




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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCamelCase__    			=			logging.get_logger(__name__)

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

UpperCamelCase__    			=			{
    """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"""
        ),
    },
}
UpperCamelCase__    			=			{
    """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"""
        ),
    },
}
UpperCamelCase__    			=			{
    """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"""
        ),
    },
}

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


UpperCamelCase__    			=			{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
UpperCamelCase__    			=			{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
UpperCamelCase__    			=			{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class 			a__    (					a__      ):
						_a							:    List[str]					=    VOCAB_FILES_NAMES
						_a							:    List[str]					=    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_a							:    Dict					=    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:    Any					=    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						_a							:    int					=    DPRContextEncoderTokenizer






class 			a__    (					a__      ):
						_a							:    Union[str, Any]					=    VOCAB_FILES_NAMES
						_a							:    Any					=    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_a							:    Tuple					=    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:    Optional[Any]					=    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						_a							:    Tuple					=    DPRQuestionEncoderTokenizer






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

UpperCamelCase__    			=			collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


UpperCamelCase__    			=			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)

    Return:
        `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(a__      )
class 			a__    :






						def __call__(   self		,			_A		,			_A = None		,			_A = None		,			_A = False		,			_A = False		,			_A = None		,			_A = None		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											if titles is None and texts is None:
																return super().__call__(
																    SCREAMING_SNAKE_CASE_		,			padding=SCREAMING_SNAKE_CASE_		,			truncation=SCREAMING_SNAKE_CASE_		,			max_length=SCREAMING_SNAKE_CASE_		,			return_tensors=SCREAMING_SNAKE_CASE_		,			return_attention_mask=SCREAMING_SNAKE_CASE_		,			**SCREAMING_SNAKE_CASE_		,			)
											elif titles is None or texts is None:
																__lowerCAmelCase				   =       titles if texts is None else texts
																return super().__call__(
																    SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			padding=SCREAMING_SNAKE_CASE_		,			truncation=SCREAMING_SNAKE_CASE_		,			max_length=SCREAMING_SNAKE_CASE_		,			return_tensors=SCREAMING_SNAKE_CASE_		,			return_attention_mask=SCREAMING_SNAKE_CASE_		,			**SCREAMING_SNAKE_CASE_		,			)
											__lowerCAmelCase				   =       titles if not isinstance(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			) else [titles]
											__lowerCAmelCase				   =       texts if not isinstance(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			) else [texts]
											__lowerCAmelCase				   =       len(SCREAMING_SNAKE_CASE_			)
											__lowerCAmelCase				   =       questions if not isinstance(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			) else [questions] * n_passages
											assert len(SCREAMING_SNAKE_CASE_			) == len(
											    SCREAMING_SNAKE_CASE_			), f"""There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_			)} titles and {len(SCREAMING_SNAKE_CASE_			)} texts."""
											__lowerCAmelCase				   =       super().__call__(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			padding=SCREAMING_SNAKE_CASE_		,			truncation=SCREAMING_SNAKE_CASE_			)['input_ids']
											__lowerCAmelCase				   =       super().__call__(SCREAMING_SNAKE_CASE_		,			add_special_tokens=SCREAMING_SNAKE_CASE_		,			padding=SCREAMING_SNAKE_CASE_		,			truncation=SCREAMING_SNAKE_CASE_			)['input_ids']
											__lowerCAmelCase				   =       {
											    '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(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)
											    ]
											}
											if return_attention_mask is not False:
																__lowerCAmelCase				   =       []
																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				   =       attention_mask
											return self.pad(SCREAMING_SNAKE_CASE_		,			padding=SCREAMING_SNAKE_CASE_		,			max_length=SCREAMING_SNAKE_CASE_		,			return_tensors=SCREAMING_SNAKE_CASE_			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = 1_6		,			_A = 6_4		,			_A = 4		,			):




											"""simple docstring"""



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

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_			):
																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				   =       sorted(SCREAMING_SNAKE_CASE_		,			key=lambda _A			: x[1]		,			reverse=SCREAMING_SNAKE_CASE_			)
											__lowerCAmelCase				   =       []
											for (start_index, end_index), score in scores:
																assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
																__lowerCAmelCase				   =       end_index - start_index + 1
																assert length <= max_answer_length, 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(SCREAMING_SNAKE_CASE_			) == top_spans:
																					break
											return chosen_span_intervals






@add_end_docstrings(a__      )
class 			a__    (					a__ ,				a__      ):
						_a							:    List[str]					=    VOCAB_FILES_NAMES
						_a							:    Dict					=    READER_PRETRAINED_VOCAB_FILES_MAP
						_a							:    List[Any]					=    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:    Tuple					=    READER_PRETRAINED_INIT_CONFIGURATION
						_a							:    Union[str, Any]					=    ["input_ids", "attention_mask"]
						_a							:    Any					=    DPRReaderTokenizer









import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Optional[int]:
   monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'       ,     set()	)
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Any:



   class 				lowerCAmelCase_		:
          def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
             UpperCamelCase    :  Tuple							=	metric_id






   class 				lowerCAmelCase_		:
          UpperCAmelCase__				:					Optional[int]        =  [MetricMock(a__							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
          def 					snake_case_       (			self       )     ->		str:
             return self._metrics






   monkeypatch.setattr('datasets.inspect.huggingface_hub'       ,     HfhMock()	)


@pytest.mark.parametrize(
    'func, args'       ,     [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]	)
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Any       ,     snake_case__       :    List[str]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   if "tmp_path" in args:
      UpperCamelCase    :  Dict							=	tuple(arg if arg != 'tmp_path' else tmp_path for arg in args	)
   with pytest.warns(snake_case__       ,     match='https://huggingface.co/docs/evaluate'	):
      func(*snake_case__	)



