
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	(						A_						):
     UpperCAmelCase__       :							Dict     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							str     =	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							str     =	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							Tuple     =	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							List[Any]     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							int     =	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							Union[str, Any]     =	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							List[str]     =	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"\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(A_						)
class   lowerCamelCase	:





     def __call__(self   :					int       ,					_A   :					Tuple       ,					_A   :					Optional[str] = None       ,					_A   :					Optional[str] = None       ,					_A   :					Union[bool, str] = False       ,					_A   :					Union[bool, str] = False       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[Union[str, TensorType]] = None       ,					_A   :					Optional[bool] = None       ,					**_A   :					Optional[int]       ,					)		->		BatchEncoding:
         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:
             snake_case				  =      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       ,					)
         snake_case				  =      titles if not isinstance(_A       ,					_A     ) else [titles]
         snake_case				  =      texts if not isinstance(_A       ,					_A     ) else [texts]
         snake_case				  =      len(_A     )
         snake_case				  =      questions if not isinstance(_A       ,					_A     ) else [questions] * n_passages
         if len(_A     ) != len(_A     ):
             raise ValueError(
                 f'There should be as many titles than texts but got {len(_A     )} titles and {len(_A     )} texts.'     )
         snake_case				  =      super().__call__(_A       ,					_A       ,					padding=_A       ,					truncation=_A     )["input_ids"]
         snake_case				  =      super().__call__(_A       ,					add_special_tokens=_A       ,					padding=_A       ,					truncation=_A     )["input_ids"]
         snake_case				  =      {
             "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:
             snake_case				  =      []
             for input_ids in encoded_inputs["input_ids"]:
                 attention_mask.append([int(input_id != self.pad_token_id     ) for input_id in input_ids]     )
             snake_case				  =      attention_mask
         return self.pad(_A       ,					padding=_A       ,					max_length=_A       ,					return_tensors=_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					BatchEncoding       ,					_A   :					DPRReaderOutput       ,					_A   :					int = 1_6       ,					_A   :					int = 6_4       ,					_A   :					int = 4       ,					)		->		List[DPRSpanPrediction]:
         snake_case				  =      reader_input["input_ids"]
         snake_case  ,							snake_case  ,							snake_case				  =      reader_output[:3]
         snake_case				  =      len(_A     )
         snake_case				  =      sorted(range(_A     )       ,					reverse=_A       ,					key=relevance_logits.__getitem__     )
         snake_case				  =      []
         for doc_id in sorted_docs:
             snake_case				  =      list(input_ids[doc_id]     )
             # assuming question & title information is at the beginning of the sequence
             snake_case				  =      sequence_ids.index(self.sep_token_id       ,					2     ) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                 snake_case				  =      sequence_ids.index(self.pad_token_id     )
             else:
                 snake_case				  =      len(_A     )

             snake_case				  =      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 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					List[int]       ,					_A   :					List[int]       ,					_A   :					int       ,					_A   :					int       ,					)		->		List[DPRSpanPrediction]:
         snake_case				  =      []
         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)     )
         snake_case				  =      sorted(_A       ,					key=lambda _A     : x[1]       ,					reverse=_A     )
         snake_case				  =      []
         for (start_index, end_index), score in scores:
             if start_index > end_index:
                 raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'     )
             snake_case				  =      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(_A     ) == top_spans:
                 break
         return chosen_span_intervals







@add_end_docstrings(A_						)
class   lowerCamelCase	(						A_							,				A_						):
     UpperCAmelCase__       :							Dict     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							Dict     =	READER_PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							Optional[int]     =	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							str     =	READER_PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__       :							int     =	["input_ids", "attention_mask"]



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Optional[int]     =	"roberta"





     def __init__(self   :					Union[str, Any]       ,					_A   :					List[Any]=5_0_2_6_5       ,					_A   :					Dict=7_6_8       ,					_A   :					Tuple=1_2       ,					_A   :					Optional[Any]=1_2       ,					_A   :					int=3_0_7_2       ,					_A   :					List[str]="gelu"       ,					_A   :					Tuple=0.1       ,					_A   :					Dict=0.1       ,					_A   :					Optional[int]=5_1_2       ,					_A   :					Dict=2       ,					_A   :					Optional[Any]=0.02       ,					_A   :					Optional[Any]=1E-12       ,					_A   :					str=1       ,					_A   :					Dict=0       ,					_A   :					Optional[int]=2       ,					_A   :					int="absolute"       ,					_A   :					Any=True       ,					_A   :					Union[str, Any]=None       ,					**_A   :					Optional[int]       ,					)		->		Tuple:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      vocab_size
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      hidden_act
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      max_position_embeddings
         snake_case				  =      type_vocab_size
         snake_case				  =      initializer_range
         snake_case				  =      layer_norm_eps
         snake_case				  =      position_embedding_type
         snake_case				  =      use_cache
         snake_case				  =      classifier_dropout







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             snake_case				  =      {0: "batch", 1: "choice", 2: "sequence"}
         else:
             snake_case				  =      {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]     )

