





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase_			=							{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_			=							['''SpeechT5Tokenizer''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_			=							[
        '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SpeechT5ForSpeechToText''',
        '''SpeechT5ForSpeechToSpeech''',
        '''SpeechT5ForTextToSpeech''',
        '''SpeechT5Model''',
        '''SpeechT5PreTrainedModel''',
        '''SpeechT5HifiGan''',
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase_			=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













"""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


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 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__						 =  DPRContextEncoderTokenizer



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__						 =  DPRQuestionEncoderTokenizer


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)

    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(snake_case							)
class 	lowerCAmelCase					:






       def __call__(   self    ,  a__    ,  a__ = None    ,  a__ = None    ,  a__ = False    ,  a__ = False    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  **a__    ,  ):
              if titles is None and texts is None:
                     return super().__call__(
                         a__    ,  padding=a__    ,  truncation=a__    ,  max_length=a__    ,  return_tensors=a__    ,  return_attention_mask=a__    ,  **a__    ,  )
              elif titles is None or texts is None:
                     _UpperCAmelCase     =     titles if texts is None else texts
                     return super().__call__(
                         a__    ,  a__    ,  padding=a__    ,  truncation=a__    ,  max_length=a__    ,  return_tensors=a__    ,  return_attention_mask=a__    ,  **a__    ,  )
              _UpperCAmelCase     =     titles if not isinstance(a__    ,  a__     ) else [titles]
              _UpperCAmelCase     =     texts if not isinstance(a__    ,  a__     ) else [texts]
              _UpperCAmelCase     =     len(a__     )
              _UpperCAmelCase     =     questions if not isinstance(a__    ,  a__     ) else [questions] * n_passages
              assert len(a__     ) == len(
                  a__     ), f"""There should be as many titles than texts but got {len(a__     )} titles and {len(a__     )} texts."""
              _UpperCAmelCase     =     super().__call__(a__    ,  a__    ,  padding=a__    ,  truncation=a__     )['input_ids']
              _UpperCAmelCase     =     super().__call__(a__    ,  add_special_tokens=a__    ,  padding=a__    ,  truncation=a__     )['input_ids']
              _UpperCAmelCase     =     {
                  '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(a__    ,  a__     )
                  ]
              }
              if return_attention_mask is not False:
                     _UpperCAmelCase     =     []
                     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     =     attention_mask
              return self.pad(a__    ,  padding=a__    ,  max_length=a__    ,  return_tensors=a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = 16    ,  a__ = 64    ,  a__ = 4    ,  ):
              _UpperCAmelCase     =     reader_input['input_ids']
              _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     reader_output[:3]
              _UpperCAmelCase     =     len(a__     )
              _UpperCAmelCase     =     sorted(range(a__     )    ,  reverse=a__    ,  key=relevance_logits.__getitem__     )
              _UpperCAmelCase     =     []
              for doc_id in sorted_docs:
                     _UpperCAmelCase     =     list(input_ids[doc_id]     )
                     # assuming question & title information is at the beginning of the sequence
                     _UpperCAmelCase     =     sequence_ids.index(self.sep_token_id    ,  2     ) + 1  # second sep id
                     if sequence_ids[-1] == self.pad_token_id:
                            _UpperCAmelCase     =     sequence_ids.index(self.pad_token_id     )
                     else:
                            _UpperCAmelCase     =     len(a__     )

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






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     []
              for start_index, start_score in enumerate(a__     ):
                     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     =     sorted(a__    ,  key=lambda a__     : x[1]    ,  reverse=a__     )
              _UpperCAmelCase     =     []
              for (start_index, end_index), score in scores:
                     assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
                     _UpperCAmelCase     =     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(a__     ) == top_spans:
                            break
              return chosen_span_intervals



@add_end_docstrings(snake_case							)
class 	lowerCAmelCase					(      snake_case			,	snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  READER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__						 =  ["""input_ids""", """attention_mask"""]
       lowerCAmelCase__						 =  DPRReaderTokenizer






