



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


snake_case__					:     str    		=   logging.get_logger(__name__)

snake_case__					:     List[Any]    		=   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case__					:     Dict    		=   {
    """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"""
        ),
    },
}
snake_case__					:     Optional[int]    		=   {
    """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"""
        ),
    },
}
snake_case__					:     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"""
        ),
    },
}

snake_case__					:     int    		=   {
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
snake_case__					:     Tuple    		=   {
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
snake_case__					:     Optional[int]    		=   {
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


snake_case__					:     Any    		=   {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
snake_case__					:     List[Any]    		=   {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
snake_case__					:     Any    		=   {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}

class   _A   ( UpperCamelCase_	):


						'''simple docstring'''





						_snake_case			:     List[Any]      							=      VOCAB_FILES_NAMES
						_snake_case			:     Optional[int]      							=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_snake_case			:     Optional[int]      							=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_snake_case			:     Tuple      							=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class   _A   ( UpperCamelCase_	):


						'''simple docstring'''





						_snake_case			:     int      							=      VOCAB_FILES_NAMES
						_snake_case			:     List[str]      							=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_snake_case			:     str      							=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_snake_case			:     Tuple      							=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

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


snake_case__					:     int    		=   R"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """

@add_start_docstrings(UpperCamelCase_	)
class   _A   :


						'''simple docstring'''





						def __call__(	self	:							Dict							,      lowerCamelCase	:							int							,      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	:							Any							,      ):






							'''simple docstring'''



							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:
								__lowercase		       =			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							,      )
							__lowercase		       =			titles if not isinstance(__A							,      __A	) else [titles]
							__lowercase		       =			texts if not isinstance(__A							,      __A	) else [texts]
							__lowercase		       =			len(__A	)
							__lowercase		       =			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."""	)
							__lowercase		       =			super().__call__(__A							,      __A							,      padding=__A							,      truncation=__A	)["input_ids"]
							__lowercase		       =			super().__call__(__A							,      add_special_tokens=__A							,      padding=__A							,      truncation=__A	)["input_ids"]
							__lowercase		       =			{
							    "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:
								__lowercase		       =			[]
								for input_ids in encoded_inputs["input_ids"]:
									attention_mask.append([int(input_id != self.pad_token_id	) for input_id in input_ids]	)
								__lowercase		       =			attention_mask
							return self.pad(__A							,      padding=__A							,      max_length=__A							,      return_tensors=__A	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							BatchEncoding							,      lowerCamelCase	:							DPRReaderOutput							,      lowerCamelCase	:							int = 16							,      lowerCamelCase	:							int = 64							,      lowerCamelCase	:							int = 4							,      ):






							'''simple docstring'''



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

								__lowercase		       =			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     _snake_case	(	self	:							Any							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      ):






							'''simple docstring'''



							__lowercase		       =			[]
							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)	)
							__lowercase		       =			sorted(__A							,      key=lambda lowerCamelCase	: x[1]							,      reverse=__A	)
							__lowercase		       =			[]
							for (start_index, end_index), score in scores:
								if start_index > end_index:
									raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""	)
								__lowercase		       =			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(UpperCamelCase_	)
class   _A   ( UpperCamelCase_  ,						UpperCamelCase_	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      VOCAB_FILES_NAMES
						_snake_case			:     List[Any]      							=      READER_PRETRAINED_VOCAB_FILES_MAP
						_snake_case			:     Union[str, Any]      							=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_snake_case			:     List[str]      							=      READER_PRETRAINED_INIT_CONFIGURATION
						_snake_case			:     Union[str, Any]      							=      ["""input_ids""", """attention_mask"""]








from ....utils import logging


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

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=2_048	):






							'''simple docstring'''



							__lowercase		       =			config.__dict__
							__lowercase		       =			modal_hidden_size
							if num_labels:
								__lowercase		       =			num_labels



