


import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    _lowercase			:				Optional[int] 						=						fname.split(os.path.sep					)[-1]
    return re.search(R'^(.*)_\d+\.jpg$'				,					lowerCamelCase_					).groups()[0]






class _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None)			->  List[str]:




           """simple docstring"""

           _lowercase			:				int 						=						file_names
           _lowercase			:				List[str] 						=						image_transform
           _lowercase			:				List[Any] 						=						label_to_id
       def __len__(   self)			->  str:




           """simple docstring"""

           return len(self.file_names)




       def __getitem__(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.file_names[idx]
           _lowercase			:				Tuple 						=						PIL.Image.open(lowerCamelCase)
           _lowercase			:				Any 						=						raw_image.convert('RGB')
           if self.image_transform is not None:
               _lowercase			:				Dict 						=						self.image_transform(lowerCamelCase)
           _lowercase			:				Dict 						=						extract_label(lowerCamelCase)
           if self.label_to_id is not None:
               _lowercase			:				List[Any] 						=						self.label_to_id[label]
           return {"image": image, "label": label}



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Union[str, Any]:
    # Initialize accelerator
    if args.with_tracking:
        _lowercase			:				int 						=						Accelerator(
            cpu=args.cpu				,					mixed_precision=args.mixed_precision				,					log_with='all'				,					project_dir=args.project_dir					)
    else:
        _lowercase			:				Tuple 						=						Accelerator(cpu=args.cpu				,					mixed_precision=args.mixed_precision					)

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase			:				List[str] 						=						config['lr']
    _lowercase			:				Optional[int] 						=						int(config['num_epochs']					)
    _lowercase			:				Union[str, Any] 						=						int(config['seed']					)
    _lowercase			:				Dict 						=						int(config['batch_size']					)
    _lowercase			:				List[str] 						=						config['image_size']
    if not isinstance(lowerCamelCase_				,					(list, tuple)					):
        _lowercase			:				List[str] 						=						(image_size, image_size)

    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps				,					'isdigit'					):
        if args.checkpointing_steps == "epoch":
            _lowercase			:				Optional[Any] 						=						args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            _lowercase			:				int 						=						int(args.checkpointing_steps					)
        else:
            raise ValueError(
                F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
    else:
        _lowercase			:				Union[str, Any] 						=						None

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        _lowercase			:				Tuple 						=						os.path.split(lowerCamelCase_					)[-1].split('.'					)[0]
        accelerator.init_trackers(lowerCamelCase_				,					lowerCamelCase_					)

    # Grab all the image filenames
    _lowercase			:				str 						=						[os.path.join(args.data_dir				,					lowerCamelCase_					) for fname in os.listdir(args.data_dir					) if fname.endswith('.jpg'					)]

    # Build the label correspondences
    _lowercase			:				List[str] 						=						[extract_label(lowerCamelCase_					) for fname in file_names]
    _lowercase			:				Dict 						=						list(set(lowerCamelCase_					)					)
    id_to_label.sort()
    _lowercase			:				Optional[int] 						=						{lbl: i for i, lbl in enumerate(lowerCamelCase_					)}

    # Set the seed before splitting the data.
    np.random.seed(lowerCamelCase_					)
    torch.manual_seed(lowerCamelCase_					)
    torch.cuda.manual_seed_all(lowerCamelCase_					)

    # Split our filenames between train and validation
    _lowercase			:				Optional[Any] 						=						np.random.permutation(len(lowerCamelCase_					)					)
    _lowercase			:				str 						=						int(0.8 * len(lowerCamelCase_					)					)
    _lowercase			:				Any 						=						random_perm[:cut]
    _lowercase			:				Optional[int] 						=						random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    _lowercase			:				Optional[int] 						=						Compose([RandomResizedCrop(lowerCamelCase_				,					scale=(0.5, 1.0)					), ToTensor()]					)
    _lowercase			:				Optional[Any] 						=						PetsDataset(
        [file_names[i] for i in train_split]				,					image_transform=lowerCamelCase_				,					label_to_id=lowerCamelCase_					)

    # For evaluation, we use a deterministic Resize
    _lowercase			:				Union[str, Any] 						=						Compose([Resize(lowerCamelCase_					), ToTensor()]					)
    _lowercase			:				Optional[Any] 						=						PetsDataset([file_names[i] for i in eval_split]				,					image_transform=lowerCamelCase_				,					label_to_id=lowerCamelCase_					)

