






'''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       		=      logging.get_logger(__name__)

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

__lowerCAmelCase       		=      {
    "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       		=      {
    "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       		=      {
    "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       		=      {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__lowerCAmelCase       		=      {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__lowerCAmelCase       		=      {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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





class 	SCREAMING_SNAKE_CASE					(					__UpperCAmelCase       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 	SCREAMING_SNAKE_CASE					(					__UpperCAmelCase       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

__lowerCAmelCase       		=      collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__lowerCAmelCase       		=      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 	SCREAMING_SNAKE_CASE					:






			def __call__( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None  ,						__SCREAMING_SNAKE_CASE      :			Union[bool, str] = False  ,						__SCREAMING_SNAKE_CASE      :			Union[bool, str] = False  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[str, TensorType]] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None  ,						**__SCREAMING_SNAKE_CASE      :			List[str]  ,						)			->			str:







							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:
											a_		:  List[str]											=      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_  ,						)
							a_		:  Union[str, Any]											=      titles if not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_					) else [titles]
							a_		:  Any											=      texts if not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_					) else [texts]
							a_		:  int											=      len(lowerCAmelCase_					)
							a_		:  str											=      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.'					)
							a_		:  Union[str, Any]											=      super().__call__(lowerCAmelCase_  ,						lowerCAmelCase_  ,						padding=lowerCAmelCase_  ,						truncation=lowerCAmelCase_					)['''input_ids''']
							a_		:  Optional[int]											=      super().__call__(lowerCAmelCase_  ,						add_special_tokens=lowerCAmelCase_  ,						padding=lowerCAmelCase_  ,						truncation=lowerCAmelCase_					)['''input_ids''']
							a_		:  Tuple											=      {
							    '''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:
											a_		:  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]					)
											a_		:  Optional[int]											=      attention_mask
							return self.pad(lowerCAmelCase_  ,						padding=lowerCAmelCase_  ,						max_length=lowerCAmelCase_  ,						return_tensors=lowerCAmelCase_					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			BatchEncoding  ,						__SCREAMING_SNAKE_CASE      :			DPRReaderOutput  ,						__SCREAMING_SNAKE_CASE      :			int = 16  ,						__SCREAMING_SNAKE_CASE      :			int = 64  ,						__SCREAMING_SNAKE_CASE      :			int = 4  ,						)			->			List[Any]:
							a_		:  Any											=      reader_input['''input_ids''']
							a_				,			a_				,			a_		:  Union[str, Any]											=      reader_output[:3]
							a_		:  str											=      len(lowerCAmelCase_					)
							a_		:  List[Any]											=      sorted(range(lowerCAmelCase_					)  ,						reverse=lowerCAmelCase_  ,						key=relevance_logits.__getitem__					)
							a_		:  Optional[int]											=      []
							for doc_id in sorted_docs:
											a_		:  Tuple											=      list(input_ids[doc_id]					)
											# assuming question & title information is at the beginning of the sequence
											a_		:  Any											=      sequence_ids.index(self.sep_token_id  ,						2					) + 1  # second sep id
											if sequence_ids[-1] == self.pad_token_id:
															a_		:  Union[str, Any]											=      sequence_ids.index(self.pad_token_id					)
											else:
															a_		:  int											=      len(lowerCAmelCase_					)

											a_		:  Union[str, 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=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 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int  ,						)			->			List[Any]:
							a_		:  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)					)
							a_		:  int											=      sorted(lowerCAmelCase_  ,						key=lambda __SCREAMING_SNAKE_CASE					: x[1]  ,						reverse=lowerCAmelCase_					)
							a_		:  str											=      []
							for (start_index, end_index), score in scores:
											if start_index > end_index:
															raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'					)
											a_		:  Optional[int]											=      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(__UpperCAmelCase       )
class 	SCREAMING_SNAKE_CASE					(					__UpperCAmelCase	,   __UpperCAmelCase       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				READER_PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				READER_PRETRAINED_INIT_CONFIGURATION
			snake_case__  =				["input_ids", "attention_mask"]












'''simple docstring'''







import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




