


'''simple docstring'''

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


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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
					UpperCAmelCase 					=				VOCAB_FILES_NAMES
					UpperCAmelCase 					=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase 					=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase 					=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
					UpperCAmelCase 					=				VOCAB_FILES_NAMES
					UpperCAmelCase 					=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase 					=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase 					=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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    ```\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 						__SCREAMING_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:
															_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
										if len(__UpperCamelCase		) != len(__UpperCamelCase		):
															raise ValueError(
															    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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 16     ,							__UpperCamelCase = 64     ,							__UpperCamelCase = 4     ,							)       ->    List[DPRSpanPrediction]:
										_a						=		reader_input["input_ids"]
										_a			,			_a			,			_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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							)       ->    List[DPRSpanPrediction]:
										_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 __UpperCamelCase		: x[1]     ,							reverse=__UpperCamelCase		)
										_a						=		[]
										for (start_index, end_index), score in scores:
															if start_index > end_index:
																				raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"		)
															_a						=		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(__UpperCamelCase		) == top_spans:
																				break
										return chosen_span_intervals







@add_end_docstrings(lowerCamelCase__ )
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			lowerCamelCase__ ):
					UpperCAmelCase 					=				VOCAB_FILES_NAMES
					UpperCAmelCase 					=				READER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase 					=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase 					=				READER_PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase 					=				['''input_ids''', '''attention_mask''']





'''simple docstring'''

def __UpperCamelCase   (			__lowerCamelCase		: int = 400_0000				)      ->     int:







     '''simple docstring'''






     _a						=		[]
     _a			,			_a						=		0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(__lowerCamelCase				)
          _a			,			_a						=		b, a + b
     return sum(__lowerCamelCase				)


if __name__ == "__main__":
   print(f'''{solution() = }''')

