






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


A_						:						Union[str, Any]						      =					logging.get_logger(__name__)

A_						:						List[str]						      =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_						:						List[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'
        ),
    },
}
A_						:						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'
        ),
    },
}
A_						:						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'
        ),
    },
}

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


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




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Tuple										=VOCAB_FILES_NAMES
       a							:						Dict										=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a							:						Dict										=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						Dict										=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a							:						Optional[Any]										=DPRContextEncoderTokenizer







class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Dict										=VOCAB_FILES_NAMES
       a							:						Optional[Any]										=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a							:						int										=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						int										=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a							:						Dict										=DPRQuestionEncoderTokenizer







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

A_						:						Optional[int]						      =					collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A_						:						List[str]						      =					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 							_lowerCAmelCase:





       """simple docstring"""







       def __call__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         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_:   Optional[int]        =							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_:   Any        =							titles if not isinstance(_lowerCamelCase    ,     _lowerCamelCase				) else [titles]
         UpperCamelCase_:   Optional[Any]        =							texts if not isinstance(_lowerCamelCase    ,     _lowerCamelCase				) else [texts]
         UpperCamelCase_:   Dict        =							len(_lowerCamelCase				)
         UpperCamelCase_:   Tuple        =							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_:   str        =							super().__call__(_lowerCamelCase    ,     _lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase				)['input_ids']
         UpperCamelCase_:   str        =							super().__call__(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase				)['input_ids']
         UpperCamelCase_:   Tuple        =							{
             '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_:   List[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_:   Dict        =							attention_mask
         return self.pad(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = 1_6    ,     _lowerCamelCase = 6_4    ,     _lowerCamelCase = 4    ,     ):
         UpperCamelCase_:   Optional[int]        =							reader_input['input_ids']
         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							reader_output[:3]
         UpperCamelCase_:   Any        =							len(_lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							sorted(range(_lowerCamelCase				)    ,     reverse=_lowerCamelCase    ,     key=relevance_logits.__getitem__				)
         UpperCamelCase_:   List[DPRReaderOutput]        =							[]
         for doc_id in sorted_docs:
           UpperCamelCase_:   int        =							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_:   str        =							sequence_ids.index(self.pad_token_id				)
           else:
             UpperCamelCase_:   Tuple        =							len(_lowerCamelCase				)

           UpperCamelCase_:   Any        =							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    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     ):
         UpperCamelCase_:   List[str]        =							[]
         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_:   List[Any]        =							sorted(_lowerCamelCase    ,     key=lambda _lowerCamelCase				: x[1]    ,     reverse=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							[]
         for (start_index, end_index), score in scores:
           assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
           UpperCamelCase_:   str        =							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 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Optional[int]										=VOCAB_FILES_NAMES
       a							:						int										=READER_PRETRAINED_VOCAB_FILES_MAP
       a							:						Any										=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						Optional[Any]										=READER_PRETRAINED_INIT_CONFIGURATION
       a							:						List[Any]										=['''input_ids''', '''attention_mask''']
       a							:						Any										=DPRReaderTokenizer






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   ["""input_features"""]


      def __init__(		self   ,				a__=80   ,				a__=1_6000   ,				a__=160   ,				a__=30   ,				a__=400   ,				a__=0.0   ,				a__=False   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(
                 feature_size=a__   ,				sampling_rate=a__   ,				padding_value=a__   ,				return_attention_mask=a__   ,				**a__   ,				)
             _lowerCamelCase				:  int						=		n_fft
             _lowerCamelCase				:  List[Any]						=		hop_length
             _lowerCamelCase				:  Tuple						=		chunk_length
             _lowerCamelCase				:  Optional[Any]						=		chunk_length * sampling_rate
             _lowerCamelCase				:  Optional[Any]						=		self.n_samples // hop_length
             _lowerCamelCase				:  List[str]						=		sampling_rate
             _lowerCamelCase				:  Optional[int]						=		mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2   ,				num_mel_filters=a__   ,				min_frequency=0.0   ,				max_frequency=8000.0   ,				sampling_rate=a__   ,				norm='''slaney'''   ,				mel_scale='''slaney'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		spectrogram(
                 a__   ,				window_function(self.n_fft   ,				'''hann''')   ,				frame_length=self.n_fft   ,				hop_length=self.hop_length   ,				power=2.0   ,				mel_filters=self.mel_filters   ,				log_mel='''log10'''   ,				)
             _lowerCamelCase				:  str						=		log_spec[:, :-1]
             _lowerCamelCase				:  Union[str, Any]						=		np.maximum(a__   ,				log_spec.max() - 8.0)
             _lowerCamelCase				:  Optional[int]						=		(log_spec + 4.0) / 4.0
             return log_spec


