
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


_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": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
_A  =	{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
_A  =	{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


_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       _lowerCAmelCase   (      __a			):
				_lowercase     						=VOCAB_FILES_NAMES
				_lowercase     						=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				_lowercase     						=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase     						=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				_lowercase     						=DPRContextEncoderTokenizer





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=VOCAB_FILES_NAMES
				_lowercase     						=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				_lowercase     						=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase     						=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				_lowercase     						=DPRQuestionEncoderTokenizer


_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    [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       _lowerCAmelCase   :




				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:
										lowerCAmelCase_         =      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							,							)
							lowerCAmelCase_         =      titles if not isinstance(_UpperCamelCase							,							_UpperCamelCase	) else [titles]
							lowerCAmelCase_         =      texts if not isinstance(_UpperCamelCase							,							_UpperCamelCase	) else [texts]
							lowerCAmelCase_         =      len(_UpperCamelCase	)
							lowerCAmelCase_         =      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."""
							lowerCAmelCase_         =      super().__call__(_UpperCamelCase							,							_UpperCamelCase							,							padding=_UpperCamelCase							,							truncation=_UpperCamelCase	)["input_ids"]
							lowerCAmelCase_         =      super().__call__(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase							,							padding=_UpperCamelCase							,							truncation=_UpperCamelCase	)["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(_UpperCamelCase							,							_UpperCamelCase	)
							    ]
							}
							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(_UpperCamelCase							,							padding=_UpperCamelCase							,							max_length=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = 16							,							_UpperCamelCase = 64							,							_UpperCamelCase = 4							,							)							-> List[DPRSpanPrediction]:
							lowerCAmelCase_         =      reader_input["input_ids"]
							lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      reader_output[:3]
							lowerCAmelCase_         =      len(_UpperCamelCase	)
							lowerCAmelCase_         =      sorted(range(_UpperCamelCase	)							,							reverse=_UpperCamelCase							,							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(_UpperCamelCase	)

										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=_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							,							)							-> List[DPRSpanPrediction]:
							lowerCAmelCase_         =      []
							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)	)
							lowerCAmelCase_         =      sorted(_UpperCamelCase							,							key=lambda _UpperCamelCase	: x[1]							,							reverse=_UpperCamelCase	)
							lowerCAmelCase_         =      []
							for (start_index, end_index), score in scores:
										assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
										lowerCAmelCase_         =      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       _lowerCAmelCase   (      __a   , __a			):
				_lowercase     						=VOCAB_FILES_NAMES
				_lowercase     						=READER_PRETRAINED_VOCAB_FILES_MAP
				_lowercase     						=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase     						=READER_PRETRAINED_INIT_CONFIGURATION
				_lowercase     						=['''input_ids''', '''attention_mask''']
				_lowercase     						=DPRReaderTokenizer





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


			if version.parse(hfh.__version__							).release < version.parse("0.11.0"							).release:
						# old versions of hfh don't url-encode the file path
						lowerCAmelCase_         =      quote(__lowerCAmelCase							)
			return hfh.hf_hub_url(__lowerCAmelCase       ,				__lowerCAmelCase       ,				repo_type="dataset"       ,				revision=__lowerCAmelCase							)



