



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


UpperCAmelCase_									=							logging.get_logger(__name__)

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

UpperCAmelCase_									=							{
    "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"
        ),
    },
}
UpperCAmelCase_									=							{
    "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"
        ),
    },
}
UpperCAmelCase_									=							{
    "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"
        ),
    },
}

UpperCAmelCase_									=							{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
UpperCAmelCase_									=							{
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
UpperCAmelCase_									=							{
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


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





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE__							=     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE__							=     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE__							=     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
   SCREAMING_SNAKE_CASE__							=     DPRContextEncoderTokenizer






class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE__							=     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE__							=     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE__							=     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
   SCREAMING_SNAKE_CASE__							=     DPRQuestionEncoderTokenizer






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

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


UpperCAmelCase_									=							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(snake_case_				)
class 		UpperCAmelCase						:





   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:
               _lowerCAmelCase 			=  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       ,     )
         _lowerCAmelCase 			=  titles if not isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			) else [titles]
         _lowerCAmelCase 			=  texts if not isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			) else [texts]
         _lowerCAmelCase 			=  len(_lowerCAmelCase			)
         _lowerCAmelCase 			=  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.'''
         _lowerCAmelCase 			=  super().__call__(_lowerCAmelCase       ,     _lowerCAmelCase       ,     padding=_lowerCAmelCase       ,     truncation=_lowerCAmelCase			)['''input_ids''']
         _lowerCAmelCase 			=  super().__call__(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase       ,     padding=_lowerCAmelCase       ,     truncation=_lowerCAmelCase			)['''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(_lowerCAmelCase       ,     _lowerCAmelCase			)
             ]
         }
         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(_lowerCAmelCase       ,     padding=_lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 16       ,     _lowerCAmelCase = 64       ,     _lowerCAmelCase = 4       ,     ):
         _lowerCAmelCase 			=  reader_input['''input_ids''']
         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  reader_output[:3]
         _lowerCAmelCase 			=  len(_lowerCAmelCase			)
         _lowerCAmelCase 			=  sorted(range(_lowerCAmelCase			)       ,     reverse=_lowerCAmelCase       ,     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(_lowerCAmelCase			)

               _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=_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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  []
         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)			)
         _lowerCAmelCase 			=  sorted(_lowerCAmelCase       ,     key=lambda _lowerCAmelCase			: x[1]       ,     reverse=_lowerCAmelCase			)
         _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(_lowerCAmelCase			) == top_spans:
                     break
         return chosen_span_intervals






@add_end_docstrings(snake_case_				)
class 		UpperCAmelCase						(						snake_case_       ,snake_case_				):
   SCREAMING_SNAKE_CASE__							=     VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE__							=     READER_PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE__							=     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE__							=     READER_PRETRAINED_INIT_CONFIGURATION
   SCREAMING_SNAKE_CASE__							=     ['''input_ids''', '''attention_mask''']
   SCREAMING_SNAKE_CASE__							=     DPRReaderTokenizer




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''ClapFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)

         if text is None and audios is None:
               raise ValueError('''You have to specify either text or audios. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if audios is not None:
               _lowerCAmelCase 			=  self.feature_extractor(
                   _lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and audios is not None:
               _lowerCAmelCase 			=  audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)