


import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


_SCREAMING_SNAKE_CASE     :			str        =							logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE     :			Union[str, Any]        =							tf.data.AUTOTUNE


def 							__lowerCAmelCase      (			):
 _lowercase:       int						=				argparse.ArgumentParser(description="Train a masked language model on TPU."      )
 parser.add_argument(
     "--pretrained_model_config" ,   type=_lowercase ,   default="roberta-base" ,   help="The model config to use. Note that we don\'t copy the model\'s weights, only the config!" ,   )
 parser.add_argument(
     "--tokenizer" ,   type=_lowercase ,   default="unigram-tokenizer-wikitext" ,   help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size." ,   )

 parser.add_argument(
     "--per_replica_batch_size" ,   type=_lowercase ,   default=8 ,   help="Batch size per TPU core." ,   )

 parser.add_argument(
     "--no_tpu" ,   action="store_true" ,   help="If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances." ,   )

 parser.add_argument(
     "--tpu_name" ,   type=_lowercase ,   help="Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs." ,   default="local" ,   )

 parser.add_argument(
     "--tpu_zone" ,   type=_lowercase ,   help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes." ,   )

 parser.add_argument(
     "--gcp_project" ,   type=_lowercase ,   help="Google cloud project name. Only used for non-Colab TPU nodes."      )

 parser.add_argument(
     "--bfloat16" ,   action="store_true" ,   help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU." ,   )

 parser.add_argument(
     "--train_dataset" ,   type=_lowercase ,   help="Path to training dataset to load. If the path begins with `gs://`"
     " then the dataset will be loaded from a Google Cloud Storage bucket." ,   )

 parser.add_argument(
     "--shuffle_buffer_size" ,   type=_lowercase ,   default=2**1_8 ,   help="Size of the shuffle buffer (in samples)" ,   )

 parser.add_argument(
     "--eval_dataset" ,   type=_lowercase ,   help="Path to evaluation dataset to load. If the path begins with `gs://`"
     " then the dataset will be loaded from a Google Cloud Storage bucket." ,   )

 parser.add_argument(
     "--num_epochs" ,   type=_lowercase ,   default=1 ,   help="Number of epochs to train for." ,   )

 parser.add_argument(
     "--learning_rate" ,   type=_lowercase ,   default=1e-4 ,   help="Learning rate to use for training." ,   )

 parser.add_argument(
     "--weight_decay_rate" ,   type=_lowercase ,   default=1e-3 ,   help="Weight decay rate to use for training." ,   )

 parser.add_argument(
     "--max_length" ,   type=_lowercase ,   default=5_1_2 ,   help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py" ,   )

 parser.add_argument(
     "--mlm_probability" ,   type=_lowercase ,   default=0.15 ,   help="Fraction of tokens to mask during training." ,   )

 parser.add_argument("--output_dir" ,   type=_lowercase ,   required=_lowercase ,   help="Path to save model checkpoints to."      )
 parser.add_argument("--hub_model_id" ,   type=_lowercase ,   help="Model ID to upload to on the Hugging Face Hub."      )

 _lowercase:       Optional[int]						=				parser.parse_args()
 return args


def 							__lowerCAmelCase      (			__magic_name__      ):


 try:
  if args.tpu_name:
   _lowercase:       List[Any]						=				tf.distribute.cluster_resolver.TPUClusterResolver(
       args.tpu_name ,   zone=args.tpu_zone ,   project=args.gcp_project      )
  else:
   _lowercase:       Optional[Any]						=				tf.distribute.cluster_resolver.TPUClusterResolver()
 except ValueError:
  raise RuntimeError(
      "Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
      "--gcp_project. When running on a TPU VM, use --tpu_name local."      )

 tf.config.experimental_connect_to_cluster(_lowercase      )
 tf.tpu.experimental.initialize_tpu_system(_lowercase      )

 return tpu


def 							__lowerCAmelCase      (			__magic_name__      ):
 _lowercase:       List[str]						=				0
 for file in file_list:
  _lowercase:       Optional[int]						=				file.split("/"      )[-1]
  _lowercase:       Tuple						=				re.search(R"-\d+-(\d+)\.tfrecord" ,   _lowercase      ).group(1      )
  _lowercase:       Dict						=				int(_lowercase      )
  num_samples += sample_count

 return num_samples


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__=None      ):
 _lowercase:       Tuple						=				count_samples(_lowercase      )
 _lowercase:       List[Any]						=				tf.data.Dataset.from_tensor_slices(_lowercase      )
 if shuffle:
  _lowercase:       List[Any]						=				dataset.shuffle(len(_lowercase      )      )
 _lowercase:       Any						=				tf.data.TFRecordDataset(_lowercase ,   num_parallel_reads=_lowercase      )
 # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
 _lowercase:       Any						=				dataset.apply(tf.data.experimental.assert_cardinality(_lowercase      )      )
 _lowercase:       List[Any]						=				dataset.map(_lowercase ,   num_parallel_calls=_lowercase      )
 if shuffle:
  assert shuffle_buffer_size is not None
  _lowercase:       Tuple						=				dataset.shuffle(args.shuffle_buffer_size      )
 _lowercase:       Optional[Any]						=				dataset.batch(_lowercase ,   drop_remainder=_lowercase      )
 _lowercase:       Optional[Any]						=				dataset.map(_lowercase ,   num_parallel_calls=_lowercase      )
 _lowercase:       Tuple						=				dataset.prefetch(_lowercase      )
 return dataset


def 							__lowerCAmelCase      (			__magic_name__      ):


 if not args.no_tpu:
  _lowercase:       List[Any]						=				initialize_tpu(_lowercase      )
  _lowercase:       Optional[int]						=				tf.distribute.TPUStrategy(_lowercase      )
 else:
  _lowercase:       Dict						=				tf.distribute.OneDeviceStrategy(device="/gpu:0"      )

 if args.bfloataa:
  tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"      )

