import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMAEForPreTraining, ViTMAEModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor
class     a_			:






 '''simple docstring'''






 def __init__(      self :  Any      ,   lowercase__ :  Tuple      ,   lowercase__ :  Tuple=13      ,   lowercase__ :  Optional[int]=30      ,   lowercase__ :  List[str]=2      ,   lowercase__ :  Dict=3      ,   lowercase__ :  Optional[Any]=True      ,   lowercase__ :  List[str]=True      ,   lowercase__ :  str=32      ,   lowercase__ :  Union[str, Any]=5      ,   lowercase__ :  Optional[int]=4      ,   lowercase__ :  Union[str, Any]=37      ,   lowercase__ :  Tuple="gelu"      ,   lowercase__ :  List[str]=0.1      ,   lowercase__ :  Optional[int]=0.1      ,   lowercase__ :  str=10      ,   lowercase__ :  int=0.02      ,   lowercase__ :  Union[str, Any]=3      ,   lowercase__ :  int=0.6      ,   lowercase__ :  Optional[Any]=None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = image_size
        lowerCAmelCase__					     = patch_size
        lowerCAmelCase__					     = num_channels
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = intermediate_size
        lowerCAmelCase__					     = hidden_act
        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = type_sequence_label_size
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = mask_ratio
        lowerCAmelCase__					     = scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        lowerCAmelCase__					     = (image_size // patch_size) ** 2
        lowerCAmelCase__					     = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        lowerCAmelCase__					     = None
        if self.use_labels:
               lowerCAmelCase__					     = ids_tensor([self.batch_size]      ,   self.type_sequence_label_size)

        lowerCAmelCase__					     = self.get_config()

        return config, pixel_values, labels




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        return ViTMAEConfig(
            image_size=self.image_size      ,   patch_size=self.patch_size      ,   num_channels=self.num_channels      ,   hidden_size=self.hidden_size      ,   num_hidden_layers=self.num_hidden_layers      ,   num_attention_heads=self.num_attention_heads      ,   intermediate_size=self.intermediate_size      ,   hidden_act=self.hidden_act      ,   hidden_dropout_prob=self.hidden_dropout_prob      ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,   is_decoder=lowercase__      ,   initializer_range=self.initializer_range      ,   mask_ratio=self.mask_ratio      ,   )




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Any      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = ViTMAEModel(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(lowercase__)
        self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size))




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = ViTMAEForPreTraining(lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(lowercase__)
        lowerCAmelCase__					     = (self.image_size // self.patch_size) ** 2
        lowerCAmelCase__					     = self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, num_patches, expected_num_channels))

        # test greyscale images
        lowerCAmelCase__					     = 1
        lowerCAmelCase__					     = ViTMAEForPreTraining(lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        lowerCAmelCase__					     = model(lowercase__)
        lowerCAmelCase__					     = self.patch_size**2
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, num_patches, expected_num_channels))




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.prepare_config_and_inputs()
        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = config_and_inputs
        lowerCAmelCase__					     = {'pixel_values': pixel_values}
        return config, inputs_dict






@require_torch
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 UpperCAmelCase_     			= {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = ViTMAEModelTester(self)
        lowerCAmelCase__					     = ConfigTester(self      ,   config_class=lowercase__      ,   has_text_modality=lowercase__      ,   hidden_size=37)




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 @unittest.skip(reason='ViTMAE does not use inputs_embeds')
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__					     = model_class(lowercase__)
               self.assertIsInstance(model.get_input_embeddings()      ,   (nn.Module))
               lowerCAmelCase__					     = model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowercase__      ,   nn.Linear))




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__					     = model_class(lowercase__)
               lowerCAmelCase__					     = inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowerCAmelCase__					     = [*signature.parameters.keys()]

               lowerCAmelCase__					     = ['pixel_values']
               self.assertListEqual(arg_names[:1]      ,   lowercase__)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowercase__)




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Dict):







        '''simple docstring'''



        np.random.seed(2)

        lowerCAmelCase__					     = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
        lowerCAmelCase__					     = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
        lowerCAmelCase__					     = torch.from_numpy(lowercase__)