    # Instantiate dataloaders.
    _lowercase			:				List[str] 						=						DataLoader(lowerCamelCase_				,					shuffle=lowerCamelCase_				,					batch_size=lowerCamelCase_				,					num_workers=4					)
    _lowercase			:				List[Any] 						=						DataLoader(lowerCamelCase_				,					shuffle=lowerCamelCase_				,					batch_size=lowerCamelCase_				,					num_workers=4					)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase			:				List[str] 						=						create_model('resnet50d'				,					pretrained=lowerCamelCase_				,					num_classes=len(lowerCamelCase_					)					)

    # 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).
    _lowercase			:				Union[str, Any] 						=						model.to(accelerator.device					)

    # Freezing the base model
    for param in model.parameters():
        _lowercase			:				Dict 						=						False
    for param in model.get_classifier().parameters():
        _lowercase			:				Any 						=						True

    # We normalize the batches of images to be a bit faster.
    _lowercase			:				int 						=						torch.tensor(model.default_cfg['mean']					)[None, :, None, None].to(accelerator.device					)
    _lowercase			:				Union[str, Any] 						=						torch.tensor(model.default_cfg['std']					)[None, :, None, None].to(accelerator.device					)

    # Instantiate optimizer
    _lowercase			:				int 						=						torch.optim.Adam(params=model.parameters()				,					lr=lr / 25					)

    # Instantiate learning rate scheduler
    _lowercase			:				List[Any] 						=						OneCycleLR(optimizer=lowerCamelCase_				,					max_lr=lowerCamelCase_				,					epochs=lowerCamelCase_				,					steps_per_epoch=len(lowerCamelCase_					)					)

    # 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.
    _lowercase						,							_lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						accelerator.prepare(
        lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    # We need to keep track of how many total steps we have iterated over
    _lowercase			:				Optional[int] 						=						0
    # We also need to keep track of the starting epoch so files are named properly
    _lowercase			:				List[Any] 						=						0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''					)
            accelerator.load_state(args.resume_from_checkpoint					)
            _lowercase			:				Optional[Any] 						=						os.path.basename(args.resume_from_checkpoint					)
        else:
            # Get the most recent checkpoint
            _lowercase			:				List[str] 						=						[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
            dirs.sort(key=os.path.getctime					)
            _lowercase			:				str 						=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        _lowercase			:				Union[str, Any] 						=						os.path.splitext(lowerCamelCase_					)[0]

        if "epoch" in training_difference:
            _lowercase			:				Any 						=						int(training_difference.replace('epoch_'				,					''					)					) + 1
            _lowercase			:				Optional[Any] 						=						None
        else:
            _lowercase			:				Dict 						=						int(training_difference.replace('step_'				,					''					)					)
            _lowercase			:				Any 						=						resume_step // len(lowerCamelCase_					)
            resume_step -= starting_epoch * len(lowerCamelCase_					)

    # Now we train the model
    for epoch in range(lowerCamelCase_				,					lowerCamelCase_					):
        model.train()
        if args.with_tracking:
            _lowercase			:				Tuple 						=						0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            _lowercase			:				Union[str, Any] 						=						accelerator.skip_first_batches(lowerCamelCase_				,					lowerCamelCase_					)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            _lowercase			:				Any 						=						train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase			:				Dict 						=						{k: v.to(accelerator.device					) for k, v in batch.items()}
            _lowercase			:				List[Any] 						=						(batch['image'] - mean) / std
            _lowercase			:				Optional[int] 						=						model(lowerCamelCase_					)
            _lowercase			:				Union[str, Any] 						=						torch.nn.functional.cross_entropy(lowerCamelCase_				,					batch['label']					)
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(lowerCamelCase_					)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(lowerCamelCase_				,					lowerCamelCase_					):
                _lowercase			:				Tuple 						=						F'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        _lowercase			:				Tuple 						=						os.path.join(args.output_dir				,					lowerCamelCase_					)
                    accelerator.save_state(lowerCamelCase_					)
        model.eval()
        _lowercase			:				List[str] 						=						0
        _lowercase			:				List[Any] 						=						0
        for step, batch in enumerate(lowerCamelCase_					):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase			:				Union[str, Any] 						=						{k: v.to(accelerator.device					) for k, v in batch.items()}
            _lowercase			:				List[Any] 						=						(batch['image'] - mean) / std
            with torch.no_grad():
                _lowercase			:				Optional[Any] 						=						model(lowerCamelCase_					)
            _lowercase			:				Any 						=						outputs.argmax(dim=-1					)
            _lowercase						,							_lowercase			:				Optional[int] 						=						accelerator.gather_for_metrics((predictions, batch['label'])					)
            _lowercase			:				int 						=						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        _lowercase			:				Union[str, Any] 						=						accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''					)
        if args.with_tracking:
            accelerator.log(
                {
                    'accuracy': 100 * eval_metric,
                    'train_loss': total_loss.item() / len(lowerCamelCase_					),
                    'epoch': epoch,
                }				,					step=lowerCamelCase_				,					)
        if checkpointing_steps == "epoch":
            _lowercase			:				Any 						=						F'''epoch_{epoch}'''
            if args.output_dir is not None:
                _lowercase			:				Dict 						=						os.path.join(args.output_dir				,					lowerCamelCase_					)
            accelerator.save_state(lowerCamelCase_					)

