
'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDebertaVaForMaskedLM,
					    TFDebertaVaForQuestionAnswering,
					    TFDebertaVaForSequenceClassification,
					    TFDebertaVaForTokenClassification,
					    TFDebertaVaModel,
					)




class      __snake_case		:
					def __init__(  self,     A,     A=13,     A=7,     A=True,     A=True,     A=True,     A=True,     A=99,     A=32,     A=2,     A=4,     A=37,     A="gelu",     A=0.1,     A=0.1,     A=512,     A=16,     A=2,     A=0.02,     A=False,     A=True,     A="None",     A=3,     A=4,     A=None,     ):

									"""simple docstring"""


									lowerCamelCase       :      int          =			parent
									lowerCamelCase       :      str          =			batch_size
									lowerCamelCase       :      List[Any]          =			seq_length
									lowerCamelCase       :      int          =			is_training
									lowerCamelCase       :      Optional[Any]          =			use_input_mask
									lowerCamelCase       :      int          =			use_token_type_ids
									lowerCamelCase       :      str          =			use_labels
									lowerCamelCase       :      Dict          =			vocab_size
									lowerCamelCase       :      Dict          =			hidden_size
									lowerCamelCase       :      int          =			num_hidden_layers
									lowerCamelCase       :      Any          =			num_attention_heads
									lowerCamelCase       :      str          =			intermediate_size
									lowerCamelCase       :      Any          =			hidden_act
									lowerCamelCase       :      Optional[int]          =			hidden_dropout_prob
									lowerCamelCase       :      Dict          =			attention_probs_dropout_prob
									lowerCamelCase       :      Dict          =			max_position_embeddings
									lowerCamelCase       :      str          =			type_vocab_size
									lowerCamelCase       :      Optional[int]          =			type_sequence_label_size
									lowerCamelCase       :      Any          =			initializer_range
									lowerCamelCase       :      List[str]          =			num_labels
									lowerCamelCase       :      Dict          =			num_choices
									lowerCamelCase       :      Any          =			relative_attention
									lowerCamelCase       :      Optional[Any]          =			position_biased_input
									lowerCamelCase       :      Optional[Any]          =			pos_att_type
									lowerCamelCase       :      List[Any]          =			scope
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      int          =			ids_tensor([self.batch_size, self.seq_length],     self.vocab_size   )

									lowerCamelCase       :      Optional[Any]          =			None
									if self.use_input_mask:
													lowerCamelCase       :      List[str]          =			random_attention_mask([self.batch_size, self.seq_length]   )

									lowerCamelCase       :      List[str]          =			None
									if self.use_token_type_ids:
													lowerCamelCase       :      Tuple          =			ids_tensor([self.batch_size, self.seq_length],     self.type_vocab_size   )

									lowerCamelCase       :      Optional[Any]          =			None
									lowerCamelCase       :      int          =			None
									lowerCamelCase       :      int          =			None
									if self.use_labels:
													lowerCamelCase       :      Tuple          =			ids_tensor([self.batch_size],     self.type_sequence_label_size   )
													lowerCamelCase       :      Optional[Any]          =			ids_tensor([self.batch_size, self.seq_length],     self.num_labels   )

									lowerCamelCase       :      Optional[int]          =			DebertaVaConfig(
									    vocab_size=self.vocab_size,     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,     max_position_embeddings=self.max_position_embeddings,     type_vocab_size=self.type_vocab_size,     relative_attention=self.relative_attention,     position_biased_input=self.position_biased_input,     initializer_range=self.initializer_range,     return_dict=_UpperCAmelCase,     )

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def      UpperCAmelCase_				(  self,     A,     A,     A,     A,     A,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      Union[str, Any]          =			TFDebertaVaModel(config=_UpperCAmelCase   )
									lowerCamelCase       :      Optional[Any]          =			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									lowerCamelCase       :      Optional[int]          =			[input_ids, input_mask]
									lowerCamelCase       :      List[Any]          =			model(_UpperCAmelCase   )

									lowerCamelCase       :      Optional[int]          =			model(_UpperCAmelCase   )

