






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


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''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_							      = {
    '''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_							      = {
    '''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_							      = {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
lowerCamelCase_							      = {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
lowerCamelCase_							      = {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


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







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__       =							DPRContextEncoderTokenizer







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__       =							DPRQuestionEncoderTokenizer





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

lowerCamelCase_							      = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCamelCase_							      = 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      UpperCamelCase_			:





	def __call__( self   :					Any      ,      lowerCAmelCase_   :					Union[str, Any]      ,      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_   :					Dict      ,      )		->     BatchEncoding:
				if titles is None and texts is None:
							return super().__call__(
							    lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				elif titles is None or texts is None:
							UpperCAmelCase_	:					int     	=   titles if texts is None else texts
							return super().__call__(
							    lowerCAmelCase_      ,      lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				UpperCAmelCase_	:					Dict     	=   titles if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else [titles]
				UpperCAmelCase_	:					str     	=   texts if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else [texts]
				UpperCAmelCase_	:					int     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   questions if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else [questions] * n_passages
				assert len(lowerCAmelCase_		) == len(
				    lowerCAmelCase_		), f"""There should be as many titles than texts but got {len(lowerCAmelCase_		)} titles and {len(lowerCAmelCase_		)} texts."""
				UpperCAmelCase_	:					Optional[int]     	=   super().__call__(lowerCAmelCase_      ,      lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_		)["input_ids"]
				UpperCAmelCase_	:					List[str]     	=   super().__call__(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_		)["input_ids"]
				UpperCAmelCase_	:					Tuple     	=   {
				    "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(lowerCAmelCase_      ,      lowerCAmelCase_		)
				    ]
				}
				if return_attention_mask is not False:
							UpperCAmelCase_	:					Dict     	=   []
							for input_ids in encoded_inputs["input_ids"]:
										attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
							UpperCAmelCase_	:					List[Any]     	=   attention_mask
				return self.pad(lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					BatchEncoding      ,      lowerCAmelCase_   :					DPRReaderOutput      ,      lowerCAmelCase_   :					int = 16      ,      lowerCAmelCase_   :					int = 64      ,      lowerCAmelCase_   :					int = 4      ,      )		->     List[DPRSpanPrediction]:
				UpperCAmelCase_	:					int     	=   reader_input["input_ids"]
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   reader_output[:3]
				UpperCAmelCase_	:					Optional[Any]     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   sorted(range(lowerCAmelCase_		)      ,      reverse=lowerCAmelCase_      ,      key=relevance_logits.__getitem__		)
				UpperCAmelCase_	:					List[DPRReaderOutput]     	=   []
				for doc_id in sorted_docs:
							UpperCAmelCase_	:					Tuple     	=   list(input_ids[doc_id]		)
							# assuming question & title information is at the beginning of the sequence
							UpperCAmelCase_	:					List[str]     	=   sequence_ids.index(self.sep_token_id      ,      2		) + 1  # second sep id
							if sequence_ids[-1] == self.pad_token_id:
										UpperCAmelCase_	:					Optional[Any]     	=   sequence_ids.index(self.pad_token_id		)
							else:
										UpperCAmelCase_	:					Dict     	=   len(lowerCAmelCase_		)

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      )		->     List[DPRSpanPrediction]:
				UpperCAmelCase_	:					str     	=   []
				for start_index, start_score in enumerate(lowerCAmelCase_		):
							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)		)
				UpperCAmelCase_	:					Union[str, Any]     	=   sorted(lowerCAmelCase_      ,      key=lambda lowerCAmelCase_		: x[1]      ,      reverse=lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   []
				for (start_index, end_index), score in scores:
							assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
							UpperCAmelCase_	:					Any     	=   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(lowerCAmelCase_		) == top_spans:
										break
				return chosen_span_intervals







@add_end_docstrings(__A	)
class      UpperCamelCase_			(__A							,		__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							READER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							READER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							DPRReaderTokenizer











"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



