






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


__UpperCAmelCase     							=						logging.get_logger(__name__)

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

__UpperCAmelCase     							=						{
    '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'
        ),
    },
}
__UpperCAmelCase     							=						{
    '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'
        ),
    },
}
__UpperCAmelCase     							=						{
    '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'
        ),
    },
}

__UpperCAmelCase     							=						{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__UpperCAmelCase     							=						{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__UpperCAmelCase     							=						{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__UpperCAmelCase     							=						{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__UpperCAmelCase     							=						{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__UpperCAmelCase     							=						{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Union[str, Any]							=  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Optional[Any]							=  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Union[str, Any]							=  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Dict							=  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Optional[Any]							=  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Optional[int]							=  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

__UpperCAmelCase     							=						collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__UpperCAmelCase     							=						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(_snake_case      )
class   lowerCamelCase				:







		'''simple docstring'''



		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:
												UpperCAmelCase_   :				Optional[int]						=						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    ,		)
							UpperCAmelCase_   :				Any						=						titles if not isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else [titles]
							UpperCAmelCase_   :				str						=						texts if not isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else [texts]
							UpperCAmelCase_   :				Any						=						len(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						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."       )
							UpperCAmelCase_   :				Optional[int]						=						super().__call__(_UpperCamelCase    ,		_UpperCamelCase    ,		padding=_UpperCamelCase    ,		truncation=_UpperCamelCase       )['input_ids']
							UpperCAmelCase_   :				Tuple						=						super().__call__(_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase    ,		padding=_UpperCamelCase    ,		truncation=_UpperCamelCase       )['input_ids']
							UpperCAmelCase_   :				int						=						{
							    '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:
												UpperCAmelCase_   :				Any						=						[]
												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_   :				Optional[int]						=						attention_mask
							return self.pad(_UpperCamelCase    ,		padding=_UpperCamelCase    ,		max_length=_UpperCamelCase    ,		return_tensors=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = 1_6    ,		_UpperCamelCase = 6_4    ,		_UpperCamelCase = 4    ,		)     ->							List[DPRSpanPrediction]:
							UpperCAmelCase_   :				Optional[int]						=						reader_input['input_ids']
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						reader_output[:3]
							UpperCAmelCase_   :				List[Any]						=						len(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						sorted(range(_UpperCamelCase       )    ,		reverse=_UpperCamelCase    ,		key=relevance_logits.__getitem__       )
							UpperCAmelCase_   :				List[DPRReaderOutput]						=						[]
							for doc_id in sorted_docs:
												UpperCAmelCase_   :				Dict						=						list(input_ids[doc_id]       )
												# assuming question & title information is at the beginning of the sequence
												UpperCAmelCase_   :				Tuple						=						sequence_ids.index(self.sep_token_id    ,		2       ) + 1  # second sep id
												if sequence_ids[-1] == self.pad_token_id:
																	UpperCAmelCase_   :				Union[str, Any]						=						sequence_ids.index(self.pad_token_id       )
												else:
																	UpperCAmelCase_   :				Union[str, Any]						=						len(_UpperCamelCase       )

												UpperCAmelCase_   :				Tuple						=						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  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							List[DPRSpanPrediction]:
							UpperCAmelCase_   :				Optional[int]						=						[]
							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)       )
							UpperCAmelCase_   :				Any						=						sorted(_UpperCamelCase    ,		key=lambda _UpperCamelCase       : x[1]    ,		reverse=_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						[]
							for (start_index, end_index), score in scores:
												if start_index > end_index:
																	raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"       )
												UpperCAmelCase_   :				int						=						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(_snake_case      )
class   lowerCamelCase				(_snake_case			,  _snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   int							=  READER_PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   List[Any]							=  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Union[str, Any]							=  READER_PRETRAINED_INIT_CONFIGURATION
		_snake_case   :   Optional[Any]							=  ['''input_ids''', '''attention_mask''']














def 					lowercase__				(					__snake_case   :							List[str]					,	__snake_case   :							List[str]					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							str					,	__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


					if index == r:
										for j in range(__snake_case  ):
															print(data[j]					,	end=' '  )
										print(' '  )
										return
					#  When no more elements are there to put in data[]
					if i >= n:
										return
					# current is included, put next at next location
					UpperCAmelCase_   :				int						=						arr[i]
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	index + 1					,	__snake_case					,	i + 1  )
					# current is excluded, replace it with
					# next (Note that i+1 is passed, but
					# index is not changed)
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	i + 1  )
					# The main function that prints all combinations
					# of size r in arr[] of size n. This function
					# mainly uses combinationUtil()




def 					lowercase__				(					__snake_case   :							List[Any]					,	__snake_case   :							List[Any]					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						[0] * r
					# Print all combination using temporary array 'data[]'
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	0					,	__snake_case					,	0  )


if __name__ == "__main__":
		# Driver code to check the function above
		__UpperCAmelCase     							=						[10, 20, 30, 40, 50]
		print_combination(arr, len(arr), 3)
		# This code is contributed by Ambuj sahu