									self.parent.assertEqual(result.last_hidden_state.shape,     (self.batch_size, self.seq_length, self.hidden_size)   )
					def      UpperCAmelCase_				(  self,     A,     A,     A,     A,     A,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      int          =			TFDebertaVaForMaskedLM(config=_UpperCAmelCase   )
									lowerCamelCase       :      Optional[Any]          =			{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									lowerCamelCase       :      Union[str, Any]          =			model(_UpperCAmelCase   )
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.vocab_size)   )
					def      UpperCAmelCase_				(  self,     A,     A,     A,     A,     A,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      Tuple          =			self.num_labels
									lowerCamelCase       :      List[str]          =			TFDebertaVaForSequenceClassification(config=_UpperCAmelCase   )
									lowerCamelCase       :      Union[str, Any]          =			{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									lowerCamelCase       :      List[Any]          =			model(_UpperCAmelCase   )
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels)   )
					def      UpperCAmelCase_				(  self,     A,     A,     A,     A,     A,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[Any]          =			self.num_labels
									lowerCamelCase       :      Optional[int]          =			TFDebertaVaForTokenClassification(config=_UpperCAmelCase   )
									lowerCamelCase       :      Union[str, Any]          =			{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									lowerCamelCase       :      Optional[Any]          =			model(_UpperCAmelCase   )
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.num_labels)   )
					def      UpperCAmelCase_				(  self,     A,     A,     A,     A,     A,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      Union[str, Any]          =			TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase   )
									lowerCamelCase       :      Union[str, Any]          =			{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									lowerCamelCase       :      Optional[Any]          =			model(_UpperCAmelCase   )
									self.parent.assertEqual(result.start_logits.shape,     (self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.end_logits.shape,     (self.batch_size, self.seq_length)   )




					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[int]          =			self.prepare_config_and_inputs()
									(
									    (
									    lowerCamelCase      
									)      ,			(
									    lowerCamelCase      
									)      ,			(
									    lowerCamelCase      
									)      ,			(
									    lowerCamelCase      
									)      ,			(
									    lowerCamelCase      
									)      ,			(
									    lowerCamelCase      
									)      ,			(
									    lowerCamelCase      
									)      ,			      
									)       :      List[Any]          =			config_and_inputs
									lowerCamelCase       :      List[str]          =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict





@require_tf
class      __snake_case		( UpperCAmelCase__     , UpperCAmelCase__     , unittest.TestCase):
					_lowerCAmelCase        =						(
					    (
					        TFDebertaVaModel,
					        TFDebertaVaForMaskedLM,
					        TFDebertaVaForQuestionAnswering,
					        TFDebertaVaForSequenceClassification,
					        TFDebertaVaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCAmelCase        =						(
					    {
					        '''feature-extraction''': TFDebertaVaModel,
					        '''fill-mask''': TFDebertaVaForMaskedLM,
					        '''question-answering''': TFDebertaVaForQuestionAnswering,
					        '''text-classification''': TFDebertaVaForSequenceClassification,
					        '''token-classification''': TFDebertaVaForTokenClassification,
					        '''zero-shot''': TFDebertaVaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					_lowerCAmelCase        =						False
					_lowerCAmelCase        =						False
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[Any]          =			TFDebertaVaModelTester(self   )
									lowerCamelCase       :      Tuple          =			ConfigTester(self,     config_class=_UpperCAmelCase,     hidden_size=37   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[Any]          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Tuple          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      str          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Any          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[int]          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase   )




					@slow
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[int]          =			TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'   )
									self.assertIsNotNone(_UpperCAmelCase   )





@require_tf
class      __snake_case		( unittest.TestCase):
					@unittest.skip(reason='Model not available yet'   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									pass




					@slow
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      str          =			TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'   )
									lowerCamelCase       :      List[str]          =			tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   )
									lowerCamelCase       :      str          =			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
									lowerCamelCase       :      Union[str, Any]          =			model(_UpperCAmelCase,     attention_mask=_UpperCAmelCase   )[0]

									lowerCamelCase       :      Tuple          =			tf.constant(
									    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]   )
									tf.debugging.assert_near(output[:, 1:4, 1:4],     _UpperCAmelCase,     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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


A			:						Optional[Any]        =  logging.get_logger(__name__)

A			:						str        =  {'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			:						Dict        =  {
    '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			:						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'
        ),
    },
}

