


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


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

snake_case							:       Dict     			=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:       Optional[Any]     			=      {
    '''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'''
        ),
    },
}
snake_case							:       str     			=      {
    '''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'''
        ),
    },
}
snake_case							:       Optional[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'''
        ),
    },
}

snake_case							:       Dict     			=      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
snake_case							:       Optional[int]     			=      {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
snake_case							:       List[str]     			=      {
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


snake_case							:       int     			=      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case							:       List[str]     			=      {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case							:       List[Any]     			=      {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Any          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    Union[str, Any]          =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    Optional[Any]          =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    Optional[int]          =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Union[str, Any]          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    List[Any]          =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    Optional[Any]          =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    str          =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

snake_case							:       List[str]     			=      collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


snake_case							:       Optional[Any]     			=      r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''







@add_start_docstrings(lowerCamelCase_      )
class        snake_case_   :
					def __call__(      self   :List[Any]			,__snake_case   :Optional[int]			,__snake_case   :Optional[str] = None			,__snake_case   :Optional[str] = None			,__snake_case   :Union[bool, str] = False			,__snake_case   :Union[bool, str] = False			,__snake_case   :Optional[int] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Optional[bool] = None			,**__snake_case   :Optional[int]			,)					->    BatchEncoding:
												if titles is None and texts is None:
																			return super().__call__(
																			    __snake_case			,padding=__snake_case			,truncation=__snake_case			,max_length=__snake_case			,return_tensors=__snake_case			,return_attention_mask=__snake_case			,**__snake_case			,)
												elif titles is None or texts is None:
																			a__             =     titles if texts is None else texts
																			return super().__call__(
																			    __snake_case			,__snake_case			,padding=__snake_case			,truncation=__snake_case			,max_length=__snake_case			,return_tensors=__snake_case			,return_attention_mask=__snake_case			,**__snake_case			,)
												a__             =     titles if not isinstance(__snake_case			,__snake_case      ) else [titles]
												a__             =     texts if not isinstance(__snake_case			,__snake_case      ) else [texts]
												a__             =     len(__snake_case      )
												a__             =     questions if not isinstance(__snake_case			,__snake_case      ) else [questions] * n_passages
												if len(__snake_case      ) != len(__snake_case      ):
																			raise ValueError(
																			    F'There should be as many titles than texts but got {len(__snake_case      )} titles and {len(__snake_case      )} texts.'      )
												a__             =     super().__call__(__snake_case			,__snake_case			,padding=__snake_case			,truncation=__snake_case      )['input_ids']
												a__             =     super().__call__(__snake_case			,add_special_tokens=__snake_case			,padding=__snake_case			,truncation=__snake_case      )['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(__snake_case			,__snake_case      )
												    ]
												}
												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(__snake_case			,padding=__snake_case			,max_length=__snake_case			,return_tensors=__snake_case      )
					def  lowerCamelCase__(      self   :str			,__snake_case   :BatchEncoding			,__snake_case   :DPRReaderOutput			,__snake_case   :int = 16			,__snake_case   :int = 64			,__snake_case   :int = 4			,)					->    List[DPRSpanPrediction]:
												a__             =     reader_input['input_ids']
												a__      ,					a__      ,					a__             =     reader_output[:3]
												a__             =     len(__snake_case      )
												a__             =     sorted(range(__snake_case      )			,reverse=__snake_case			,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(__snake_case      )

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



					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :List[int]			,__snake_case   :List[int]			,__snake_case   :int			,__snake_case   :int			,)					->    List[DPRSpanPrediction]:
												a__             =     []
												for start_index, start_score in enumerate(__snake_case      ):
																			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(__snake_case			,key=lambda __snake_case      : x[1]			,reverse=__snake_case      )
												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(__snake_case      ) == top_spans:
																										break
												return chosen_span_intervals







@add_end_docstrings(lowerCamelCase_      )
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_      ):
					UpperCAmelCase__					:    Tuple          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    Tuple          =			READER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    Optional[Any]          =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    Any          =			READER_PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__					:    str          =			['''input_ids''', '''attention_mask''']








def 							__lowercase   ( __lowerCAmelCase							:				list[int]   ):
							a__             =     []
							if len(__lowerCAmelCase   ) == 1:
														return [nums.copy()]
							for _ in range(len(__lowerCAmelCase   )   ):
														a__             =     nums.pop(0   )
														a__             =     permute(__lowerCAmelCase   )
														for perm in permutations:
																					perm.append(__lowerCAmelCase   )
														result.extend(__lowerCAmelCase   )
														nums.append(__lowerCAmelCase   )
							return result






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):

							def backtrack(__lowerCAmelCase							:				Any   ):
														if start == len(__lowerCAmelCase   ) - 1:
																					output.append(nums[:]   )
														else:
																					for i in range(__lowerCAmelCase					,    len(__lowerCAmelCase   )   ):
																												a__      ,					a__             =     nums[i], nums[start]
																												backtrack(start + 1   )
																												a__      ,					a__             =     nums[i], nums[start]  # backtrack

							a__             =     []
							backtrack(0   )
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						snake_case							:       Optional[Any]     			=      permutea([1, 2, 3])
						print(res)
						doctest.testmod()