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        lowerCAmelCase__					     = pt_noise

        super().check_pt_tf_models(lowercase__      ,   lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__					     = model_class(lowercase__)
               model.to(lowercase__)
               model.eval()
               # make random mask reproducible
               torch.manual_seed(2)
               with torch.no_grad():
                      lowerCAmelCase__					     = model(**self._prepare_for_class(lowercase__      ,   lowercase__))

               lowerCAmelCase__					     = outputs[0].cpu().numpy()
               lowerCAmelCase__					     = 0

               with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(lowercase__)
                      lowerCAmelCase__					     = model_class.from_pretrained(lowercase__)
                      model.to(lowercase__)
                      # make random mask reproducible
                      torch.manual_seed(2)
                      with torch.no_grad():
                             lowerCAmelCase__					     = model(**self._prepare_for_class(lowercase__      ,   lowercase__))

                      # Make sure we don't have nans
                      lowerCAmelCase__					     = after_outputs[0].cpu().numpy()
                      lowerCAmelCase__					     = 0
                      lowerCAmelCase__					     = np.amax(np.abs(out_a - out_a))
                      self.assertLessEqual(lowercase__      ,   1e-5)




 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        pass




 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        pass




 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        pass




 @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        pass




 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        pass




 @slow
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase__					     = ViTMAEModel.from_pretrained(lowercase__)
               self.assertIsNotNone(lowercase__)






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image



@require_torch
@require_vision
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 @cached_property
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None




 @slow
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        np.random.seed(2)

        lowerCAmelCase__					     = ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowercase__)

        lowerCAmelCase__					     = self.default_image_processor
        lowerCAmelCase__					     = prepare_img()
        lowerCAmelCase__					     = image_processor(images=lowercase__      ,   return_tensors='pt').to(lowercase__)

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        lowerCAmelCase__					     = ViTMAEConfig()
        lowerCAmelCase__					     = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
        lowerCAmelCase__					     = np.random.uniform(size=(1, num_patches))

        # forward pass
        with torch.no_grad():
               lowerCAmelCase__					     = model(**lowercase__      ,   noise=torch.from_numpy(lowercase__).to(device=lowercase__))

        # verify the logits
        lowerCAmelCase__					     = torch.Size((1, 196, 768))
        self.assertEqual(outputs.logits.shape      ,   lowercase__)

        lowerCAmelCase__					     = torch.tensor(
            [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]])

        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,   expected_slice.to(lowercase__)      ,   atol=1e-4))

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


lowerCAmelCase__     						=			logging.get_logger(__name__)

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

lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

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


lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

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


lowerCAmelCase__     						=			r'\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(SCREAMING_SNAKE_CASE						)
class     a_			:






 '''simple docstring'''






 def __call__(      self :  Optional[int]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  Optional[bool] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        if titles is None and texts is None:
               return super().__call__(
                   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        elif titles is None or texts is None:
               lowerCAmelCase__					     = titles if texts is None else texts
               return super().__call__(
                   lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = titles if not isinstance(lowercase__      ,   lowercase__) else [titles]
        lowerCAmelCase__					     = texts if not isinstance(lowercase__      ,   lowercase__) else [texts]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = questions if not isinstance(lowercase__      ,   lowercase__) else [questions] * n_passages
        if len(lowercase__) != len(lowercase__):
               raise ValueError(
                   F"""There should be as many titles than texts but got {len(lowercase__)} titles and {len(lowercase__)} texts.""")
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   add_special_tokens=lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = {
            '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(lowercase__      ,   lowercase__)
            ]
        }
        if return_attention_mask is not False:
               lowerCAmelCase__					     = []
               for input_ids in encoded_inputs["input_ids"]:
                      attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
               lowerCAmelCase__					     = attention_mask
        return self.pad(lowercase__      ,   padding=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  BatchEncoding      ,   lowercase__ :  DPRReaderOutput      ,   lowercase__ :  int = 16      ,   lowercase__ :  int = 64      ,   lowercase__ :  int = 4      ,   ):







        '''simple docstring'''



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

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




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  int      ,   lowercase__ :  int      ,   ):







        '''simple docstring'''



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






@add_end_docstrings(SCREAMING_SNAKE_CASE						)
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= READER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= READER_PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_     			= ['input_ids', 'attention_mask']
