




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


lowerCamelCase_    		=  logging.get_logger(__name__)

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

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


lowerCamelCase_    		=  {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase_    		=  {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase_    		=  {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class  __a   (					__lowerCamelCase	):


     """simple docstring"""


     _A							:       Optional[int]							   =    VOCAB_FILES_NAMES
     _A							:       Any							   =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     _A							:       List[Any]							   =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A							:       int							   =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class  __a   (					__lowerCamelCase	):


     """simple docstring"""


     _A							:       Tuple							   =    VOCAB_FILES_NAMES
     _A							:       List[str]							   =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     _A							:       Tuple							   =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A							:       List[Any]							   =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

lowerCamelCase_    		=  collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


lowerCamelCase_    		=  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  __a   :


     """simple docstring"""


     def __call__(      self     :					Optional[int]							,_UpperCamelCase     :					int							,_UpperCamelCase     :					Optional[str] = None							,_UpperCamelCase     :					Optional[str] = None							,_UpperCamelCase     :					Union[bool, str] = False							,_UpperCamelCase     :					Union[bool, str] = False							,_UpperCamelCase     :					Optional[int] = None							,_UpperCamelCase     :					Optional[Union[str, TensorType]] = None							,_UpperCamelCase     :					Optional[bool] = None							,**_UpperCamelCase     :					List[Any]							,)						->  BatchEncoding:


         '''simple docstring'''





         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:
             SCREAMING_SNAKE_CASE__        =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							,)
         SCREAMING_SNAKE_CASE__        =titles if not isinstance(__lowerCAmelCase							,__lowerCAmelCase		) else [titles]
         SCREAMING_SNAKE_CASE__        =texts if not isinstance(__lowerCAmelCase							,__lowerCAmelCase		) else [texts]
         SCREAMING_SNAKE_CASE__        =len(__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =questions if not isinstance(__lowerCAmelCase							,__lowerCAmelCase		) else [questions] * n_passages
         if len(__lowerCAmelCase		) != len(__lowerCAmelCase		):
             raise ValueError(
                 f"""There should be as many titles than texts but got {len(__lowerCAmelCase		)} titles and {len(__lowerCAmelCase		)} texts."""		)
         SCREAMING_SNAKE_CASE__        =super().__call__(__lowerCAmelCase							,__lowerCAmelCase							,padding=__lowerCAmelCase							,truncation=__lowerCAmelCase		)["""input_ids"""]
         SCREAMING_SNAKE_CASE__        =super().__call__(__lowerCAmelCase							,add_special_tokens=__lowerCAmelCase							,padding=__lowerCAmelCase							,truncation=__lowerCAmelCase		)["""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(__lowerCAmelCase							,__lowerCAmelCase		)
             ]
         }
         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(__lowerCAmelCase							,padding=__lowerCAmelCase							,max_length=__lowerCAmelCase							,return_tensors=__lowerCAmelCase		)




     def     __A							(      self     :					List[str]							,_UpperCamelCase     :					BatchEncoding							,_UpperCamelCase     :					DPRReaderOutput							,_UpperCamelCase     :					int = 1_6							,_UpperCamelCase     :					int = 6_4							,_UpperCamelCase     :					int = 4							,)						->  List[DPRSpanPrediction]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =reader_input["""input_ids"""]
         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =reader_output[:3]
         SCREAMING_SNAKE_CASE__        =len(__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =sorted(range(__lowerCAmelCase		)							,reverse=__lowerCAmelCase							,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(__lowerCAmelCase		)

             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=__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     __A							(      self     :					str							,_UpperCamelCase     :					List[int]							,_UpperCamelCase     :					List[int]							,_UpperCamelCase     :					int							,_UpperCamelCase     :					int							,)						->  List[DPRSpanPrediction]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =[]
         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)		)
         SCREAMING_SNAKE_CASE__        =sorted(__lowerCAmelCase							,key=lambda _UpperCamelCase		: x[1]							,reverse=__lowerCAmelCase		)
         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(__lowerCAmelCase		) == top_spans:
                 break
         return chosen_span_intervals






@add_end_docstrings(__lowerCamelCase	)
class  __a   (					__lowerCamelCase				,	__lowerCamelCase	):


     """simple docstring"""


     _A							:       str							   =    VOCAB_FILES_NAMES
     _A							:       Optional[Any]							   =    READER_PRETRAINED_VOCAB_FILES_MAP
     _A							:       Tuple							   =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A							:       Tuple							   =    READER_PRETRAINED_INIT_CONFIGURATION
     _A							:       str							   =    ['''input_ids''', '''attention_mask''']













import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       '''Wav2Vec2FeatureExtractor'''
			__lowerCamelCase      :			str							     =       '''AutoTokenizer'''






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Tuple:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase				,      __lowerCAmelCase  )
					A__     =  self.feature_extractor
					A__     =  False






			@classmethod
			def   a_			(     cls  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					try:
							return super().from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
					except OSError:
							warnings.warn(
							    f'Loading a tokenizer inside {cls.__name__} from a config that does not'
							    """ include a `tokenizer_class` attribute is deprecated and will be """
							    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
							    """ attribute to either your `config.json` or `tokenizer_config.json` """
							    """file to suppress this warning: """				,      __lowerCAmelCase				,      )

							A__     =  WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
							A__     =  WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )

							return cls(feature_extractor=__lowerCAmelCase				,      tokenizer=__lowerCAmelCase  )






			def __call__(     self  :       Any				,      *__lowerCAmelCase  :       Union[str, Any]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*__lowerCAmelCase				,      **__lowerCAmelCase  )

					if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
							A__     =  kwargs.pop("""raw_speech"""  )
					else:
							A__     =  kwargs.pop("""audio"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""sampling_rate"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""text"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 0:
							A__     =  args[0]
							A__     =  args[1:]

					if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

					if audio is not None:
							A__     =  self.feature_extractor(__lowerCAmelCase				,      *__lowerCAmelCase				,      sampling_rate=__lowerCAmelCase				,      **__lowerCAmelCase  )
					if text is not None:
							A__     =  self.tokenizer(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if text is None:
							return inputs
					elif audio is None:
							return encodings
					else:
							A__     =  encodings["""input_ids"""]
							return inputs






			def   a_			(     self  :       List[str]				,      *__lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Tuple  )      ->      List[str]:







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor.pad(*__lowerCAmelCase				,      **__lowerCAmelCase  )

					A__     =  kwargs.pop("""input_features"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""labels"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 0:
							A__     =  args[0]
							A__     =  args[1:]

					if input_features is not None:
							A__     =  self.feature_extractor.pad(__lowerCAmelCase				,      *__lowerCAmelCase				,      **__lowerCAmelCase  )
					if labels is not None:
							A__     =  self.tokenizer.pad(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if labels is None:
							return input_features
					elif input_features is None:
							return labels
					else:
							A__     =  labels["""input_ids"""]
							return input_features






			def   a_			(     self  :       Optional[int]				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Dict:







					"""simple docstring"""






					return self.tokenizer.batch_decode(*__lowerCAmelCase				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Any				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






					return self.tokenizer.decode(*__lowerCAmelCase				,      **__lowerCAmelCase  )







			@contextmanager
			def   a_			(     self  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					warnings.warn(
					    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
					    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
					    """your audio inputs, or in a separate call."""  )
					A__     =  True
					A__     =  self.tokenizer
					yield
					A__     =  self.feature_extractor
					A__     =  False



