






"""simple docstring"""



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   List[Any] = None    ,						)    ->     Tuple:
							lowerCamelCase_	:							List[str]									={}

							if train_file is not None:
														lowerCamelCase_	:							Tuple									=[train_file]
							if eval_file is not None:
														lowerCamelCase_	:							List[str]									=[eval_file]
							if test_file is not None:
														lowerCamelCase_	:							List[Any]									=[test_file]

							lowerCamelCase_	:							List[Any]									=datasets.load_dataset("csv"    ,						data_files=__lowerCAmelCase  )
							lowerCamelCase_	:							Optional[int]									=list(ds[list(files.keys()  )[0]].features.keys()  )
							lowerCamelCase_	:							List[str]									=features_name.pop(__lowerCAmelCase  )
							lowerCamelCase_	:							Optional[Any]									=list(set(ds[list(files.keys()  )[0]][label_name]  )  )
							lowerCamelCase_	:							Optional[Any]									={label: i for i, label in enumerate(__lowerCAmelCase  )}
							lowerCamelCase_	:							int									=tokenizer.model_input_names
							lowerCamelCase_	:							List[Any]									={}

							if len(__lowerCAmelCase  ) == 1:
														for k in files.keys():
																					lowerCamelCase_	:							Optional[int]									=ds[k].map(
																					    lambda lowerCamelCase__  : tokenizer.batch_encode_plus(
																					        example[features_name[0]]    ,						truncation=__lowerCAmelCase    ,						max_length=__lowerCAmelCase    ,						padding="max_length"  )    ,						batched=__lowerCAmelCase    ,						)
							elif len(__lowerCAmelCase  ) == 2:
														for k in files.keys():
																					lowerCamelCase_	:							Optional[Any]									=ds[k].map(
																					    lambda lowerCamelCase__  : tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])    ,						truncation=__lowerCAmelCase    ,						max_length=__lowerCAmelCase    ,						padding="max_length"    ,						)    ,						batched=__lowerCAmelCase    ,						)

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					lowerCamelCase_	:							int									={k: v for k, v in ex.items() if k in input_names}
																					lowerCamelCase_	:							Union[str, Any]									=labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					lowerCamelCase_	:							Optional[Any]									={k: v for k, v in ex.items() if k in input_names}
																					lowerCamelCase_	:							List[str]									=labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					lowerCamelCase_	:							List[Any]									={k: v for k, v in ex.items() if k in input_names}
																					lowerCamelCase_	:							Any									=labelaid[ex[label_name]]
																					yield (d, label)

							lowerCamelCase_	:							List[Any]									=(
							    tf.data.Dataset.from_generator(
							        __lowerCAmelCase    ,						({k: tf.intaa for k in input_names}, tf.intaa)    ,						({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))    ,						)
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														lowerCamelCase_	:							str									=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

							lowerCamelCase_	:							Tuple									=(
							    tf.data.Dataset.from_generator(
							        __lowerCAmelCase    ,						({k: tf.intaa for k in input_names}, tf.intaa)    ,						({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))    ,						)
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														lowerCamelCase_	:							List[Any]									=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]  )  )  )

							lowerCamelCase_	:							List[Any]									=(
							    tf.data.Dataset.from_generator(
							        __lowerCAmelCase    ,						({k: tf.intaa for k in input_names}, tf.intaa)    ,						({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))    ,						)
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														lowerCamelCase_	:							Optional[int]									=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]  )  )  )

							return train_ds, val_ds, test_ds, labelaid


A__	:    List[Any]			 = logging.getLogger(__name__)







@dataclass
class        lowercase__      :
			_UpperCAmelCase		:int             =			field(metadata={"help": "Which column contains the label"}       )
			_UpperCAmelCase		:str             =			field(default=UpperCamelCase_,       metadata={"help": "The path of the training file"}       )
			_UpperCAmelCase		:Optional[str]             =			field(default=UpperCamelCase_,       metadata={"help": "The path of the development file"}       )
			_UpperCAmelCase		:Optional[str]             =			field(default=UpperCamelCase_,       metadata={"help": "The path of the test file"}       )
			_UpperCAmelCase		:int             =			field(
			    default=128,       metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    },       )
			_UpperCAmelCase		:bool             =			field(
			    default=UpperCamelCase_,       metadata={"help": "Overwrite the cached training and evaluation sets"}       )







@dataclass
class        lowercase__      :
			_UpperCAmelCase		:str             =			field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
			_UpperCAmelCase		:Optional[str]             =			field(
			    default=UpperCamelCase_,       metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
			_UpperCAmelCase		:Optional[str]             =			field(
			    default=UpperCamelCase_,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
			_UpperCAmelCase		:bool             =			field(default=UpperCamelCase_,       metadata={"help": "Set this flag to use fast tokenization."}       )
			# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
			# or just modify its tokenizer_config.json.
			_UpperCAmelCase		:Optional[str]             =			field(
			    default=UpperCamelCase_,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},       )


def 			_snake_case    (					)    ->     Any:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.
							lowerCamelCase_	:							Optional[Any]									=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
							lowerCamelCase_	:							Optional[int]									=parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir  )
							    and os.listdir(training_args.output_dir  )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    " --overwrite_output_dir to overcome."  )

							# Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,						datefmt="%m/%d/%Y %H:%M:%S"    ,						level=logging.INFO    ,						)
							logger.info(
							    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1  )}, """
							    F"""16-bits training: {training_args.fpaa}"""  )
							logger.info(F"""Training/evaluation parameters {training_args}"""  )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							lowerCamelCase_	:							List[Any]									=AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,						cache_dir=model_args.cache_dir    ,						)

							lowerCamelCase_	:							Union[str, Any]									=get_tfds(
							    train_file=data_args.train_file    ,						eval_file=data_args.dev_file    ,						test_file=data_args.test_file    ,						tokenizer=__lowerCAmelCase    ,						label_column_id=data_args.label_column_id    ,						max_seq_length=data_args.max_seq_length    ,						)

							lowerCamelCase_	:							List[str]									=AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,						num_labels=len(__lowerCAmelCase  )    ,						labelaid=__lowerCAmelCase    ,						idalabel={id: label for label, id in labelaid.items()}    ,						finetuning_task="text-classification"    ,						cache_dir=model_args.cache_dir    ,						)

							with training_args.strategy.scope():
														lowerCamelCase_	:							Any									=TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path    ,						from_pt=bool(".bin" in model_args.model_name_or_path  )    ,						config=__lowerCAmelCase    ,						cache_dir=model_args.cache_dir    ,						)

							def compute_metrics(lowerCamelCase__     :   Union[str, Any]  ) -> Dict:
														lowerCamelCase_	:							Optional[int]									=np.argmax(p.predictions    ,						axis=1  )

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							lowerCamelCase_	:							str									=TFTrainer(
							    model=__lowerCAmelCase    ,						args=__lowerCAmelCase    ,						train_dataset=__lowerCAmelCase    ,						eval_dataset=__lowerCAmelCase    ,						compute_metrics=__lowerCAmelCase    ,						)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir  )

							# Evaluation
							lowerCamelCase_	:							Union[str, Any]									={}
							if training_args.do_eval:
														logger.info("*** Evaluate ***"  )
														lowerCamelCase_	:							Union[str, Any]									=trainer.evaluate()
														lowerCamelCase_	:							Tuple									=os.path.join(training_args.output_dir    ,						"eval_results.txt"  )

														with open(__lowerCAmelCase    ,						"w"  ) as writer:
																					logger.info("***** Eval results *****"  )

																					for key, value in result.items():
																												logger.info(F"""  {key} = {value}"""  )
																												writer.write(F"""{key} = {value}\n"""  )

																					results.update(__lowerCAmelCase  )

							return results


if __name__ == "__main__":
						main()











"""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__	:    Tuple			 = logging.get_logger(__name__)

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

A__	:    List[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__	:    Optional[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__	:    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__	:    Dict			 = {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A__	:    Dict			 = {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
A__	:    Any			 = {
    '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__	:    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        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[Any]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Dict             =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:str             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Dict             =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Dict             =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


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

A__	:    Union[str, Any]			 = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A__	:    List[Any]			 = 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(snake_case__       )
class        lowercase__      :


			def __call__(				self					:  List[str]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Optional[str] = None       ,	snake_case__					:  Optional[str] = None       ,	snake_case__					:  Union[bool, str] = False       ,	snake_case__					:  Union[bool, str] = False       ,	snake_case__					:  Optional[int] = None       ,	snake_case__					:  Optional[Union[str, TensorType]] = None       ,	snake_case__					:  Optional[bool] = None       ,	**snake_case__					:  Tuple       ,	):
										if titles is None and texts is None:
																	return super().__call__(
																	    snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__       ,	max_length=snake_case__       ,	return_tensors=snake_case__       ,	return_attention_mask=snake_case__       ,	**snake_case__       ,	)
										elif titles is None or texts is None:
																	lowerCamelCase_	:							Dict									=titles if texts is None else texts
																	return super().__call__(
																	    snake_case__       ,	snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__       ,	max_length=snake_case__       ,	return_tensors=snake_case__       ,	return_attention_mask=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Tuple									=titles if not isinstance(snake_case__       ,	snake_case__ ) else [titles]
										lowerCamelCase_	:							List[str]									=texts if not isinstance(snake_case__       ,	snake_case__ ) else [texts]
										lowerCamelCase_	:							int									=len(snake_case__ )
										lowerCamelCase_	:							Any									=questions if not isinstance(snake_case__       ,	snake_case__ ) else [questions] * n_passages
										if len(snake_case__ ) != len(snake_case__ ):
																	raise ValueError(
																	    F"""There should be as many titles than texts but got {len(snake_case__ )} titles and {len(snake_case__ )} texts.""" )
										lowerCamelCase_	:							Any									=super().__call__(snake_case__       ,	snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__ )["input_ids"]
										lowerCamelCase_	:							Union[str, Any]									=super().__call__(snake_case__       ,	add_special_tokens=snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__ )["input_ids"]
										lowerCamelCase_	:							str									={
										    "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(snake_case__       ,	snake_case__ )
										    ]
										}
										if return_attention_mask is not False:
																	lowerCamelCase_	:							List[Any]									=[]
																	for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
																	lowerCamelCase_	:							Optional[int]									=attention_mask
										return self.pad(snake_case__       ,	padding=snake_case__       ,	max_length=snake_case__       ,	return_tensors=snake_case__ )


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

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


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  List[int]       ,	snake_case__					:  List[int]       ,	snake_case__					:  int       ,	snake_case__					:  int       ,	):
										lowerCamelCase_	:							Any									=[]
										for start_index, start_score in enumerate(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) )
										lowerCamelCase_	:							List[str]									=sorted(snake_case__       ,	key=lambda snake_case__ : x[1]       ,	reverse=snake_case__ )
										lowerCamelCase_	:							List[Any]									=[]
										for (start_index, end_index), score in scores:
																	if start_index > end_index:
																								raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""" )
																	lowerCamelCase_	:							Tuple									=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(snake_case__ ) == top_spans:
																								break
										return chosen_span_intervals







@add_end_docstrings(snake_case__       )
class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:List[str]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Union[str, Any]             =			READER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:int             =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:int             =			READER_PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase		:Optional[Any]             =			["input_ids", "attention_mask"]



