

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


lowercase_			=  logging.get_logger(__name__)

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

lowercase_			=  {
    "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"
        ),
    },
}
lowercase_			=  {
    "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"
        ),
    },
}
lowercase_			=  {
    "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"
        ),
    },
}

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


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


class 			A						(							_UpperCamelCase			):







				"""simple docstring"""





				lowerCamelCase		     =							VOCAB_FILES_NAMES
				lowerCamelCase		     =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase		     =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase		     =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				lowerCamelCase		     =							DPRContextEncoderTokenizer




class 			A						(							_UpperCamelCase			):







				"""simple docstring"""





				lowerCamelCase		     =							VOCAB_FILES_NAMES
				lowerCamelCase		     =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase		     =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase		     =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				lowerCamelCase		     =							DPRQuestionEncoderTokenizer




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

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


lowercase_			=  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 			A						:







				"""simple docstring"""





				def __call__(				self			:	Any,lowercase_			:	Optional[int],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],)->   List[str]:





								'''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 						snake_case__					(				self			:	Any,lowercase_			:	BatchEncoding,lowercase_			:	DPRReaderOutput,lowercase_			:	int = 1_6,lowercase_			:	int = 6_4,lowercase_			:	int = 4,)->   Union[str, Any]:





								'''simple docstring'''







								A__   				=       reader_input['input_ids']
								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 						snake_case__					(				self			:	Any,lowercase_			:	List[int],lowercase_			:	List[int],lowercase_			:	int,lowercase_			:	int,)->   Any:





								'''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 lowercase_		: 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 			A						(							_UpperCamelCase   ,							_UpperCamelCase			):







				"""simple docstring"""





				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']
				lowerCamelCase		     =							DPRReaderTokenizer




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

