

'''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


SCREAMING_SNAKE_CASE							:				str     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Optional[int]     	=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE							:				int     	=			{
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE							:				int     	=			{
    "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"
        ),
    },
}

SCREAMING_SNAKE_CASE							:				List[Any]     	=			{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE							:				List[str]     	=			{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE							:				Dict     	=			{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE							:				str     	=			{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class    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    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


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

SCREAMING_SNAKE_CASE							:				str     	=			collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


SCREAMING_SNAKE_CASE							:				Dict     	=			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    snake_case     :






				"""simple docstring"""



				def __call__(    self,     _lowercase,     _lowercase = None,     _lowercase = None,     _lowercase = False,     _lowercase = False,     _lowercase = None,     _lowercase = None,     _lowercase = None,     **_lowercase,     )     ->							BatchEncoding:
											if titles is None and texts is None:
																		return super().__call__(
																		    _lowercase,     padding=_lowercase,     truncation=_lowercase,     max_length=_lowercase,     return_tensors=_lowercase,     return_attention_mask=_lowercase,     **_lowercase,     )
											elif titles is None or texts is None:
																		SCREAMING_SNAKE_CASE_				=				titles if texts is None else texts
																		return super().__call__(
																		    _lowercase,     _lowercase,     padding=_lowercase,     truncation=_lowercase,     max_length=_lowercase,     return_tensors=_lowercase,     return_attention_mask=_lowercase,     **_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				titles if not isinstance(_lowercase,     _lowercase       ) else [titles]
											SCREAMING_SNAKE_CASE_				=				texts if not isinstance(_lowercase,     _lowercase       ) else [texts]
											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				questions if not isinstance(_lowercase,     _lowercase       ) else [questions] * n_passages
											if len(_lowercase       ) != len(_lowercase       ):
																		raise ValueError(
																		    f"""There should be as many titles than texts but got {len(_lowercase       )} titles and {len(_lowercase       )} texts."""       )
											SCREAMING_SNAKE_CASE_				=				super().__call__(_lowercase,     _lowercase,     padding=_lowercase,     truncation=_lowercase       )['input_ids']
											SCREAMING_SNAKE_CASE_				=				super().__call__(_lowercase,     add_special_tokens=_lowercase,     padding=_lowercase,     truncation=_lowercase       )['input_ids']
											SCREAMING_SNAKE_CASE_				=				{
											    '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(_lowercase,     _lowercase       )
											    ]
											}
											if return_attention_mask is not False:
																		SCREAMING_SNAKE_CASE_				=				[]
																		for input_ids in encoded_inputs["input_ids"]:
																									attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
																		SCREAMING_SNAKE_CASE_				=				attention_mask
											return self.pad(_lowercase,     padding=_lowercase,     max_length=_lowercase,     return_tensors=_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = 16,     _lowercase = 64,     _lowercase = 4,     )     ->							List[DPRSpanPrediction]:
											SCREAMING_SNAKE_CASE_				=				reader_input['input_ids']
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				reader_output[:3]
											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				sorted(range(_lowercase       ),     reverse=_lowercase,     key=relevance_logits.__getitem__       )
											SCREAMING_SNAKE_CASE_				=				[]
											for doc_id in sorted_docs:
																		SCREAMING_SNAKE_CASE_				=				list(input_ids[doc_id]       )
																		# assuming question & title information is at the beginning of the sequence
																		SCREAMING_SNAKE_CASE_				=				sequence_ids.index(self.sep_token_id,     2       ) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																									SCREAMING_SNAKE_CASE_				=				sequence_ids.index(self.pad_token_id       )
																		else:
																									SCREAMING_SNAKE_CASE_				=				len(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				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=_lowercase,     top_spans=_lowercase,     )
																		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=_lowercase,     start_index=_lowercase,     end_index=_lowercase,     text=self.decode(sequence_ids[start_index : end_index + 1]       ),     )       )
																		if len(_lowercase       ) >= num_spans:
																									break
											return nbest_spans_predictions[:num_spans]





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							List[DPRSpanPrediction]:
											SCREAMING_SNAKE_CASE_				=				[]
											for start_index, start_score in enumerate(_lowercase       ):
																		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)       )
											SCREAMING_SNAKE_CASE_				=				sorted(_lowercase,     key=lambda _lowercase       : x[1],     reverse=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				[]
											for (start_index, end_index), score in scores:
																		if start_index > end_index:
																									raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""       )
																		SCREAMING_SNAKE_CASE_				=				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(_lowercase       ) == top_spans:
																									break
											return chosen_span_intervals






@add_end_docstrings(lowercase_		)
class    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"""]








'''simple docstring'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


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





