





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


__A									=				logging.get_logger(__name__)

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

__A									=				{
    "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"
        ),
    },
}
__A									=				{
    "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"
        ),
    },
}
__A									=				{
    "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"
        ),
    },
}

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


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



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

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


__A									=				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(__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

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



   '''simple docstring'''



   if titles is None and texts is None:
     return super().__call__(
         UpperCAmelCase_       , padding=UpperCAmelCase_       , truncation=UpperCAmelCase_       , max_length=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , return_attention_mask=UpperCAmelCase_       , **UpperCAmelCase_       , )
   elif titles is None or texts is None:
     lowerCamelCase__:      Optional[int]			  =titles if texts is None else texts
     return super().__call__(
         UpperCAmelCase_       , UpperCAmelCase_       , padding=UpperCAmelCase_       , truncation=UpperCAmelCase_       , max_length=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , return_attention_mask=UpperCAmelCase_       , **UpperCAmelCase_       , )
   lowerCamelCase__:      List[str]			  =titles if not isinstance(UpperCAmelCase_       , UpperCAmelCase_) else [titles]
   lowerCamelCase__:      List[str]			  =texts if not isinstance(UpperCAmelCase_       , UpperCAmelCase_) else [texts]
   lowerCamelCase__:      int			  =len(UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =questions if not isinstance(UpperCAmelCase_       , UpperCAmelCase_) else [questions] * n_passages
   if len(UpperCAmelCase_) != len(UpperCAmelCase_):
     raise ValueError(
         F"""There should be as many titles than texts but got {len(UpperCAmelCase_)} titles and {len(UpperCAmelCase_)} texts.""")
   lowerCamelCase__:      int			  =super().__call__(UpperCAmelCase_       , UpperCAmelCase_       , padding=UpperCAmelCase_       , truncation=UpperCAmelCase_)["input_ids"]
   lowerCamelCase__:      List[Any]			  =super().__call__(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_       , padding=UpperCAmelCase_       , truncation=UpperCAmelCase_)["input_ids"]
   lowerCamelCase__:      Any			  ={
       "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(UpperCAmelCase_       , UpperCAmelCase_)
       ]
   }
   if return_attention_mask is not False:
     lowerCamelCase__:      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__:      Dict			  =attention_mask
   return self.pad(UpperCAmelCase_       , padding=UpperCAmelCase_       , max_length=UpperCAmelCase_       , return_tensors=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   BatchEncoding       , UpperCAmelCase_				:   DPRReaderOutput       , UpperCAmelCase_				:   int = 16       , UpperCAmelCase_				:   int = 64       , UpperCAmelCase_				:   int = 4       , )       ->List[DPRSpanPrediction]:



   '''simple docstring'''



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

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







 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   int       , )       ->List[DPRSpanPrediction]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =[]
   for start_index, start_score in enumerate(UpperCAmelCase_):
     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(UpperCAmelCase_       , key=lambda UpperCAmelCase_: x[1]       , reverse=UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =[]
   for (start_index, end_index), score in scores:
     if start_index > end_index:
       raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
     lowerCamelCase__:      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(UpperCAmelCase_) == top_spans:
       break
   return chosen_span_intervals



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







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			READER_PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			READER_PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			["input_ids", "attention_mask"]







import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a = None		,     _a = None		,     _a = False		,     **_a		,     )						->							int:
           super().__init__(features=_a		,     cache_dir=_a		,     keep_in_memory=_a		,     **_a				)
           _A							:     Optional[int]			=						Sql(
               cache_dir=_a		,     features=_a		,     sql=_a		,     con=_a		,     **_a		,     )



     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Tuple			=						None
           _A							:     int			=						None
           _A							:     Tuple			=						None
           _A							:     Union[str, Any]			=						None

           self.builder.download_and_prepare(
               download_config=_a		,     download_mode=_a		,     verification_mode=_a		,     base_path=_a		,     )

           # Build dataset for splits
           _A							:     int			=						self.builder.as_dataset(
               split="""train"""		,     verification_mode=_a		,     in_memory=self.keep_in_memory				)
           return dataset







class 						lowercase							:

     def __init__(  self		,     _a		,     _a		,     _a		,     _a = None		,     _a = None		,     **_a		,     )						->							Union[str, Any]:
           if num_proc is not None and num_proc <= 0:
                 raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''				)

           _A							:     Dict			=						dataset
           _A							:     int			=						name
           _A							:     Union[str, Any]			=						con
           _A							:     str			=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           _A							:     str			=						num_proc
           _A							:     Optional[Any]			=						to_sql_kwargs

     def 					a__	(  self				)						->							int:
           _A							:     Any			=						self.to_sql_kwargs.pop("""sql"""		,     _a				)
           _A							:     List[str]			=						self.to_sql_kwargs.pop("""con"""		,     _a				)
           _A							:     int			=						self.to_sql_kwargs.pop("""index"""		,     _a				)

           _A							:     List[str]			=						self._write(index=_a		,     **self.to_sql_kwargs				)
           return written

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           _A			,   _A			,   _A							:     List[str]			=						args
           _A							:     int			=						{**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
           _A							:     str			=						query_table(
               table=self.dataset.data		,     key=slice(_a		,     offset + self.batch_size				)		,     indices=self.dataset._indices		,     )
           _A							:     Tuple			=						batch.to_pandas()
           _A							:     Union[str, Any]			=						df.to_sql(self.name		,     self.con		,     index=_a		,     **_a				)
           return num_rows or len(_a				)



     def 					a__	(  self		,     _a		,     **_a				)						->							int:
           _A							:     Any			=						0

           if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0		,     len(self.dataset				)		,     self.batch_size				)		,     unit="""ba"""		,     disable=not logging.is_progress_bar_enabled()		,     desc="""Creating SQL from Arrow format"""		,     ):
                       written += self._batch_sql((offset, index, to_sql_kwargs)				)
           else:
                 _A			,   _A							:     Tuple			=						len(self.dataset				), self.batch_size
                 with multiprocessing.Pool(self.num_proc				) as pool:
                       for num_rows in logging.tqdm(
                           pool.imap(
                               self._batch_sql		,     [(offset, index, to_sql_kwargs) for offset in range(0		,     _a		,     _a				)]		,     )		,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,     unit="""ba"""		,     disable=not logging.is_progress_bar_enabled()		,     desc="""Creating SQL from Arrow format"""		,     ):
                             written += num_rows

           return written

