





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


_a 						=      logging.get_logger(__name__)

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

_a 						=      {
    "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"
        ),
    },
}
_a 						=      {
    "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"
        ),
    },
}
_a 						=      {
    "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"
        ),
    },
}

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


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






class 					__A    (    lowerCAmelCase ):

			'''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






class 					__A    (    lowerCAmelCase ):

			'''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



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

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


_a 						=      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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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 					__A    :

			'''simple docstring'''







			def __call__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				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:
					lowerCamelCase__								=		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       ,  )
				lowerCamelCase__								=		titles if not isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							) else [titles]
				lowerCamelCase__								=		texts if not isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							) else [texts]
				lowerCamelCase__								=		len(__lowerCAmelCase							)
				lowerCamelCase__								=		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.'							)
				lowerCamelCase__								=		super().__call__(__lowerCAmelCase       ,  __lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  truncation=__lowerCAmelCase							)['''input_ids''']
				lowerCamelCase__								=		super().__call__(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  truncation=__lowerCAmelCase							)['''input_ids''']
				lowerCamelCase__								=		{
				    '''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:
					lowerCamelCase__								=		[]
					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__								=		attention_mask
				return self.pad(__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  max_length=__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = 1_6       ,  __lowerCAmelCase = 6_4       ,  __lowerCAmelCase = 4       ,  ):



				'''simple docstring'''







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

					lowerCamelCase__								=		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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				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)							)
				lowerCamelCase__								=		sorted(__lowerCAmelCase       ,  key=lambda __lowerCAmelCase							: x[1]       ,  reverse=__lowerCAmelCase							)
				lowerCamelCase__								=		[]
				for (start_index, end_index), score in scores:
					if start_index > end_index:
						raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'							)
					lowerCamelCase__								=		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(lowerCAmelCase )
class 					__A    (    lowerCAmelCase			,				lowerCAmelCase ):

			'''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"""]










import numpy as np

from transformers import Pipeline





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







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






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.model(**__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		model_outputs.logits[0].numpy()
				lowerCamelCase__								=		softmax(__lowerCAmelCase							)

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



