




"""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							:			List[Any]     							=							logging.get_logger(__name__)

lowerCAmelCase							:			Union[str, Any]     							=							{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase							:			Dict     							=							{
    """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							:			List[Any]     							=							{
    """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							:			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"""
        ),
    },
}

lowerCAmelCase							:			Optional[int]     							=							{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
lowerCAmelCase							:			Dict     							=							{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
lowerCAmelCase							:			Tuple     							=							{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						VOCAB_FILES_NAMES
				__UpperCamelCase			  =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase			  =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase			  =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				__UpperCamelCase			  =						DPRContextEncoderTokenizer






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						VOCAB_FILES_NAMES
				__UpperCamelCase			  =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase			  =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase			  =						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				__UpperCamelCase			  =						DPRQuestionEncoderTokenizer




lowerCAmelCase							:			Optional[int]     							=							collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

lowerCAmelCase							:			Optional[Any]     							=							collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


lowerCAmelCase							:			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     __magic_name__							:






				'''simple docstring'''





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





							"""simple docstring"""
							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:
										lowerCamelCase			      =					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		, )
							lowerCamelCase			      =					titles if not isinstance(_a		, _a ) else [titles]
							lowerCamelCase			      =					texts if not isinstance(_a		, _a ) else [texts]
							lowerCamelCase			      =					len(_a )
							lowerCamelCase			      =					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.'
							lowerCamelCase			      =					super().__call__(_a		, _a		, padding=_a		, truncation=_a )["""input_ids"""]
							lowerCamelCase			      =					super().__call__(_a		, add_special_tokens=_a		, padding=_a		, truncation=_a )["""input_ids"""]
							lowerCamelCase			      =					{
							    """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:
										lowerCamelCase			      =					[]
										for input_ids in encoded_inputs["input_ids"]:
													attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
										lowerCamelCase			      =					attention_mask
							return self.pad(_a		, padding=_a		, max_length=_a		, return_tensors=_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = 16		, _a = 64		, _a = 4		, ):





							"""simple docstring"""
							lowerCamelCase			      =					reader_input["""input_ids"""]
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					reader_output[:3]
							lowerCamelCase			      =					len(_a )
							lowerCamelCase			      =					sorted(range(_a )		, reverse=_a		, key=relevance_logits.__getitem__ )
							lowerCamelCase			      =					[]
							for doc_id in sorted_docs:
										lowerCamelCase			      =					list(input_ids[doc_id] )
										# assuming question & title information is at the beginning of the sequence
										lowerCamelCase			      =					sequence_ids.index(self.sep_token_id		, 2 ) + 1  # second sep id
										if sequence_ids[-1] == self.pad_token_id:
													lowerCamelCase			      =					sequence_ids.index(self.pad_token_id )
										else:
													lowerCamelCase			      =					len(_a )

										lowerCamelCase			      =					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    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					[]
							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) )
							lowerCamelCase			      =					sorted(_a		, key=lambda _a : x[1]		, reverse=_a )
							lowerCamelCase			      =					[]
							for (start_index, end_index), score in scores:
										assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
										lowerCamelCase			      =					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(UpperCAmelCase__				)
class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						VOCAB_FILES_NAMES
				__UpperCamelCase			  =						READER_PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase			  =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase			  =						READER_PRETRAINED_INIT_CONFIGURATION
				__UpperCamelCase			  =						["input_ids", "attention_mask"]
				__UpperCamelCase			  =						DPRReaderTokenizer









"""simple docstring"""




from math import ceil
from typing import List, Optional, Union

import numpy as np

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


lowerCAmelCase							:			List[str]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["audio_values", "audio_mask"]





				def __init__(       self		, _a=2_048		, _a=1		, _a=[16, 16]		, _a=128		, _a=44_100		, _a=86		, _a=2_048		, _a=0.0		, **_a		, ):





							"""simple docstring"""
							super().__init__(
							    feature_size=_a		, sampling_rate=_a		, padding_value=_a		, **_a		, )

							lowerCamelCase			      =					spectrogram_length
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					feature_size // self.patch_size[1]
							lowerCamelCase			      =					n_fft
							lowerCamelCase			      =					sampling_rate // hop_length_to_sampling_rate
							lowerCamelCase			      =					sampling_rate
							lowerCamelCase			      =					padding_value
							lowerCamelCase			      =					mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2		, num_mel_filters=_a		, min_frequency=0.0		, max_frequency=22_050.0		, sampling_rate=_a		, norm="""slaney"""		, mel_scale="""slaney"""		, ).T





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					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.T		, log_mel="""dB"""		, db_range=80.0		, )
							lowerCamelCase			      =					log_spec[:, :-1]
							lowerCamelCase			      =					log_spec - 20.0
							lowerCamelCase			      =					np.clip(log_spec / 40.0		, -2.0		, 0.0 ) + 1.0
							return log_spec





				def __call__(       self		, _a		, _a = None		, _a = True		, _a = None		, _a = False		, _a = False		, **_a		, ):





							"""simple docstring"""

							if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
													raise ValueError(
													    """This feature extractor is set to support sampling rate"""
													    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
													    f' 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			      =					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			      =					is_batched_numpy or (
							    isinstance(_a		, (list, tuple) ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list) ))
							)
							if is_batched:
										lowerCamelCase			      =					[np.asarray([speech]		, dtype=np.floataa ).T for speech in raw_speech]
							elif not is_batched and not isinstance(_a		, np.ndarray ):
										lowerCamelCase			      =					np.asarray(_a		, dtype=np.floataa )
							elif isinstance(_a		, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
										lowerCamelCase			      =					raw_speech.astype(np.floataa )
							# always return batch
							if not is_batched:
										lowerCamelCase			      =					[np.asarray([raw_speech] ).T]

							# Convert audio signals to log mel spectrograms, truncate by time axis
							lowerCamelCase			      =					[
							    self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
							]
							if isinstance(audio_features[0]		, _a ):
										lowerCamelCase			      =					[np.asarray(_a		, dtype=np.floataa ) for feature in audio_features]

							# Create audio attention mask
							lowerCamelCase			      =					max(
							    [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
							if return_attention_mask:
										lowerCamelCase			      =					[
										    (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
										    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
										    for feature in audio_features
										]
										lowerCamelCase			      =					np.array(_a ).astype(np.floataa )

							# convert into correct format for padding
							lowerCamelCase			      =					max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
							lowerCamelCase			      =					np.ones([len(_a ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
							lowerCamelCase			      =					padded_audio_features * self.padding_value
							for i in range(len(_a ) ):
										lowerCamelCase			      =					audio_features[i]
										lowerCamelCase			      =					feature

							# return as BatchFeature
							if return_attention_mask:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features}

							lowerCamelCase			      =					BatchFeature(data=_a		, tensor_type=_a )
							return encoded_inputs



