


'''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 import BertTokenizer


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

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

lowerCAmelCase    :     str					=						{
    "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[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"
        ),
    },
}
lowerCAmelCase    :     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"
        ),
    },
}

lowerCAmelCase    :     Dict					=						{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
lowerCAmelCase    :     Tuple					=						{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
lowerCAmelCase    :     int					=						{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


lowerCAmelCase    :     Any					=						{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowerCAmelCase    :     List[str]					=						{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowerCAmelCase    :     Any					=						{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class 	UpperCamelCase__					( _UpperCAmelCase	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class 	UpperCamelCase__					( _UpperCAmelCase	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase    :     Dict					=						collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

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


lowerCAmelCase    :     Union[str, Any]					=						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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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 	UpperCamelCase__					:



     """simple docstring"""






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







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = 16      ,	snake_case__ = 64      ,	snake_case__ = 4      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       reader_input["""input_ids"""]
            _lowerCAmelCase    :							Tuple   			=       reader_output[:3]
            _lowerCAmelCase    :							Optional[Any]   			=       len(lowercase_   )
            _lowerCAmelCase    :							Optional[Any]   			=       sorted(range(lowercase_   )      ,	reverse=lowercase_      ,	key=relevance_logits.__getitem__   )
            _lowerCAmelCase    :							List[DPRReaderOutput]   			=       []
            for doc_id in sorted_docs:
                   _lowerCAmelCase    :							Optional[int]   			=       list(input_ids[doc_id]   )
                   # assuming question & title information is at the beginning of the sequence
                   _lowerCAmelCase    :							int   			=       sequence_ids.index(self.sep_token_id      ,	2   ) + 1  # second sep id
                   if sequence_ids[-1] == self.pad_token_id:
                          _lowerCAmelCase    :							Any   			=       sequence_ids.index(self.pad_token_id   )
                   else:
                          _lowerCAmelCase    :							Optional[int]   			=       len(lowercase_   )

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







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       []
            for start_index, start_score in enumerate(lowercase_   ):
                   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    :							Dict   			=       sorted(lowercase_      ,	key=lambda snake_case__   : x[1]      ,	reverse=lowercase_   )
            _lowerCAmelCase    :							Any   			=       []
            for (start_index, end_index), score in scores:
                   if start_index > end_index:
                          raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'   )
                   _lowerCAmelCase    :							Tuple   			=       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(lowercase_   ) == top_spans:
                          break
            return chosen_span_intervals
@add_end_docstrings(_UpperCAmelCase	)
class 	UpperCamelCase__					( _UpperCAmelCase    ,					_UpperCAmelCase	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    READER_PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    READER_PRETRAINED_INIT_CONFIGURATION
     __magic_name__    			=    ["input_ids", "attention_mask"]





'''simple docstring'''







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

            return {"attention_mask": attention_mask, "input_ids": input_ids}

