






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

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

__UpperCAmelCase							:   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							:   Union[str, 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							:   str							=  {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
__UpperCAmelCase							:   List[str]							=  {
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
__UpperCAmelCase							:   List[str]							=  {
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


__UpperCAmelCase							:   Optional[Any]							=  {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCAmelCase							:   Optional[int]							=  {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCAmelCase							:   List[Any]							=  {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class 					_snake_case						(			_A ):
	_A          =   VOCAB_FILES_NAMES
	_A          =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	_A          =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A          =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	_A          =   DPRContextEncoderTokenizer




class 					_snake_case						(			_A ):
	_A          =   VOCAB_FILES_NAMES
	_A          =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	_A          =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A          =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	_A          =   DPRQuestionEncoderTokenizer




__UpperCAmelCase							:   Tuple							=  collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

__UpperCAmelCase							:   Dict							=  collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


__UpperCAmelCase							:   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(_A )
class 					_snake_case						:






	def __call__(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = False					,UpperCamelCase = False					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,**UpperCamelCase					,)						->    BatchEncoding:
					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:
									snake_case__      :Tuple         =   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					,)
					snake_case__      :List[str]         =   titles if not isinstance(UpperCamelCase					,UpperCamelCase       ) else [titles]
					snake_case__      :List[Any]         =   texts if not isinstance(UpperCamelCase					,UpperCamelCase       ) else [texts]
					snake_case__      :Optional[Any]         =   len(UpperCamelCase       )
					snake_case__      :Tuple         =   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.'
					snake_case__      :Dict         =   super().__call__(UpperCamelCase					,UpperCamelCase					,padding=UpperCamelCase					,truncation=UpperCamelCase       )["input_ids"]
					snake_case__      :int         =   super().__call__(UpperCamelCase					,add_special_tokens=UpperCamelCase					,padding=UpperCamelCase					,truncation=UpperCamelCase       )["input_ids"]
					snake_case__      :Tuple         =   {
					    "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:
									snake_case__      :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]       )
									snake_case__      :int         =   attention_mask
					return self.pad(UpperCamelCase					,padding=UpperCamelCase					,max_length=UpperCamelCase					,return_tensors=UpperCamelCase       )






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

									snake_case__      :str         =   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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,)						->    List[DPRSpanPrediction]:
					snake_case__      :Any         =   []
					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)       )
					snake_case__      :str         =   sorted(UpperCamelCase					,key=lambda UpperCamelCase       : x[1]					,reverse=UpperCamelCase       )
					snake_case__      :List[str]         =   []
					for (start_index, end_index), score in scores:
									assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
									snake_case__      :str         =   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(_A )
class 					_snake_case						(			_A       ,    _A ):
	_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']
	_A          =   DPRReaderTokenizer







from __future__ import annotations





def 					lowercase_ (    __snake_case     :							list      )     -> float:





				'''simple docstring'''


				if not nums:
								raise ValueError("List is empty"      )
				return sum(__snake_case      ) / len(__snake_case      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()