





'''simple docstring'''




import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
    )






class UpperCAmelCase							:



    def __init__(  self     ,			__A     ,			__A=13     ,			__A=7     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=99     ,			__A=64     ,			__A=32     ,			__A=5     ,			__A=4     ,			__A=37     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=512     ,			__A=16     ,			__A=2     ,			__A=0.0_2     ,			__A=3     ,			__A=4     ,			__A=None     ,			):
     __UpperCAmelCase					=   parent
     __UpperCAmelCase					=   batch_size
     __UpperCAmelCase					=   seq_length
     __UpperCAmelCase					=   is_training
     __UpperCAmelCase					=   use_input_mask
     __UpperCAmelCase					=   use_token_type_ids
     __UpperCAmelCase					=   use_labels
     __UpperCAmelCase					=   vocab_size
     __UpperCAmelCase					=   hidden_size
     __UpperCAmelCase					=   embedding_size
     __UpperCAmelCase					=   num_hidden_layers
     __UpperCAmelCase					=   num_attention_heads
     __UpperCAmelCase					=   intermediate_size
     __UpperCAmelCase					=   hidden_act
     __UpperCAmelCase					=   hidden_dropout_prob
     __UpperCAmelCase					=   attention_probs_dropout_prob
     __UpperCAmelCase					=   max_position_embeddings
     __UpperCAmelCase					=   type_vocab_size
     __UpperCAmelCase					=   type_sequence_label_size
     __UpperCAmelCase					=   initializer_range
     __UpperCAmelCase					=   num_labels
     __UpperCAmelCase					=   num_choices
     __UpperCAmelCase					=   scope



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size					)

     __UpperCAmelCase					=   None
     if self.use_input_mask:
      __UpperCAmelCase					=   random_attention_mask([self.batch_size, self.seq_length]					)

     __UpperCAmelCase					=   None
     if self.use_token_type_ids:
      __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size					)

     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     if self.use_labels:
      __UpperCAmelCase					=   ids_tensor([self.batch_size]     ,			self.type_sequence_label_size					)
      __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels					)
      __UpperCAmelCase					=   ids_tensor([self.batch_size]     ,			self.num_choices					)

     __UpperCAmelCase					=   self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



    def 					__lowerCamelCase						(  self					):
     return MegatronBertConfig(
         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     ,			embedding_size=self.embedding_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     ,			is_decoder=UpperCamelCase__     ,			initializer_range=self.initializer_range     ,			)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertModel(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__					)
     __UpperCAmelCase					=   model(UpperCamelCase__     ,			token_type_ids=UpperCamelCase__					)
     __UpperCAmelCase					=   model(UpperCamelCase__					)

     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)					)
     self.parent.assertEqual(result.pooler_output.shape     ,			(self.batch_size, self.hidden_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForMaskedLM(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			labels=UpperCamelCase__					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForCausalLM(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			labels=UpperCamelCase__					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForNextSentencePrediction(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(
         UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			labels=UpperCamelCase__     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, 2)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForPreTraining(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(
         UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			labels=UpperCamelCase__     ,			next_sentence_label=UpperCamelCase__     ,			)
     self.parent.assertEqual(result.prediction_logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)
     self.parent.assertEqual(result.seq_relationship_logits.shape     ,			(self.batch_size, 2)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForQuestionAnswering(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(
         UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			start_positions=UpperCamelCase__     ,			end_positions=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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   MegatronBertForSequenceClassification(UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			labels=UpperCamelCase__					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   MegatronBertForTokenClassification(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   model(UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			labels=UpperCamelCase__					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_choices
     __UpperCAmelCase					=   MegatronBertForMultipleChoice(config=UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.eval()
     __UpperCAmelCase					=   input_ids.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   token_type_ids.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   input_mask.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   model(
         UpperCamelCase__     ,			attention_mask=UpperCamelCase__     ,			token_type_ids=UpperCamelCase__     ,			labels=UpperCamelCase__     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     (
         __UpperCAmelCase       
     )					=   config_and_inputs
     __UpperCAmelCase					=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict






@require_torch
class UpperCAmelCase							(		__A					,			__A					,			unittest.TestCase							):
    _A :			Any						   =       (
        (
            MegatronBertModel,
            MegatronBertForMaskedLM,
            MegatronBertForCausalLM,
            MegatronBertForMultipleChoice,
            MegatronBertForNextSentencePrediction,
            MegatronBertForPreTraining,
            MegatronBertForQuestionAnswering,
            MegatronBertForSequenceClassification,
            MegatronBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _A :			int						   =       (
        {
            """feature-extraction""": MegatronBertModel,
            """fill-mask""": MegatronBertForMaskedLM,
            """question-answering""": MegatronBertForQuestionAnswering,
            """text-classification""": MegatronBertForSequenceClassification,
            """text-generation""": MegatronBertForCausalLM,
            """token-classification""": MegatronBertForTokenClassification,
            """zero-shot""": MegatronBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _A :			str						   =       True
    # test_resize_embeddings = False
    _A :			int						   =       False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A=False					):
     __UpperCAmelCase					=   super()._prepare_for_class(UpperCamelCase__     ,			UpperCamelCase__     ,			return_labels=UpperCamelCase__					)

     if return_labels:
      if model_class in get_values(UpperCamelCase__					):
       __UpperCAmelCase					=   torch.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)     ,			dtype=torch.long     ,			device=UpperCamelCase__					)
       __UpperCAmelCase					=   torch.zeros(
           self.model_tester.batch_size     ,			dtype=torch.long     ,			device=UpperCamelCase__					)
     return inputs_dict



