
'''simple docstring'''




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase			:				Optional[Any]    		=			50_00_00

lowercase		,     lowercase			:				Union[str, Any]    		=			os.path.split(__file__)
lowercase			:				Any    		=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))







@get_duration
def 		lowerCAmelCase_				(   snake_case__		,				**snake_case__						):




    '''simple docstring'''



    A       :   Tuple					=						dataset.map(**snake_case__						)







@get_duration
def 		lowerCAmelCase_				(   snake_case__		,				**snake_case__						):




    '''simple docstring'''



    A       :   str					=						dataset.filter(**snake_case__						)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   Dict					=						{'''num examples''': SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        A       :   str					=						datasets.Features({'''text''': datasets.Value('''string'''						), '''numbers''': datasets.Value('''float32'''						)}						)
        A       :   List[Any]					=						generate_example_dataset(
            os.path.join(snake_case__		,				'''dataset.arrow'''						)		,				snake_case__		,				num_examples=snake_case__						)

        A       :   Tuple					=						transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''		,				use_fast=snake_case__						)

        def tokenize(snake_case__						):
            return tokenizer(examples['''text''']						)

        A       :   Any					=						map(snake_case__						)

        A       :   List[Any]					=						map(snake_case__		,				batched=snake_case__						)

        A       :   str					=						map(snake_case__		,				function=lambda snake_case__						: None		,				batched=snake_case__						)

        with dataset.formatted_as(type='''numpy'''						):
            A       :   Union[str, Any]					=						map(snake_case__		,				function=lambda snake_case__						: None		,				batched=snake_case__						)

        with dataset.formatted_as(type='''pandas'''						):
            A       :   int					=						map(snake_case__		,				function=lambda snake_case__						: None		,				batched=snake_case__						)

        with dataset.formatted_as(type='''torch'''		,				columns='''numbers'''						):
            A       :   List[str]					=						map(snake_case__		,				function=lambda snake_case__						: None		,				batched=snake_case__						)

        with dataset.formatted_as(type='''tensorflow'''		,				columns='''numbers'''						):
            A       :   Optional[Any]					=						map(snake_case__		,				function=lambda snake_case__						: None		,				batched=snake_case__						)

        A       :   Any					=						map(snake_case__		,				function=snake_case__		,				batched=snake_case__						)

        A       :   str					=						filter(snake_case__						)

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(snake_case__		,				'''wb'''						) as f:
        f.write(json.dumps(snake_case__						).encode('''utf-8'''						)						)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_map_filter()










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


_lowerCamelCase	:		Union[str, Any]							       =   logging.get_logger(__name__)

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

_lowerCamelCase	:		Union[str, Any]							       =   {
    """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	:		str							       =   {
    """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	:		str							       =   {
    """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	:		Any							       =   {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		List[str]							       =   {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		Tuple							       =   {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase__							  =      DPRContextEncoderTokenizer


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase__							  =      DPRQuestionEncoderTokenizer
_lowerCamelCase	:		int							       =   collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

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


_lowerCamelCase	:		Dict							       =   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(UpperCAmelCase__     )
class 				UpperCamelCase_    :

    '''simple docstring'''






    def __call__(			self      :	Optional[int]					, UpperCAmelCase__      :	Tuple					, 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__      :	Optional[int]					, )						->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:
                  A__       =    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__					, )
           A__       =    titles if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [titles]
           A__       =    texts if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [texts]
           A__       =    len(UpperCAmelCase__)
           A__       =    questions if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [questions] * n_passages
           assert len(UpperCAmelCase__) == len(
               UpperCAmelCase__), f"""There should be as many titles than texts but got {len(UpperCAmelCase__)} titles and {len(UpperCAmelCase__)} texts."""
           A__       =    super().__call__(UpperCAmelCase__					, UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__)['''input_ids''']
           A__       =    super().__call__(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__)['''input_ids''']
           A__       =    {
               '''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:
                  A__       =    []
                  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__       =    attention_mask
           return self.pad(UpperCAmelCase__					, padding=UpperCAmelCase__					, max_length=UpperCAmelCase__					, return_tensors=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	BatchEncoding					, UpperCAmelCase__      :	DPRReaderOutput					, UpperCAmelCase__      :	int = 16					, UpperCAmelCase__      :	int = 64					, UpperCAmelCase__      :	int = 4					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

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

                  A__       =    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      :	Any					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	int					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

           A__       =    []
           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))
           A__       =    sorted(UpperCAmelCase__					, key=lambda UpperCAmelCase__: x[1]					, reverse=UpperCAmelCase__)
           A__       =    []
           for (start_index, end_index), score in scores:
                  assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
                  A__       =    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(UpperCAmelCase__) == top_spans:
                         break
           return chosen_span_intervals


@add_end_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      READER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      READER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase__							  =      ['''input_ids''', '''attention_mask''']
    UpperCAmelCase__							  =      DPRReaderTokenizer


