

'''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


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 	lowerCAmelCase  (   __snake_case ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 	lowerCAmelCase  (   __snake_case ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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)

    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(__snake_case )
class 	lowerCAmelCase  :







       def __call__(				self      :			Tuple    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			int = None    ,   __lowercase      :			Any = None    ,   __lowercase      :			int = False    ,   __lowercase      :			List[Any] = False    ,   __lowercase      :			Any = None    ,   __lowercase      :			str = None    ,   __lowercase      :			Union[str, Any] = None    ,   **__lowercase      :			int    ,   ):







            """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      :			Any    ,   __lowercase      :			List[str]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			Dict = 16    ,   __lowercase      :			Optional[int] = 64    ,   __lowercase      :			Union[str, Any] = 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      :			str    ,   __lowercase      :			Tuple    ,   __lowercase      :			Dict    ,   __lowercase      :			Any    ,   __lowercase      :			Optional[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 __lowercase   : 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(__snake_case )
class 	lowerCAmelCase  (   __snake_case ,      __snake_case ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			READER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase_												=			['input_ids', 'attention_mask']









import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    A__  :
      _UpperCAmelCase      :Union[str, Any]		       =  None





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  self.feature_extraction_class(**self.feat_extract_dict  )
       UpperCamelCase      :   Union[str, Any]			      =  json.loads(feat_extract.to_json_string()  )
       for key, value in self.feat_extract_dict.items():
        self.assertEqual(obj[key]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  os.path.join(A_			,				"feat_extract.json"  )
        feat_extract_first.to_json_file(A_  )
        UpperCamelCase      :   str			      =  self.feature_extraction_class.from_json_file(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   int			      =  feat_extract_first.save_pretrained(A_  )[0]
        check_json_file_has_correct_format(A_  )
        UpperCamelCase      :   Optional[Any]			      =  self.feature_extraction_class.from_pretrained(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.feature_extraction_class()
       self.assertIsNotNone(A_  )
