


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 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				PreTrainedTokenizer       ,       __magic_name__	:				int       ,       __magic_name__	:				Optional[int] = None       ,       )  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						{}

		if train_file is not None:
				lowercase__	     =						[train_file]
		if eval_file is not None:
				lowercase__	     =						[eval_file]
		if test_file is not None:
				lowercase__	     =						[test_file]

		lowercase__	     =						datasets.load_dataset("""csv"""       ,       data_files=__magic_name__	)
		lowercase__	     =						list(ds[list(files.keys()	)[0]].features.keys()	)
		lowercase__	     =						features_name.pop(__magic_name__	)
		lowercase__	     =						list(set(ds[list(files.keys()	)[0]][label_name]	)	)
		lowercase__	     =						{label: i for i, label in enumerate(__magic_name__	)}
		lowercase__	     =						tokenizer.model_input_names
		lowercase__	     =						{}

		if len(__magic_name__	) == 1:
				for k in files.keys():
						lowercase__	     =						ds[k].map(
						    lambda __magic_name__	: tokenizer.batch_encode_plus(
						        example[features_name[0]]       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       padding="""max_length"""	)       ,       batched=__magic_name__       ,       )
		elif len(__magic_name__	) == 2:
				for k in files.keys():
						lowercase__	     =						ds[k].map(
						    lambda __magic_name__	: tokenizer.batch_encode_plus(
						        (example[features_name[0]], example[features_name[1]])       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       padding="""max_length"""       ,       )       ,       batched=__magic_name__       ,       )

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

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

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

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({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:
				lowercase__	     =						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({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:
				lowercase__	     =						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({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:
				lowercase__	     =						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

		return train_ds, val_ds, test_ds, labelaid


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



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					field(metadata={'''help''': '''Which column contains the label'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the training file'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the development file'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the test file'''}				)
					A__     =					field(
					    default=1_28					,    metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated, sequences shorter will be padded.'''
					        )
					    }					,    )
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)






@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
					A__     =					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.
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,    )






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
		lowercase__     ,      lowercase__     ,      lowercase__	     =						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.

		lowercase__	     =						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       ,       )

		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						get_tfds(
		    train_file=data_args.train_file       ,       eval_file=data_args.dev_file       ,       test_file=data_args.test_file       ,       tokenizer=__magic_name__       ,       label_column_id=data_args.label_column_id       ,       max_seq_length=data_args.max_seq_length       ,       )

		lowercase__	     =						AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,       num_labels=len(__magic_name__	)       ,       labelaid=__magic_name__       ,       idalabel={id: label for label, id in labelaid.items()}       ,       finetuning_task="""text-classification"""       ,       cache_dir=model_args.cache_dir       ,       )

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

		def compute_metrics(__magic_name__	:				EvalPrediction	) -> Dict:
				lowercase__	     =						np.argmax(p.predictions       ,       axis=1	)

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

		# Initialize our Trainer
		lowercase__	     =						TFTrainer(
		    model=__magic_name__       ,       args=__magic_name__       ,       train_dataset=__magic_name__       ,       eval_dataset=__magic_name__       ,       compute_metrics=__magic_name__       ,       )

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

		# Evaluation
		lowercase__	     =						{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""	)
				lowercase__	     =						trainer.evaluate()
				lowercase__	     =						os.path.join(training_args.output_dir       ,       """eval_results.txt"""	)

				with open(__magic_name__       ,       """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(__magic_name__	)

		return results


if __name__ == "__main__":
	main()





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

