




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
     import tensorflow as tf

     from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class     __a:






    """simple docstring"""





    lowerCAmelCase         = PegasusConfig
    lowerCAmelCase         = {}
    lowerCAmelCase         = '''gelu'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=13  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=99  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=37  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=40  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE=0  ,)					->						List[Any]:
         UpperCAmelCase_     :				Optional[int]  =     parent
         UpperCAmelCase_     :				Tuple  =     batch_size
         UpperCAmelCase_     :				Union[str, Any]  =     seq_length
         UpperCAmelCase_     :				List[Any]  =     is_training
         UpperCAmelCase_     :				Optional[Any]  =     use_labels
         UpperCAmelCase_     :				str  =     vocab_size
         UpperCAmelCase_     :				str  =     hidden_size
         UpperCAmelCase_     :				int  =     num_hidden_layers
         UpperCAmelCase_     :				Dict  =     num_attention_heads
         UpperCAmelCase_     :				List[Any]  =     intermediate_size

         UpperCAmelCase_     :				List[Any]  =     hidden_dropout_prob
         UpperCAmelCase_     :				Union[str, Any]  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				Optional[Any]  =     max_position_embeddings
         UpperCAmelCase_     :				Union[str, Any]  =     eos_token_id
         UpperCAmelCase_     :				Union[str, Any]  =     pad_token_id
         UpperCAmelCase_     :				Optional[int]  =     bos_token_id





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				str  =     ids_tensor([self.batch_size, self.seq_length - 1]  ,self.vocab_size    )
         UpperCAmelCase_     :				List[Any]  =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )  ,1    )
         UpperCAmelCase_     :				Any  =     tf.concat([input_ids, eos_tensor]  ,axis=1    )

         UpperCAmelCase_     :				str  =     ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

         UpperCAmelCase_     :				Any  =     self.config_cls(
             vocab_size=self.vocab_size  ,d_model=self.hidden_size  ,encoder_layers=self.num_hidden_layers  ,decoder_layers=self.num_hidden_layers  ,encoder_attention_heads=self.num_attention_heads  ,decoder_attention_heads=self.num_attention_heads  ,encoder_ffn_dim=self.intermediate_size  ,decoder_ffn_dim=self.intermediate_size  ,dropout=self.hidden_dropout_prob  ,attention_dropout=self.attention_probs_dropout_prob  ,max_position_embeddings=self.max_position_embeddings  ,eos_token_ids=[2]  ,bos_token_id=self.bos_token_id  ,pad_token_id=self.pad_token_id  ,decoder_start_token_id=self.pad_token_id  ,**self.config_updates  ,)
         UpperCAmelCase_     :				Optional[Any]  =     prepare_pegasus_inputs_dict(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         return config, inputs_dict





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Tuple  =     TFPegasusModel(config=_SCREAMING_SNAKE_CASE    ).get_decoder()
         UpperCAmelCase_     :				Optional[Any]  =     inputs_dict['''input_ids''']

         UpperCAmelCase_     :				Dict  =     input_ids[:1, :]
         UpperCAmelCase_     :				List[Any]  =     inputs_dict['''attention_mask'''][:1, :]
         UpperCAmelCase_     :				Optional[Any]  =     inputs_dict['''head_mask''']
         UpperCAmelCase_     :				Any  =     1

         # first forward pass
         UpperCAmelCase_     :				Tuple  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,head_mask=_SCREAMING_SNAKE_CASE  ,use_cache=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				int  =     outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         UpperCAmelCase_     :				str  =     ids_tensor((self.batch_size, 3)  ,config.vocab_size    )
         UpperCAmelCase_     :				List[Any]  =     tf.cast(ids_tensor((self.batch_size, 3)  ,2    )  ,tf.inta    )

         # append to next input_ids and
         UpperCAmelCase_     :				Optional[int]  =     tf.concat([input_ids, next_tokens]  ,axis=-1    )
         UpperCAmelCase_     :				Any  =     tf.concat([attention_mask, next_attn_mask]  ,axis=-1    )

         UpperCAmelCase_     :				Optional[Any]  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE    )[0]
         UpperCAmelCase_     :				Tuple  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,past_key_values=_SCREAMING_SNAKE_CASE    )[0]

         self.parent.assertEqual(next_tokens.shape[1]  ,output_from_past.shape[1]    )

