'''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


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

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

snake_case__					:      Tuple				      =  {
    '''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__					:      int				      =  {
    '''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[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'''
        ),
    },
}

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


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






class 					__SCREAMING_SNAKE_CASE						(	_UpperCamelCase	):







		'''simple docstring'''






		lowerCamelCase_		:Union[str, Any]						   =		VOCAB_FILES_NAMES
		lowerCamelCase_		:Any						   =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase_		:Tuple						   =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase_		:Optional[Any]						   =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase_		:Optional[int]						   =		DPRContextEncoderTokenizer







class 					__SCREAMING_SNAKE_CASE						(	_UpperCamelCase	):







		'''simple docstring'''






		lowerCamelCase_		:Tuple						   =		VOCAB_FILES_NAMES
		lowerCamelCase_		:Optional[Any]						   =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase_		:List[Any]						   =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase_		:Any						   =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase_		:Union[str, Any]						   =		DPRQuestionEncoderTokenizer







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

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


snake_case__					:      Union[str, 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)

    Return:
        `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(_UpperCamelCase	)
class 					__SCREAMING_SNAKE_CASE						:







		'''simple docstring'''






		def __call__(			self				,		snake_case_				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = False				,		snake_case_ = False				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = None				,		**snake_case_				,		):




							'''simple docstring'''

							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_							:       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_							:       int       =				titles if not isinstance(_UpperCAmelCase				,		_UpperCAmelCase						) else [titles]
							UpperCAmelCase_							:       Tuple       =				texts if not isinstance(_UpperCAmelCase				,		_UpperCAmelCase						) else [texts]
							UpperCAmelCase_							:       List[Any]       =				len(_UpperCAmelCase						)
							UpperCAmelCase_							:       List[Any]       =				questions if not isinstance(_UpperCAmelCase				,		_UpperCAmelCase						) else [questions] * n_passages
							assert len(_UpperCAmelCase						) == len(
							    _UpperCAmelCase						), F'''There should be as many titles than texts but got {len(_UpperCAmelCase						)} titles and {len(_UpperCAmelCase						)} texts.'''
							UpperCAmelCase_							:       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_							:       str       =				{
							    '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_							:       Optional[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_							:       int       =				attention_mask
							return self.pad(_UpperCAmelCase				,		padding=_UpperCAmelCase				,		max_length=_UpperCAmelCase				,		return_tensors=_UpperCAmelCase						)


		def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_ = 1_6				,		snake_case_ = 6_4				,		snake_case_ = 4				,		):




							'''simple docstring'''

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

												UpperCAmelCase_							:       Optional[int]       =				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				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




							'''simple docstring'''

							UpperCAmelCase_							:       List[str]       =				[]
							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_							:       str       =				sorted(_UpperCAmelCase				,		key=lambda snake_case_						: x[1]				,		reverse=_UpperCAmelCase						)
							UpperCAmelCase_							:       Any       =				[]
							for (start_index, end_index), score in scores:
												assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
												UpperCAmelCase_							:       Optional[int]       =				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(_UpperCAmelCase						) == top_spans:
																	break
							return chosen_span_intervals







@add_end_docstrings(_UpperCamelCase	)
class 					__SCREAMING_SNAKE_CASE						(	_UpperCamelCase						,     _UpperCamelCase	):







		'''simple docstring'''






		lowerCamelCase_		:Any						   =		VOCAB_FILES_NAMES
		lowerCamelCase_		:str						   =		READER_PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase_		:Optional[Any]						   =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase_		:Dict						   =		READER_PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase_		:Optional[int]						   =		['input_ids', 'attention_mask']
		lowerCamelCase_		:int						   =		DPRReaderTokenizer







'''simple docstring'''


import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)






class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				['a', 'b', 'c']

       # Defaults to last layer if both are None
       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[str]       =				get_aligned_output_features_output_indices(snake_case_				,		snake_case_				,		snake_case_						)
       self.assertEqual(snake_case_				,		['c']						)
       self.assertEqual(snake_case_				,		[2]						)

       # Out indices set to match out features
       UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				get_aligned_output_features_output_indices(['a', 'c']				,		snake_case_				,		snake_case_						)
       self.assertEqual(snake_case_				,		['a', 'c']						)
       self.assertEqual(snake_case_				,		[0, 2]						)

       # Out features set to match out indices
       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				get_aligned_output_features_output_indices(snake_case_				,		[0, 2]				,		snake_case_						)
       self.assertEqual(snake_case_				,		['a', 'c']						)
       self.assertEqual(snake_case_				,		[0, 2]						)

       # Out features selected from negative indices
       UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				get_aligned_output_features_output_indices(snake_case_				,		[-3, -1]				,		snake_case_						)
       self.assertEqual(snake_case_				,		['a', 'c']						)
       self.assertEqual(snake_case_				,		[-3, -1]						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0, 1)				,		snake_case_						)

       # Out features must be a list
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(('a', 'b')				,		(0, 1)				,		['a', 'b']						)

       # Out features must be a subset of stage names
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0, 1)				,		['a']						)

       # Out indices must be a list or tuple
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(snake_case_				,		0				,		['a', 'b']						)

       # Out indices must be a subset of stage names
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(snake_case_				,		(0, 1)				,		['a']						)

       # Out features and out indices must be the same length
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0,)				,		['a', 'b', 'c']						)

       # Out features should match out indices
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0, 2)				,		['a', 'b', 'c']						)

       # Out features and out indices should be in order
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['b', 'a']				,		(0, 1)				,		['a', 'b']						)

       # Check passes with valid inputs
       verify_out_features_out_indices(['a', 'b', 'd']				,		(0, 1, -1)				,		['a', 'b', 'c', 'd']						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				BackboneMixin()

       UpperCAmelCase_							:       Any       =				['a', 'b', 'c']
       UpperCAmelCase_							:       str       =				['a', 'c']
       UpperCAmelCase_							:       str       =				[0, 2]

       # Check that the output features and indices are set correctly
       self.assertEqual(backbone.out_features				,		['a', 'c']						)
       self.assertEqual(backbone.out_indices				,		[0, 2]						)

       # Check out features and indices are updated correctly
       UpperCAmelCase_							:       str       =				['a', 'b']
       self.assertEqual(backbone.out_features				,		['a', 'b']						)
       self.assertEqual(backbone.out_indices				,		[0, 1]						)

       UpperCAmelCase_							:       Optional[int]       =				[-3, -1]
       self.assertEqual(backbone.out_features				,		['a', 'c']						)
       self.assertEqual(backbone.out_indices				,		[-3, -1]						)






