCAmelCase__  :       str				=  questions if not isinstance(__A	,__A				) else [questions] * n_passages
						assert len(__A				) == len(
						    __A				), f"""There should be as many titles than texts but got {len(__A				)} titles and {len(__A				)} texts."""
						lowerCAmelCase__  :       int				=  super().__call__(__A	,__A	,padding=__A	,truncation=__A				)["input_ids"]
						lowerCAmelCase__  :       int				=  super().__call__(__A	,add_special_tokens=__A	,padding=__A	,truncation=__A				)["input_ids"]
						lowerCAmelCase__  :       Optional[int]				=  {
						    "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(__A	,__A				)
						    ]
						}
						if return_attention_mask is not False:
										lowerCAmelCase__  :       Dict				=  []
										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__  :       int				=  attention_mask
						return self.pad(__A	,padding=__A	,max_length=__A	,return_tensors=__A				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = 16	,__lowerCamelCase = 64	,__lowerCamelCase = 4	,)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  reader_input["input_ids"]
						lowerCAmelCase__  :       Optional[Any]				=  reader_output[:3]
						lowerCAmelCase__  :       str				=  len(__A				)
						lowerCAmelCase__  :       List[str]				=  sorted(range(__A				)	,reverse=__A	,key=relevance_logits.__getitem__				)
						lowerCAmelCase__  :       List[DPRReaderOutput]				=  []
						for doc_id in sorted_docs:
										lowerCAmelCase__  :       Union[str, Any]				=  list(input_ids[doc_id]				)
										# assuming question & title information is at the beginning of the sequence
										lowerCAmelCase__  :       str				=  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__  :       Optional[int]				=  len(__A				)

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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  []
						for start_index, start_score in enumerate(__A				):
										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__  :       List[str]				=  sorted(__A	,key=lambda __lowerCamelCase				: x[1]	,reverse=__A				)
						lowerCAmelCase__  :       List[Any]				=  []
						for (start_index, end_index), score in scores:
										assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
										lowerCAmelCase__  :       Optional[Any]				=  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(__A				) == top_spans:
														break
						return chosen_span_intervals



@add_end_docstrings(lowerCamelCase__)
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=READER_PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=READER_PRETRAINED_INIT_CONFIGURATION
		snake_case_			=["""input_ids""", """attention_mask"""]
		snake_case_			=DPRReaderTokenizer






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       Dict				=  f"""Input value of [number={number}] must be an integer"""
								raise TypeError(lowerCamelCase_)

				if number < 1:
								lowerCAmelCase__  :       Any				=  f"""Input value of [number={number}] must be > 0"""
								raise ValueError(lowerCamelCase_)

				lowerCAmelCase__  :       List[str]				=  1

				for i in range(1 ,lowerCamelCase_):
								current_number *= 4 * i - 2
								current_number //= i + 1

				return current_number


if __name__ == "__main__":
							import doctest

							doctest.testmod()

