





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": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
A_												=     {
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
A_												=     {
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    VOCAB_FILES_NAMES
					A__				 =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					A__				 =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				 =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					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_												=     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(lowercase_   )
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = False	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = 16	,  _lowerCAmelCase = 64	,  _lowerCAmelCase = 4	,  ):
										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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  ):
										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(lowercase_   )
class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_   ):





					"""simple docstring"""


					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_												=     {str(digit): digit**5 for digit in range(1_0)}





def    __UpperCamelCase      (    a) ->int:
					return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a))





def    __UpperCamelCase      (    ) ->int:
					return sum(
					    number
					    for number in range(1000,     1000000)
					    if number == digits_fifth_powers_sum(a))


if __name__ == "__main__":
						print(solution())

