




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCamelCase	:		List[str]							       =   16
_lowerCamelCase	:		int							       =   32







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ = 16						)   ->       List[str]:



       """simple docstring"""

       A__       =    AutoTokenizer.from_pretrained('''bert-base-cased'''						)
       A__       =    DatasetDict(
           {
               '''train''': dataset['''train'''].select(lowercase_						),
               '''validation''': dataset['''train'''].select(lowercase_						),
               '''test''': dataset['''validation'''],
           }						)

       def tokenize_function(lowercase_						):
              # max_length=None => use the model max length (it's actually the default)
              A__       =    tokenizer(examples['''sentence1'''] ,				examples['''sentence2'''] ,				truncation=lowercase_ ,				max_length=lowercase_						)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              A__       =    datasets.map(
                  lowercase_ ,				batched=lowercase_ ,				remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,				)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A__       =    tokenized_datasets.rename_column('''label''' ,				'''labels'''						)

       def collate_fn(lowercase_						):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              A__       =    128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     A__       =    16
              elif accelerator.mixed_precision != "no":
                     A__       =    8
              else:
                     A__       =    None

              return tokenizer.pad(
                  lowercase_ ,				padding='''longest''' ,				max_length=lowercase_ ,				pad_to_multiple_of=lowercase_ ,				return_tensors='''pt''' ,				)

       # Instantiate dataloaders.
       A__       =    DataLoader(
           tokenized_datasets['''train'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)
       A__       =    DataLoader(
           tokenized_datasets['''validation'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)

       A__       =    DataLoader(
           tokenized_datasets['''test'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)

       return train_dataloader, eval_dataloader, test_dataloader







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    []
       # Download the dataset
       A__       =    load_dataset('''glue''' ,				'''mrpc'''						)
       # Create our splits
       A__       =    StratifiedKFold(n_splits=int(args.num_folds						)						)
       # Initialize accelerator
       A__       =    Accelerator(cpu=args.cpu ,				mixed_precision=args.mixed_precision						)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A__       =    config['''lr''']
       A__       =    int(config['''num_epochs''']						)
       A__       =    int(config['''seed''']						)
       A__       =    int(config['''batch_size''']						)

       A__       =    evaluate.load('''glue''' ,				'''mrpc'''						)

       # If the batch size is too big we use gradient accumulation
       A__       =    1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              A__       =    batch_size // MAX_GPU_BATCH_SIZE
              A__       =    MAX_GPU_BATCH_SIZE

       set_seed(lowercase_						)

       # New Code #
       # Create our folds:
       A__       =    kfold.split(np.zeros(datasets['''train'''].num_rows						) ,				datasets['''train''']['''label''']						)
       A__       =    []
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(lowercase_						):
              A__ ,							A__ ,							A__       =    get_fold_dataloaders(
                  lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				)
              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              A__       =    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,				return_dict=lowercase_						)

              # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
              # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
              # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
              A__       =    model.to(accelerator.device						)

              # Instantiate optimizer
              A__       =    AdamW(params=model.parameters() ,				lr=lowercase_						)

              # Instantiate scheduler
              A__       =    get_linear_schedule_with_warmup(
                  optimizer=lowercase_ ,				num_warmup_steps=100 ,				num_training_steps=(len(lowercase_						) * num_epochs) // gradient_accumulation_steps ,				)

              # Prepare everything
              # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
              # prepare method.
              A__ ,							A__ ,							A__ ,							A__ ,							A__       =    accelerator.prepare(
                  lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)

              # Now we train the model
              for epoch in range(lowercase_						):
                     model.train()
                     for step, batch in enumerate(lowercase_						):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device						)
                            A__       =    model(**lowercase_						)
                            A__       =    outputs.loss
                            A__       =    loss / gradient_accumulation_steps
                            accelerator.backward(lowercase_						)
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     model.eval()
                     for step, batch in enumerate(lowercase_						):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device						)
                            with torch.no_grad():
                                   A__       =    model(**lowercase_						)
                            A__       =    outputs.logits.argmax(dim=-1						)
                            A__ ,							A__       =    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                            metric.add_batch(
                                predictions=lowercase_ ,				references=lowercase_ ,				)

                     A__       =    metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(f"""epoch {epoch}:""" ,				lowercase_						)

              # New Code #
              # We also run predictions on the test set at the very end
              A__       =    []
              for step, batch in enumerate(lowercase_						):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device						)
                     with torch.no_grad():
                            A__       =    model(**lowercase_						)
                     A__       =    outputs.logits
                     A__ ,							A__       =    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                     fold_predictions.append(predictions.cpu()						)
                     if i == 0:
                            # We need all of the test predictions
                            test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
              test_predictions.append(torch.cat(lowercase_ ,				dim=0						)						)
              # We now need to release all our memory and get rid of the current model, optimizer, etc
              accelerator.free_memory()
       # New Code #
       # Finally we check the accuracy of our folded results:
       A__       =    torch.cat(lowercase_ ,				dim=0						)
       A__       =    torch.stack(lowercase_ ,				dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
       A__       =    metric.compute(predictions=lowercase_ ,				references=lowercase_						)
       accelerator.print('''Average test metrics from all folds:''' ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       )   ->       Tuple:



       """simple docstring"""

       A__       =    argparse.ArgumentParser(description='''Simple example of training script.'''						)
       parser.add_argument(
           '''--mixed_precision''' ,				type=lowercase_ ,				default=lowercase_ ,				choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,				help='''Whether to use mixed precision. Choose'''
           '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
           '''and an Nvidia Ampere GPU.''' ,				)
       parser.add_argument('''--cpu''' ,				action='''store_true''' ,				help='''If passed, will train on the CPU.'''						)
       # New Code #
       parser.add_argument('''--num_folds''' ,				type=lowercase_ ,				default=3 ,				help='''The number of splits to perform across the dataset'''						)
       A__       =    parser.parse_args()
       A__       =    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_ ,				lowercase_						)


if __name__ == "__main__":
  main()








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