         # select random slice
         UpperCAmelCase_     :				Any  =     int(ids_tensor((1,)  ,output_from_past.shape[-1]    )    )
         UpperCAmelCase_     :				Tuple  =     output_from_no_past[:, -3:, random_slice_idx]
         UpperCAmelCase_     :				Union[str, Any]  =     output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,rtol=1e-3    )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=None					,		_lowercase=None					,		_lowercase=None					,		_lowercase=None					,		):

     '''simple docstring'''


     if attention_mask is None:
          UpperCAmelCase_     :				Optional[int]  =     tf.cast(tf.math.not_equal(_lowercase					,		config.pad_token_id )					,		tf.inta )
     if decoder_attention_mask is None:
          UpperCAmelCase_     :				str  =     tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape					,		dtype=tf.inta ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,		config.pad_token_id )					,		tf.inta ),
              ]					,		axis=-1					,		)
     if head_mask is None:
          UpperCAmelCase_     :				Any  =     tf.ones((config.encoder_layers, config.encoder_attention_heads) )
     if decoder_head_mask is None:
          UpperCAmelCase_     :				List[str]  =     tf.ones((config.decoder_layers, config.decoder_attention_heads) )
     if cross_attn_head_mask is None:
          UpperCAmelCase_     :				str  =     tf.ones((config.decoder_layers, config.decoder_attention_heads) )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }

@require_tf
class     __a(	_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    lowerCAmelCase         = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    lowerCAmelCase         = (
        {
            '''conversational''': TFPegasusForConditionalGeneration,
            '''feature-extraction''': TFPegasusModel,
            '''summarization''': TFPegasusForConditionalGeneration,
            '''text2text-generation''': TFPegasusForConditionalGeneration,
            '''translation''': TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowerCAmelCase         = True
    lowerCAmelCase         = False
    lowerCAmelCase         = False





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Union[str, Any]  =     TFPegasusModelTester(self    )
         UpperCAmelCase_     :				str  =     ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[int]:
         self.config_tester.run_common_tests()





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Any  =     self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE    )


@require_sentencepiece
@require_tokenizers
@require_tf
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = [
        ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
        ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
    ]
    lowerCAmelCase         = [
        '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
        ''' reduce the risk of wildfires.''',
        '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    lowerCAmelCase         = '''google/pegasus-xsum'''





    @cached_property
    def 				a__ ( self    )					->						Optional[Any]:
         return AutoTokenizer.from_pretrained(self.model_name    )





    @cached_property
    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				str  =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
         return model





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Tuple:
         UpperCAmelCase_     :				Optional[Any]  =     self.translate_src_text(**_SCREAMING_SNAKE_CASE    )
         assert self.expected_text == generated_words





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         UpperCAmelCase_     :				Any  =     self.tokenizer(self.src_text  ,**_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,return_tensors='''tf'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.model.generate(
             model_inputs.input_ids  ,attention_mask=model_inputs.attention_mask  ,num_beams=2  ,use_cache=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				Dict  =     self.tokenizer.batch_decode(generated_ids.numpy()  ,skip_special_tokens=_SCREAMING_SNAKE_CASE    )
         return generated_words





    @slow
    def 				a__ ( self    )					->						List[Any]:
         self._assert_generated_batch_equal_expected()





import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__a							     =					logging.get_logger(__name__)

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

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

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


__a							     =					{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__a							     =					{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__a							     =					{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


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

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


__a							     =					R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '

@add_start_docstrings(_a  )
class     __a:






    """simple docstring"""





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





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

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						List[DPRSpanPrediction]:
         UpperCAmelCase_     :				Tuple  =     []
         for start_index, start_score in enumerate(_SCREAMING_SNAKE_CASE    ):
              for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]    ):
                   scores.append(((start_index, start_index + answer_length), start_score + end_score)    )
         UpperCAmelCase_     :				int  =     sorted(_SCREAMING_SNAKE_CASE  ,key=lambda _SCREAMING_SNAKE_CASE    : x[1]  ,reverse=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     []
         for (start_index, end_index), score in scores:
              if start_index > end_index:
                   raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''    )
              UpperCAmelCase_     :				str  =     end_index - start_index + 1
              if length > max_answer_length:
                   raise ValueError(f'''Span is too long: {length} > {max_answer_length}'''    )
              if any(
                  start_index <= prev_start_index <= prev_end_index <= end_index
                  or prev_start_index <= start_index <= end_index <= prev_end_index
                  for (prev_start_index, prev_end_index) in chosen_span_intervals    ):
                   continue
              chosen_span_intervals.append((start_index, end_index)    )

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


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






    """simple docstring"""





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