 _lowercase:       str						=				AutoTokenizer.from_pretrained(args.tokenizer      )
 _lowercase:       List[str]						=				AutoConfig.from_pretrained(args.pretrained_model_config      )
 _lowercase:       int						=				tokenizer.vocab_size

 _lowercase:       Union[str, Any]						=				tf.io.gfile.glob(os.path.join(args.train_dataset ,   "*.tfrecord"      )      )
 if not training_records:
  raise ValueError(f"No .tfrecord files found in {args.train_dataset}."      )
 _lowercase:       Tuple						=				tf.io.gfile.glob(os.path.join(args.eval_dataset ,   "*.tfrecord"      )      )
 if not eval_records:
  raise ValueError(f"No .tfrecord files found in {args.eval_dataset}."      )

 _lowercase:       Dict						=				count_samples(_lowercase      )

 _lowercase:       Any						=				num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
 _lowercase:       Optional[int]						=				steps_per_epoch * args.num_epochs

 with strategy.scope():
  _lowercase:       Any						=				TFAutoModelForMaskedLM.from_config(_lowercase      )
  model(model.dummy_inputs      )  # Pass some dummy inputs through the model to ensure all the weights are built
  _lowercase:       Any						=				create_optimizer(
      num_train_steps=_lowercase ,   num_warmup_steps=total_train_steps // 2_0 ,   init_lr=args.learning_rate ,   weight_decay_rate=args.weight_decay_rate ,   )

  # Transformers models compute the right loss for their task by default when labels are passed, and will
  # use this for training unless you specify your own loss function in compile().
  model.compile(optimizer=_lowercase ,   metrics=["accuracy"]      )

 def decode_fn(__magic_name__      ):
  _lowercase:       List[Any]						=				{
      '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa ,   shape=(args.max_length,)      ),
      '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa ,   shape=(args.max_length,)      ),
  }
  return tf.io.parse_single_example(_lowercase ,   _lowercase      )

 # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
 # use their methods in our data pipeline.
 _lowercase:       str						=				DataCollatorForLanguageModeling(
     tokenizer=_lowercase ,   mlm_probability=args.mlm_probability ,   mlm=_lowercase ,   return_tensors="tf"      )

 def mask_with_collator(__magic_name__      ):
  # TF really needs an isin() function
  _lowercase:       Dict						=				(
      ~tf.cast(batch["attention_mask"] ,   tf.bool      )
      | (batch['''input_ids'''] == tokenizer.cls_token_id)
      | (batch['''input_ids'''] == tokenizer.sep_token_id)
  )
  _lowercase:       List[Any]						=				data_collator.tf_mask_tokens(
      batch["input_ids"] ,   vocab_size=len(_lowercase      ) ,   mask_token_id=tokenizer.mask_token_id ,   special_tokens_mask=_lowercase ,   )
  return batch

 _lowercase:       Any						=				args.per_replica_batch_size * strategy.num_replicas_in_sync

 _lowercase:       Any						=				prepare_dataset(
     _lowercase ,   decode_fn=_lowercase ,   mask_fn=_lowercase ,   batch_size=_lowercase ,   shuffle=_lowercase ,   shuffle_buffer_size=args.shuffle_buffer_size ,   )

 _lowercase:       Dict						=				prepare_dataset(
     _lowercase ,   decode_fn=_lowercase ,   mask_fn=_lowercase ,   batch_size=_lowercase ,   shuffle=_lowercase ,   )

 _lowercase:       Optional[Any]						=				[]
 if args.hub_model_id:
  callbacks.append(
      PushToHubCallback(output_dir=args.output_dir ,   hub_model_id=args.hub_model_id ,   tokenizer=_lowercase      )      )

 model.fit(
     _lowercase ,   validation_data=_lowercase ,   epochs=args.num_epochs ,   callbacks=_lowercase ,   )

 model.save_pretrained(args.output_dir      )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			List[str]        =							parse_args()
       main(args)







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     __a(	_a  ):






    """simple docstring"""





    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     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = 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(_a  )
class     __a:






    """simple docstring"""





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





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

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						List[DPRSpanPrediction]:
         UpperCAmelCase_     :				Tuple  =     []
         for start_index, start_score in enumerate(_SCREAMING_SNAKE_CASE    ):
              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_     :				int  =     sorted(_SCREAMING_SNAKE_CASE  ,key=lambda _SCREAMING_SNAKE_CASE    : x[1]  ,reverse=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     []
         for (start_index, end_index), score in scores:
              if start_index > end_index:
                   raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''    )
              UpperCAmelCase_     :				str  =     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(_SCREAMING_SNAKE_CASE    ) == top_spans:
                   break
         return chosen_span_intervals


@add_end_docstrings(_a  )
class     __a(	_a							,		_a  ):






    """simple docstring"""





    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''']