



"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase						:		Optional[int]			= logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")


@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default="cifar10"      ,  metadata={"help": "Name of a dataset from the datasets package"}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "The column name of the images in the files."}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "A folder containing the training data."}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "A folder containing the validation data."}   )
    lowercase__      =   field(
        default=0.15      ,  metadata={"help": "Percent to split off of train for validation."}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }      ,  )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }      ,  )

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= {}
      if self.train_dir is not None:
        lowercase_								= self.train_dir
      if self.validation_dir is not None:
        lowercase_								= self.validation_dir
      lowercase_								= data_files if data_files else None







@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={
            "help": (
                "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
            )
        }      ,  )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        }      ,  )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}   )
    lowercase__      =   field(
        default="main"      ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}      ,  )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "Name or path of preprocessor config."}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }      ,  )
    lowercase__      =   field(
        default=0.75      ,  metadata={"help": "The ratio of the number of masked tokens in the input sequence."}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Whether or not to train with normalized pixel values as target."}   )







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   field(
        default=1e-3      ,  metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}   )







def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  lowercase_								= torch.stack([example["""pixel_values"""] for example in examples]							)
  return {"pixel_values": pixel_values}


def 			_SCREAMING_SNAKE_CASE						() ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
  if len(sys.argv							) == 2 and sys.argv[1].endswith(""".json"""							):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    lowercase_					,    lowercase_					,    lowercase_								= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
  else:
    lowercase_					,    lowercase_					,    lowercase_								= parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("""run_mae""" ,  __lowerCAmelCase ,  __lowerCAmelCase							)

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,  datefmt="""%m/%d/%Y %H:%M:%S""" ,  handlers=[logging.StreamHandler(sys.stdout							)] ,  )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  lowercase_								= training_args.get_process_log_level()
  logger.setLevel(__lowerCAmelCase							)
  transformers.utils.logging.set_verbosity(__lowerCAmelCase							)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
      + F'''distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}'''							)
  logger.info(F'''Training/evaluation parameters {training_args}'''							)

  # Detecting last checkpoint.
  lowercase_								= None
  if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
    lowercase_								= get_last_checkpoint(training_args.output_dir							)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
      raise ValueError(
          F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
          """Use --overwrite_output_dir to overcome."""							)
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
          """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""							)

    # Initialize our dataset.
  lowercase_								= load_dataset(
      data_args.dataset_name ,  data_args.dataset_config_name ,  data_files=data_args.data_files ,  cache_dir=model_args.cache_dir ,  use_auth_token=True if model_args.use_auth_token else None ,  )

  # If we don't have a validation split, split off a percentage of train as validation.
  lowercase_								= None if """validation""" in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split ,  __lowerCAmelCase							) and data_args.train_val_split > 0.0:
    lowercase_								= ds["""train"""].train_test_split(data_args.train_val_split							)
    lowercase_								= split["""train"""]
    lowercase_								= split["""test"""]

  # Load pretrained model and image processor
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowercase_								= {
      """cache_dir""": model_args.cache_dir,
      """revision""": model_args.model_revision,
      """use_auth_token""": True if model_args.use_auth_token else None,
  }
  if model_args.config_name:
    lowercase_								= ViTMAEConfig.from_pretrained(model_args.config_name ,  **__lowerCAmelCase							)
  elif model_args.model_name_or_path:
    lowercase_								= ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,  **__lowerCAmelCase							)
  else:
    lowercase_								= ViTMAEConfig()
    logger.warning("""You are instantiating a new config instance from scratch."""							)
    if model_args.config_overrides is not None:
      logger.info(F'''Overriding config: {model_args.config_overrides}'''							)
      config.update_from_string(model_args.config_overrides							)
      logger.info(F'''New config: {config}'''							)

    # adapt config
  config.update(
      {
          """mask_ratio""": model_args.mask_ratio,
          """norm_pix_loss""": model_args.norm_pix_loss,
      }							)

  # create image processor
  if model_args.image_processor_name:
    lowercase_								= ViTImageProcessor.from_pretrained(model_args.image_processor_name ,  **__lowerCAmelCase							)
  elif model_args.model_name_or_path:
    lowercase_								= ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,  **__lowerCAmelCase							)
  else:
    lowercase_								= ViTImageProcessor()

  # create model
  if model_args.model_name_or_path:
    lowercase_								= ViTMAEForPreTraining.from_pretrained(
        model_args.model_name_or_path ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path							) ,  config=__lowerCAmelCase ,  cache_dir=model_args.cache_dir ,  revision=model_args.model_revision ,  use_auth_token=True if model_args.use_auth_token else None ,  )
  else:
    logger.info("""Training new model from scratch"""							)
    lowercase_								= ViTMAEForPreTraining(__lowerCAmelCase							)

  if training_args.do_train:
    lowercase_								= ds["""train"""].column_names
  else:
    lowercase_								= ds["""validation"""].column_names

  if data_args.image_column_name is not None:
    lowercase_								= data_args.image_column_name
  elif "image" in column_names:
    lowercase_								= """image"""
  elif "img" in column_names:
    lowercase_								= """img"""
  else:
    lowercase_								= column_names[0]

