



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


A__	      =       logging.get_logger(__name__)

A__	      =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A__	      =       {
    """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__	      =       {
    """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__	      =       {
    """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__	      =       {
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
A__	      =       {
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
A__	      =       {
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


A__	      =       {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
A__	      =       {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
A__	      =       {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				VOCAB_FILES_NAMES
				__lowerCamelCase =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				VOCAB_FILES_NAMES
				__lowerCamelCase =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

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


A__	      =       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 					__lowerCAmelCase :




				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__(
														    _snake_case						,				padding=_snake_case						,				truncation=_snake_case						,				max_length=_snake_case						,				return_tensors=_snake_case						,				return_attention_mask=_snake_case						,				**_snake_case						,				)
									elif titles is None or texts is None:
														_lowerCAmelCase						=    titles if texts is None else texts
														return super().__call__(
														    _snake_case						,				_snake_case						,				padding=_snake_case						,				truncation=_snake_case						,				max_length=_snake_case						,				return_tensors=_snake_case						,				return_attention_mask=_snake_case						,				**_snake_case						,				)
									_lowerCAmelCase						=    titles if not isinstance(_snake_case						,				_snake_case		) else [titles]
									_lowerCAmelCase						=    texts if not isinstance(_snake_case						,				_snake_case		) else [texts]
									_lowerCAmelCase						=    len(_snake_case		)
									_lowerCAmelCase						=    questions if not isinstance(_snake_case						,				_snake_case		) else [questions] * n_passages
									if len(_snake_case		) != len(_snake_case		):
														raise ValueError(
														    F'There should be as many titles than texts but got {len(_snake_case		)} titles and {len(_snake_case		)} texts.'		)
									_lowerCAmelCase						=    super().__call__(_snake_case						,				_snake_case						,				padding=_snake_case						,				truncation=_snake_case		)["""input_ids"""]
									_lowerCAmelCase						=    super().__call__(_snake_case						,				add_special_tokens=_snake_case						,				padding=_snake_case						,				truncation=_snake_case		)["""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(_snake_case						,				_snake_case		)
									    ]
									}
									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(_snake_case						,				padding=_snake_case						,				max_length=_snake_case						,				return_tensors=_snake_case		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case = 16						,				_snake_case = 64						,				_snake_case = 4						,				):



									"""simple docstring"""





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

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




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    []
									for start_index, start_score in enumerate(_snake_case		):
														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(_snake_case						,				key=lambda _snake_case		: x[1]						,				reverse=_snake_case		)
									_lowerCAmelCase						=    []
									for (start_index, end_index), score in scores:
														if start_index > end_index:
																			raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'		)
														_lowerCAmelCase						=    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(_snake_case		) == top_spans:
																			break
									return chosen_span_intervals





@add_end_docstrings(lowerCamelCase__       )
class 					__lowerCAmelCase (     lowerCamelCase__       ,						lowerCamelCase__       ):
				__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''']







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__)