A			:						int        =  {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
A			:						Optional[Any]        =  {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
A			:						Any        =  {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


A			:						Union[str, Any]        =  {
    '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			:						Any        =  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
					A__     =					DPRContextEncoderTokenizer






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
					A__     =					DPRQuestionEncoderTokenizer






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

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


A			:						int        =  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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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 				A       :
					'''simple docstring'''






					def __call__(self       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None ,   _UpperCAmelCase       :		Optional[str] = None ,   _UpperCAmelCase       :		Union[bool, str] = False ,   _UpperCAmelCase       :		Union[bool, str] = False ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Optional[Union[str, TensorType]] = None ,   _UpperCAmelCase       :		Optional[bool] = None ,   **_UpperCAmelCase       :		Any ,   )		->  BatchEncoding:




							"""simple docstring"""


							if titles is None and texts is None:
									return super().__call__(
									    _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							elif titles is None or texts is None:
									lowercase__	     =						titles if texts is None else texts
									return super().__call__(
									    _UpperCAmelCase ,   _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						titles if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [titles]
							lowercase__	     =						texts if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [texts]
							lowercase__	     =						len(_UpperCAmelCase						)
							lowercase__	     =						questions if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [questions] * n_passages
							assert len(_UpperCAmelCase						) == len(
							    _UpperCAmelCase						), f'''There should be as many titles than texts but got {len(_UpperCAmelCase						)} titles and {len(_UpperCAmelCase						)} texts.'''
							lowercase__	     =						super().__call__(_UpperCAmelCase ,   _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase						)["""input_ids"""]
							lowercase__	     =						super().__call__(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase						)["""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(_UpperCAmelCase ,   _UpperCAmelCase						)
							    ]
							}
							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(_UpperCAmelCase ,   padding=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		BatchEncoding ,   _UpperCAmelCase       :		DPRReaderOutput ,   _UpperCAmelCase       :		int = 16 ,   _UpperCAmelCase       :		int = 64 ,   _UpperCAmelCase       :		int = 4 ,   )		->  List[DPRSpanPrediction]:




							"""simple docstring"""


							lowercase__	     =						reader_input["""input_ids"""]
							lowercase__     ,      lowercase__     ,      lowercase__	     =						reader_output[:3]
							lowercase__	     =						len(_UpperCAmelCase						)
							lowercase__	     =						sorted(range(_UpperCAmelCase						) ,   reverse=_UpperCAmelCase ,   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(_UpperCAmelCase						)

									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=_UpperCAmelCase ,   top_spans=_UpperCAmelCase ,   )
									for start_index, end_index in best_spans:
											start_index += passage_offset
											end_index += passage_offset
											nbest_spans_predictions.append(
											    DPRSpanPrediction(
											        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] ,   relevance_score=relevance_logits[doc_id] ,   doc_id=_UpperCAmelCase ,   start_index=_UpperCAmelCase ,   end_index=_UpperCAmelCase ,   text=self.decode(sequence_ids[start_index : end_index + 1]						) ,   )						)
									if len(_UpperCAmelCase						) >= num_spans:
											break
							return nbest_spans_predictions[:num_spans]


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		int ,   )		->  List[DPRSpanPrediction]:




							"""simple docstring"""


							lowercase__	     =						[]
							for start_index, start_score in enumerate(_UpperCAmelCase						):
									for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
											scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
							lowercase__	     =						sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x[1] ,   reverse=_UpperCAmelCase						)
							lowercase__	     =						[]
							for (start_index, end_index), score in scores:
									assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
									lowercase__	     =						end_index - start_index + 1
									assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
									if any(
									    start_index <= prev_start_index <= prev_end_index <= end_index
									    or prev_start_index <= start_index <= end_index <= prev_end_index
									    for (prev_start_index, prev_end_index) in chosen_span_intervals						):
											continue
									chosen_span_intervals.append((start_index, end_index)						)

									if len(_UpperCAmelCase						) == top_spans:
											break
							return chosen_span_intervals






@add_end_docstrings(UpperCAmelCase__				)
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					READER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					READER_PRETRAINED_INIT_CONFIGURATION
					A__     =					['''input_ids''', '''attention_mask''']
					A__     =					DPRReaderTokenizer