      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def 					__snake_case      (		a__   ,				a__   ,				a__ = 0.0):



             """simple docstring"""





             if attention_mask is not None:
                    _lowerCamelCase				:  Dict						=		np.array(a__   ,				np.intaa)
                    _lowerCamelCase				:  Any						=		[]

                    for vector, length in zip(a__   ,				attention_mask.sum(-1)):
                           _lowerCamelCase				:  Any						=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                           if length < normed_slice.shape[0]:
                                  _lowerCamelCase				:  Optional[int]						=		padding_value

                           normed_input_values.append(a__)
             else:
                    _lowerCamelCase				:  Optional[int]						=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

             return normed_input_values


      def __call__(		self   ,				a__   ,				a__ = True   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				a__ = "max_length"   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             if sampling_rate is not None:
                    if sampling_rate != self.sampling_rate:
                           raise ValueError(
                               F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                               F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                               F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""")
             else:
                    logger.warning(
                        '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                        '''Failing to do so can result in silent errors that might be hard to debug.''')

             _lowerCamelCase				:  Dict						=		isinstance(a__   ,				np.ndarray) and len(raw_speech.shape) > 1
             if is_batched_numpy and len(raw_speech.shape) > 2:
                    raise ValueError(F"""Only mono-channel audio is supported for input to {self}""")
             _lowerCamelCase				:  int						=		is_batched_numpy or (
                 isinstance(a__   ,				(list, tuple)) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)))
             )

             if is_batched:
                    _lowerCamelCase				:  List[Any]						=		[np.asarray([speech]   ,				dtype=np.floataa).T for speech in raw_speech]
             elif not is_batched and not isinstance(a__   ,				np.ndarray):
                    _lowerCamelCase				:  str						=		np.asarray(a__   ,				dtype=np.floataa)
             elif isinstance(a__   ,				np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
                    _lowerCamelCase				:  str						=		raw_speech.astype(np.floataa)

             # always return batch
             if not is_batched:
                    _lowerCamelCase				:  List[Any]						=		[np.asarray([raw_speech]).T]

             _lowerCamelCase				:  List[str]						=		BatchFeature({'''input_features''': raw_speech})

             # convert into correct format for padding

             _lowerCamelCase				:  Optional[Any]						=		self.pad(
                 a__   ,				padding=a__   ,				max_length=max_length if max_length else self.n_samples   ,				truncation=a__   ,				pad_to_multiple_of=a__   ,				return_attention_mask=return_attention_mask or do_normalize   ,				)

             # zero-mean and unit-variance normalization
             if do_normalize:
                    _lowerCamelCase				:  Dict						=		self.zero_mean_unit_var_norm(
                        padded_inputs['''input_features''']   ,				attention_mask=padded_inputs['''attention_mask''']   ,				padding_value=self.padding_value   ,				)
                    _lowerCamelCase				:  int						=		np.stack(padded_inputs['''input_features''']   ,				axis=0)

             # make sure list is in array format
             _lowerCamelCase				:  Optional[Any]						=		padded_inputs.get('''input_features''').transpose(2   ,				0   ,				1)

             _lowerCamelCase				:  Tuple						=		[self._np_extract_fbank_features(a__) for waveform in input_features[0]]

             if isinstance(input_features[0]   ,				a__):
                    _lowerCamelCase				:  str						=		[np.asarray(a__   ,				dtype=np.floataa) for feature in input_features]
             else:
                    _lowerCamelCase				:  Dict						=		input_features

             if return_attention_mask:
                    # rescale from sample (48000) to feature (3000)
                    _lowerCamelCase				:  Optional[int]						=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

             if return_tensors is not None:
                    _lowerCamelCase				:  str						=		padded_inputs.convert_to_tensors(a__)

             return padded_inputs



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		copy.deepcopy(self.__dict__)
             _lowerCamelCase				:  int						=		self.__class__.__name__
             if "mel_filters" in output:
                    del output["mel_filters"]
             return output