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   MegatronBertModelTester(self					)
     __UpperCAmelCase					=   ConfigTester(self     ,			config_class=UpperCamelCase__     ,			hidden_size=37					)



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



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_model(*UpperCamelCase__					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_masked_lm(*UpperCamelCase__					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*UpperCamelCase__					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*UpperCamelCase__					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_pretraining(*UpperCamelCase__					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_question_answering(*UpperCamelCase__					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*UpperCamelCase__					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_token_classification(*UpperCamelCase__					)







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Optional[Any]:
 return torch.tensor(
     lowerCAmelCase__       ,    dtype=torch.long       ,    device=lowerCAmelCase__       ,    )


_A:					List[Any]							=				1E-4






@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase							(		unittest.TestCase							):



    @slow
    @unittest.skip('Model is not available.'					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   'nvidia/megatron-bert-uncased-345m'
     if "MYDIR" in os.environ:
      __UpperCAmelCase					=   os.path.join(os.environ['MYDIR']     ,			UpperCamelCase__					)
     __UpperCAmelCase					=   MegatronBertModel.from_pretrained(UpperCamelCase__					)
     model.to(UpperCamelCase__					)
     model.half()
     __UpperCAmelCase					=   _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]					)
     with torch.no_grad():
      __UpperCAmelCase					=   model(UpperCamelCase__					)[0]
     __UpperCAmelCase					=   torch.Size((1, 9, 1_024)					)
     self.assertEqual(output.shape     ,			UpperCamelCase__					)

     __UpperCAmelCase					=   [-0.6_0_4_0, -0.2_5_1_7, -0.1_0_2_5, 0.3_4_2_0, -0.6_7_5_8, -0.0_0_1_7, -0.1_0_8_9, -0.1_9_9_0, 0.5_7_2_8]
     for ii in range(3					):
      for jj in range(3					):
       __UpperCAmelCase					=   output[0, ii, jj]
       __UpperCAmelCase					=   expected[3 * ii + jj]
       __UpperCAmelCase					=   'ii={} jj={} a={} b={}'.format(UpperCamelCase__     ,			UpperCamelCase__     ,			UpperCamelCase__     ,			UpperCamelCase__					)
       self.assertTrue(math.isclose(UpperCamelCase__     ,			UpperCamelCase__     ,			rel_tol=UpperCamelCase__     ,			abs_tol=UpperCamelCase__					)     ,			msg=UpperCamelCase__					)












'''simple docstring'''




import collections
from typing import List, Optional, Union

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


_A:					List[str]							=				logging.get_logger(__name__)

_A:					Dict							=				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_A:					int							=				{
    """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:					int							=				{
    """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:					Dict							=				{
    """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:					Any							=				{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_A:					Tuple							=				{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_A:					int							=				{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_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:					List[Any]							=				{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_A:					Dict							=				{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       VOCAB_FILES_NAMES
    _A :			Dict						   =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Any						   =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Optional[Any]						   =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[str]						   =       VOCAB_FILES_NAMES
    _A :			str						   =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Union[str, Any]						   =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			int						   =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

_A:					Tuple							=				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 UpperCAmelCase							:



    def __call__(  self     ,			__A     ,			__A = None     ,			__A = None     ,			__A = False     ,			__A = False     ,			__A = None     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     if titles is None and texts is None:
      return super().__call__(
          __A     ,			padding=__A     ,			truncation=__A     ,			max_length=__A     ,			return_tensors=__A     ,			return_attention_mask=__A     ,			**__A     ,			)
     elif titles is None or texts is None:
      __UpperCAmelCase					=   titles if texts is None else texts
      return super().__call__(
          __A     ,			__A     ,			padding=__A     ,			truncation=__A     ,			max_length=__A     ,			return_tensors=__A     ,			return_attention_mask=__A     ,			**__A     ,			)
     __UpperCAmelCase					=   titles if not isinstance(__A     ,			__A					) else [titles]
     __UpperCAmelCase					=   texts if not isinstance(__A     ,			__A					) else [texts]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   questions if not isinstance(__A     ,			__A					) else [questions] * n_passages
     if len(__A					) != len(__A					):
      raise ValueError(
          f'There should be as many titles than texts but got {len(__A					)} titles and {len(__A					)} texts.'					)
     __UpperCAmelCase					=   super().__call__(__A     ,			__A     ,			padding=__A     ,			truncation=__A					)['input_ids']
     __UpperCAmelCase					=   super().__call__(__A     ,			add_special_tokens=__A     ,			padding=__A     ,			truncation=__A					)['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(__A     ,			__A					)
         ]
     }
     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(__A     ,			padding=__A     ,			max_length=__A     ,			return_tensors=__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A = 16     ,			__A = 64     ,			__A = 4     ,			):
     __UpperCAmelCase					=   reader_input['input_ids']
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   reader_output[:3]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   sorted(range(__A					)     ,			reverse=__A     ,			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(__A					)

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




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			):
     __UpperCAmelCase					=   []
     for start_index, start_score in enumerate(__A					):
      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(__A     ,			key=lambda __A					: x[1]     ,			reverse=__A					)
     __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(__A					) == top_spans:
       break
     return chosen_span_intervals






@add_end_docstrings(UpperCAmelCase_							)
class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_							):
    _A :			Optional[int]						   =       VOCAB_FILES_NAMES
    _A :			Optional[int]						   =       READER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Dict						   =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Union[str, Any]						   =       READER_PRETRAINED_INIT_CONFIGURATION
    _A :			List[Any]						   =       ["""input_ids""", """attention_mask"""]