  # transformations as done in original MAE paper
  # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
  if "shortest_edge" in image_processor.size:
    lowercase_								= image_processor.size["""shortest_edge"""]
  else:
    lowercase_								= (image_processor.size["""height"""], image_processor.size["""width"""])
  lowercase_								= Compose(
      [
          Lambda(lambda __lowerCAmelCase							: img.convert("""RGB"""							) if img.mode != "RGB" else img							),
          RandomResizedCrop(__lowerCAmelCase ,  scale=(0.2, 1.0) ,  interpolation=InterpolationMode.BICUBIC							),
          RandomHorizontalFlip(),
          ToTensor(),
          Normalize(mean=image_processor.image_mean ,  std=image_processor.image_std							),
      ]							)

  def preprocess_images(__lowerCAmelCase							):
    lowercase_								= [transforms(__lowerCAmelCase							) for image in examples[image_column_name]]
    return examples

  if training_args.do_train:
    if "train" not in ds:
      raise ValueError("""--do_train requires a train dataset"""							)
    if data_args.max_train_samples is not None:
      lowercase_								= ds["""train"""].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
    # Set the training transforms
    ds["train"].set_transform(__lowerCAmelCase							)

  if training_args.do_eval:
    if "validation" not in ds:
      raise ValueError("""--do_eval requires a validation dataset"""							)
    if data_args.max_eval_samples is not None:
      lowercase_								= (
          ds["""validation"""].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
      )
    # Set the validation transforms
    ds["validation"].set_transform(__lowerCAmelCase							)

  # Compute absolute learning rate
  lowercase_								= (
      training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
  )
  if training_args.base_learning_rate is not None:
    lowercase_								= training_args.base_learning_rate * total_train_batch_size / 2_56

  # Initialize our trainer
  lowercase_								= Trainer(
      model=__lowerCAmelCase ,  args=__lowerCAmelCase ,  train_dataset=ds["""train"""] if training_args.do_train else None ,  eval_dataset=ds["""validation"""] if training_args.do_eval else None ,  tokenizer=__lowerCAmelCase ,  data_collator=__lowerCAmelCase ,  )

  # Training
  if training_args.do_train:
    lowercase_								= None
    if training_args.resume_from_checkpoint is not None:
      lowercase_								= training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowercase_								= last_checkpoint
    lowercase_								= trainer.train(resume_from_checkpoint=__lowerCAmelCase							)
    trainer.save_model()
    trainer.log_metrics("""train""" ,  train_result.metrics							)
    trainer.save_metrics("""train""" ,  train_result.metrics							)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    lowercase_								= trainer.evaluate()
    trainer.log_metrics("""eval""" ,  __lowerCAmelCase							)
    trainer.save_metrics("""eval""" ,  __lowerCAmelCase							)

  # Write model card and (optionally) push to hub
  lowercase_								= {
      """tasks""": """masked-auto-encoding""",
      """dataset""": data_args.dataset_name,
      """tags""": ["""masked-auto-encoding"""],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**__lowerCAmelCase							)
  else:
    trainer.create_model_card(**__lowerCAmelCase							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  main()


if __name__ == "__main__":
       main()






"""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 import BertTokenizer


UpperCAmelCase						:		Any			= logging.get_logger(__name__)

UpperCAmelCase						:		Optional[int]			= {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase						:		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"
        ),
    },
}
UpperCAmelCase						:		Any			= {
    "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"
        ),
    },
}
UpperCAmelCase						:		Optional[int]			= {
    "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"
        ),
    },
}

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


UpperCAmelCase						:		int			= {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase						:		Union[str, Any]			= {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase						:		List[str]			= {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







UpperCAmelCase						:		Union[str, Any]			= collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

UpperCAmelCase						:		Optional[int]			= collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCAmelCase						:		Any			= 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(__UpperCAmelCase   )
class    SCREAMING_SNAKE_CASE__ :

    def __call__(				self			:				Any ,  lowerCAmelCase_			:				str ,  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_			:				List[str] ,  ):







      """simple docstring"""







      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:
        lowercase_								= 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_ ,  )
      lowercase_								= titles if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_) else [titles]
      lowercase_								= texts if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_) else [texts]
      lowercase_								= len(lowerCAmelCase_)
      lowercase_								= questions if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_) else [questions] * n_passages
      if len(lowerCAmelCase_) != len(lowerCAmelCase_):
        raise ValueError(
            F'''There should be as many titles than texts but got {len(lowerCAmelCase_)} titles and {len(lowerCAmelCase_)} texts.''')
      lowercase_								= super().__call__(lowerCAmelCase_ ,  lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_)["""input_ids"""]
      lowercase_								= super().__call__(lowerCAmelCase_ ,  add_special_tokens=lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_)["""input_ids"""]
      lowercase_								= {
          """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:
        lowercase_								= []
        for input_ids in encoded_inputs["input_ids"]:
          attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
        lowercase_								= attention_mask
      return self.pad(lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				BatchEncoding ,  lowerCAmelCase_			:				DPRReaderOutput ,  lowerCAmelCase_			:				int = 1_6 ,  lowerCAmelCase_			:				int = 6_4 ,  lowerCAmelCase_			:				int = 4 ,  ):







      """simple docstring"""







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

        lowercase_								= 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 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				int ,  ):







      """simple docstring"""







      lowercase_								= []
      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))
      lowercase_								= sorted(lowerCAmelCase_ ,  key=lambda lowerCAmelCase_: x[1] ,  reverse=lowerCAmelCase_)
      lowercase_								= []
      for (start_index, end_index), score in scores:
        if start_index > end_index:
          raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''')
        lowercase_								= 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(lowerCAmelCase_) == top_spans:
          break
      return chosen_span_intervals







@add_end_docstrings(__UpperCAmelCase   )
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   READER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   READER_PRETRAINED_INIT_CONFIGURATION
    lowercase__      =   ["input_ids", "attention_mask"]