    if args.with_tracking:
        accelerator.end_training()



def 							UpperCamelCase_( )       ->		Optional[Any]:
    _lowercase			:				Tuple 						=						argparse.ArgumentParser(description='Simple example of training script.'					)
    parser.add_argument('--data_dir'				,					required=lowerCamelCase_				,					help='The data folder on disk.'					)
    parser.add_argument('--fp16'				,					action='store_true'				,					help='If passed, will use FP16 training.'					)
    parser.add_argument(
        '--mixed_precision'				,					type=lowerCamelCase_				,					default=lowerCamelCase_				,					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.'					)
    parser.add_argument(
        '--checkpointing_steps'				,					type=lowerCamelCase_				,					default=lowerCamelCase_				,					help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'				,					)
    parser.add_argument(
        '--output_dir'				,					type=lowerCamelCase_				,					default='.'				,					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'				,					)
    parser.add_argument(
        '--resume_from_checkpoint'				,					type=lowerCamelCase_				,					default=lowerCamelCase_				,					help='If the training should continue from a checkpoint folder.'				,					)
    parser.add_argument(
        '--with_tracking'				,					action='store_true'				,					help='Whether to load in all available experiment trackers from the environment and use them for logging.'				,					)
    parser.add_argument(
        '--project_dir'				,					type=lowerCamelCase_				,					default='logs'				,					help='Location on where to store experiment tracking logs` and relevent project information'				,					)
    _lowercase			:				str 						=						parser.parse_args()
    _lowercase			:				List[str] 						=						{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
    training_function(lowerCamelCase_				,					lowerCamelCase_					)


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 import BertTokenizer


__a:     Tuple   					=					logging.get_logger(__name__)

__a:     Optional[Any]   					=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__a:     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'''
        ),
    },
}
__a:     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'''
        ),
    },
}
__a:     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'''
        ),
    },
}

__a:     Dict   					=					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__a:     List[str]   					=					{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
__a:     Dict   					=					{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


__a:     Optional[int]   					=					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a:     Tuple   					=					{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a:     Optional[int]   					=					{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

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

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


__a:     Optional[Any]   					=					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)

    Returns:
        `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 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __call__(      self		:			int       ,				lowerCamelCase		:			int       ,				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		:			Optional[int]       ,				)						->	BatchEncoding:


         """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:
                _UpperCAmelCase							=    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							=    titles if not isinstance(lowerCamelCase       ,				lowerCamelCase							) else [titles]
         _UpperCAmelCase							=    texts if not isinstance(lowerCamelCase       ,				lowerCamelCase							) else [texts]
         _UpperCAmelCase							=    len(lowerCamelCase							)
         _UpperCAmelCase							=    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."""							)
         _UpperCAmelCase							=    super().__call__(lowerCamelCase       ,				lowerCamelCase       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase							)["""input_ids"""]
         _UpperCAmelCase							=    super().__call__(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase							)["""input_ids"""]
         _UpperCAmelCase							=    {
             """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							=    []
                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							=    attention_mask
         return self.pad(lowerCamelCase       ,				padding=lowerCamelCase       ,				max_length=lowerCamelCase       ,				return_tensors=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			BatchEncoding       ,				lowerCamelCase		:			DPRReaderOutput       ,				lowerCamelCase		:			int = 16       ,				lowerCamelCase		:			int = 64       ,				lowerCamelCase		:			int = 4       ,				)						->	List[DPRSpanPrediction]:


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

                _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int       ,				)						->	List[DPRSpanPrediction]:


         """simple docstring"""
         _UpperCAmelCase							=    []
         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							=    sorted(lowerCamelCase       ,				key=lambda lowerCamelCase							: x[1]       ,				reverse=lowerCamelCase							)
         _UpperCAmelCase							=    []
         for (start_index, end_index), score in scores:
                if start_index > end_index:
                       raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
                _UpperCAmelCase							=    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     ):

  '''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''']