



"""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


_lowercase			:			Tuple										=						logging.get_logger(__name__)

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

_lowercase			:			List[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"
        ),
    },
}
_lowercase			:			Dict										=						{
    "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"
        ),
    },
}
_lowercase			:			str										=						{
    "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"
        ),
    },
}

_lowercase			:			List[Any]										=						{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
_lowercase			:			List[Any]										=						{
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
_lowercase			:			str										=						{
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


_lowercase			:			Optional[int]										=						{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase			:			Tuple										=						{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase			:			Tuple										=						{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					_a             =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a             =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					_a             =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a             =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

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


_lowercase			:			Dict										=						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(lowerCAmelCase_       )
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __call__(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None,  lowerCamelCase							:  Optional[str] = None,  lowerCamelCase							:  Union[bool, str] = False,  lowerCamelCase							:  Union[bool, str] = False,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  lowerCamelCase							:  Optional[bool] = None,  **lowerCamelCase							:  Tuple,  )-> BatchEncoding:
								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__		:		Tuple		     =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__		:		List[Any]		     =titles if not isinstance(lowerCamelCase,  lowerCamelCase					) else [titles]
								lowerCamelCase__		:		Dict		     =texts if not isinstance(lowerCamelCase,  lowerCamelCase					) else [texts]
								lowerCamelCase__		:		List[Any]		     =len(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =questions if not isinstance(lowerCamelCase,  lowerCamelCase					) else [questions] * n_passages
								if len(lowerCamelCase					) != len(lowerCamelCase					):
											raise ValueError(
											    F'''There should be as many titles than texts but got {len(lowerCamelCase					)} titles and {len(lowerCamelCase					)} texts.'''					)
								lowerCamelCase__		:		Optional[int]		     =super().__call__(lowerCamelCase,  lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase					)['''input_ids''']
								lowerCamelCase__		:		Union[str, Any]		     =super().__call__(lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase					)['''input_ids''']
								lowerCamelCase__		:		Dict		     ={
								    '''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__		:		List[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(lowerCamelCase,  padding=lowerCamelCase,  max_length=lowerCamelCase,  return_tensors=lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  BatchEncoding,  lowerCamelCase							:  DPRReaderOutput,  lowerCamelCase							:  int = 16,  lowerCamelCase							:  int = 64,  lowerCamelCase							:  int = 4,  )-> List[DPRSpanPrediction]:
								lowerCamelCase__		:		Dict		     =reader_input['''input_ids''']
								lowerCamelCase__		:		Optional[Any]		     =reader_output[:3]
								lowerCamelCase__		:		Optional[Any]		     =len(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =sorted(range(lowerCamelCase					),  reverse=lowerCamelCase,  key=relevance_logits.__getitem__					)
								lowerCamelCase__		:		List[DPRReaderOutput]		     =[]
								for doc_id in sorted_docs:
											lowerCamelCase__		:		str		     =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__		:		Union[str, Any]		     =sequence_ids.index(self.pad_token_id					)
											else:
														lowerCamelCase__		:		Dict		     =len(lowerCamelCase					)

											lowerCamelCase__		:		Dict		     =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 		snake_case	(	self							:  str,  lowerCamelCase							:  List[int],  lowerCamelCase							:  List[int],  lowerCamelCase							:  int,  lowerCamelCase							:  int,  )-> List[DPRSpanPrediction]:
								lowerCamelCase__		:		str		     =[]
								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__		:		List[str]		     =sorted(lowerCamelCase,  key=lambda lowerCamelCase					: x[1],  reverse=lowerCamelCase					)
								lowerCamelCase__		:		Optional[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__		:		Any		     =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(lowerCamelCase					) == top_spans:
														break
								return chosen_span_intervals





@add_end_docstrings(lowerCAmelCase_       )
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							READER_PRETRAINED_VOCAB_FILES_MAP
					_a             =							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a             =							READER_PRETRAINED_INIT_CONFIGURATION
					_a             =							['input_ids', 'attention_mask']









"""simple docstring"""






from ..utils import DummyObject, requires_backends

class 	__SCREAMING_SNAKE_CASE     (				metaclass=lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							['torch', 'torchsde']



					def __init__(	self							:  Union[str, Any],  *lowerCamelCase							:  str,  **lowerCamelCase							:  int					)-> Tuple:
								requires_backends(self,  ['''torch''', '''torchsde''']					)



					@classmethod
					def 		snake_case	(	cls							:  List[str],  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  Dict					)-> str:
								requires_backends(cls,  ['''torch''', '''torchsde''']					)




					@classmethod
					def 		snake_case	(	cls							:  Tuple,  *lowerCamelCase							:  Dict,  **lowerCamelCase							:  Tuple					)-> List[str]:
								requires_backends(cls,  ['''torch''', '''torchsde''']					)




