"""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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowerCAmelCase__  :  Optional[int]						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  Tuple						     =	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__  :  Optional[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'
        ),
    },
}
lowerCAmelCase__  :  List[Any]						     =	{
    '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'
        ),
    },
}
lowerCAmelCase__  :  List[Any]						     =	{
    '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'
        ),
    },
}

lowerCAmelCase__  :  Optional[int]						     =	{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowerCAmelCase__  :  Any						     =	{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
lowerCAmelCase__  :  Union[str, Any]						     =	{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


lowerCAmelCase__  :  Optional[Any]						     =	{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__  :  List[str]						     =	{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__  :  str						     =	{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = VOCAB_FILES_NAMES
					snake_case__				    = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					snake_case__				    = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__				    = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
					snake_case__				    = DPRContextEncoderTokenizer






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = VOCAB_FILES_NAMES
					snake_case__				    = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					snake_case__				    = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__				    = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
					snake_case__				    = DPRQuestionEncoderTokenizer






lowerCAmelCase__  :  Optional[int]						     =	collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

lowerCAmelCase__  :  Tuple						     =	collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCAmelCase__  :  Tuple						     =	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(__UpperCAmelCase				)
class 				snake_case       :




					"""simple docstring"""

					def __call__(				self   :	Optional[int]    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Union[bool, str] = False    ,lowerCamelCase__   :	Union[bool, str] = False    ,lowerCamelCase__   :	Optional[int] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	Optional[bool] = None    ,**lowerCamelCase__   :	List[Any]    ,):
									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:
													UpperCAmelCase__             =							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__    ,)
									UpperCAmelCase__             =							titles if not isinstance(lowerCamelCase__    ,lowerCamelCase__			) else [titles]
									UpperCAmelCase__             =							texts if not isinstance(lowerCamelCase__    ,lowerCamelCase__			) else [texts]
									UpperCAmelCase__             =							len(lowerCamelCase__			)
									UpperCAmelCase__             =							questions if not isinstance(lowerCamelCase__    ,lowerCamelCase__			) else [questions] * n_passages
									assert len(lowerCamelCase__			) == len(
									    lowerCamelCase__			), f'''There should be as many titles than texts but got {len(lowerCamelCase__			)} titles and {len(lowerCamelCase__			)} texts.'''
									UpperCAmelCase__             =							super().__call__(lowerCamelCase__    ,lowerCamelCase__    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__			)['input_ids']
									UpperCAmelCase__             =							super().__call__(lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__			)['input_ids']
									UpperCAmelCase__             =							{
									    '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:
													UpperCAmelCase__             =							[]
													for input_ids in encoded_inputs["input_ids"]:
																	attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
													UpperCAmelCase__             =							attention_mask
									return self.pad(lowerCamelCase__    ,padding=lowerCamelCase__    ,max_length=lowerCamelCase__    ,return_tensors=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	BatchEncoding    ,lowerCamelCase__   :	DPRReaderOutput    ,lowerCamelCase__   :	int = 16    ,lowerCamelCase__   :	int = 64    ,lowerCamelCase__   :	int = 4    ,):
									UpperCAmelCase__             =							reader_input['input_ids']
									UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							reader_output[:3]
									UpperCAmelCase__             =							len(lowerCamelCase__			)
									UpperCAmelCase__             =							sorted(range(lowerCamelCase__			)    ,reverse=lowerCamelCase__    ,key=relevance_logits.__getitem__			)
									UpperCAmelCase__             =							[]
									for doc_id in sorted_docs:
													UpperCAmelCase__             =							list(input_ids[doc_id]			)
													# assuming question & title information is at the beginning of the sequence
													UpperCAmelCase__             =							sequence_ids.index(self.sep_token_id    ,2			) + 1  # second sep id
													if sequence_ids[-1] == self.pad_token_id:
																	UpperCAmelCase__             =							sequence_ids.index(self.pad_token_id			)
													else:
																	UpperCAmelCase__             =							len(lowerCamelCase__			)

													UpperCAmelCase__             =							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   :	Union[str, Any]    ,lowerCamelCase__   :	List[int]    ,lowerCamelCase__   :	List[int]    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	int    ,):
									UpperCAmelCase__             =							[]
									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)			)
									UpperCAmelCase__             =							sorted(lowerCamelCase__    ,key=lambda lowerCamelCase__			: x[1]    ,reverse=lowerCamelCase__			)
									UpperCAmelCase__             =							[]
									for (start_index, end_index), score in scores:
													assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
													UpperCAmelCase__             =							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(lowerCamelCase__			) == top_spans:
																	break
									return chosen_span_intervals






@add_end_docstrings(__UpperCAmelCase				)
class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = VOCAB_FILES_NAMES
					snake_case__				    = READER_PRETRAINED_VOCAB_FILES_MAP
					snake_case__				    = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__				    = READER_PRETRAINED_INIT_CONFIGURATION
					snake_case__				    = ["input_ids", "attention_mask"]
					snake_case__				    = DPRReaderTokenizer




"""simple docstring"""






import functools



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							len(lowerCamelCase       )
				UpperCAmelCase__             =							len(lowerCamelCase       )

				@functools.cache
				def min_distance(lowerCamelCase					,			lowerCamelCase       ) -> int:
								# if first word index is overflow - delete all from the second word
								if indexa >= len_worda:
												return len_worda - indexa
								# if second word index is overflow - delete all from the first word
								if indexa >= len_worda:
												return len_worda - indexa
								UpperCAmelCase__             =							int(worda[indexa] != worda[indexa]       )  # current letters not identical
								return min(
								    1 + min_distance(indexa + 1					,			lowerCamelCase       )					,			1 + min_distance(lowerCamelCase					,			indexa + 1       )					,			diff + min_distance(indexa + 1					,			indexa + 1       )					,			)

				return min_distance(0					,			0       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



