



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


__snake_case     :Optional[int]			       =logging.get_logger(__name__)

__snake_case     :Optional[Any]			       ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case     :Optional[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'
        ),
    },
}
__snake_case     :Tuple			       ={
    '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'
        ),
    },
}
__snake_case     :List[Any]			       ={
    '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'
        ),
    },
}

__snake_case     :Dict			       ={
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__snake_case     :List[Any]			       ={
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__snake_case     :Dict			       ={
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__snake_case     :str			       ={
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__snake_case     :Optional[int]			       ={
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__snake_case     :Any			       ={
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     VOCAB_FILES_NAMES
				A_   :     Dict								=     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				A_   :     int								=     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A_   :     Union[str, Any]								=     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				A_   :     Any								=     DPRContextEncoderTokenizer





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Tuple								=     VOCAB_FILES_NAMES
				A_   :     Optional[int]								=     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				A_   :     Dict								=     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A_   :     Optional[Any]								=     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				A_   :     str								=     DPRQuestionEncoderTokenizer





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

__snake_case     :Dict			       =collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__snake_case     :Optional[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(_lowerCamelCase       )
class        lowerCAmelCase__					:


				def __call__(							self      :  List[str]						, __UpperCamelCase      :  Any						, __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      :  str						, )		->     BatchEncoding:
											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:
																		A      						=    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						, )
											A      						=    titles if not isinstance(__UpperCamelCase						, __UpperCamelCase       ) else [titles]
											A      						=    texts if not isinstance(__UpperCamelCase						, __UpperCamelCase       ) else [texts]
											A      						=    len(__UpperCamelCase       )
											A      						=    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.'''
											A      						=    super().__call__(__UpperCamelCase						, __UpperCamelCase						, padding=__UpperCamelCase						, truncation=__UpperCamelCase       )['input_ids']
											A      						=    super().__call__(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, padding=__UpperCamelCase						, truncation=__UpperCamelCase       )['input_ids']
											A      						=    {
											    '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:
																		A      						=    []
																		for input_ids in encoded_inputs["input_ids"]:
																									attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
																		A      						=    attention_mask
											return self.pad(__UpperCamelCase						, padding=__UpperCamelCase						, max_length=__UpperCamelCase						, return_tensors=__UpperCamelCase       )


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

																		A      						=    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 __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  int						, )		->     List[DPRSpanPrediction]:
											A      						=    []
											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)       )
											A      						=    sorted(__UpperCamelCase						, key=lambda __UpperCamelCase       : x[1]						, reverse=__UpperCamelCase       )
											A      						=    []
											for (start_index, end_index), score in scores:
																		assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
																		A      						=    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(_lowerCamelCase       )
class        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase       ):
				A_   :     List[str]								=     VOCAB_FILES_NAMES
				A_   :     str								=     READER_PRETRAINED_VOCAB_FILES_MAP
				A_   :     List[Any]								=     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A_   :     Dict								=     READER_PRETRAINED_INIT_CONFIGURATION
				A_   :     List[str]								=     ['input_ids', 'attention_mask']
				A_   :     List[Any]								=     DPRReaderTokenizer




import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


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

__snake_case     :int			       =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__snake_case     :Optional[int]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCamelCase       )}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}       )
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} ,				)
				A_   :     int								=     field(
				    default=6_4 ,				metadata={
				        'help': (
				            'The maximum number of tokens for the question. Questions longer than this will '
				            'be truncated to this length.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=3_0 ,				metadata={
				        'help': (
				            'The maximum length of an answer that can be generated. This is needed because the start '
				            'and end predictions are not conditioned on one another.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}       )
				A_   :     float								=     field(
				    default=0.0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=2_0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=0 ,				metadata={
				        'help': (
				            'language id of input for language-specific xlm models (see'
				            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
				        )
				    } ,				)
				A_   :     int								=     field(default=1 ,				metadata={'help': 'multiple threads for converting example to features'}       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     'train'
				A_   :     str								=     'dev'





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     SquadDataTrainingArguments
				A_   :     List[SquadFeatures]
				A_   :     Split
				A_   :     bool


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  SquadDataTrainingArguments						, __UpperCamelCase      :  PreTrainedTokenizer						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Union[str, Split] = Split.train						, __UpperCamelCase      :  Optional[bool] = False						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  Optional[str] = "pt"						, )		->     Any:
											A      						=    args
											A      						=    is_language_sensitive
											A      						=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																		try:
																									A      						=    Split[mode]
																		except KeyError:
																									raise KeyError('mode is not a valid split name'       )
											A      						=    mode
											# Load data features from cache or dataset file
											A      						=    'v2' if args.version_2_with_negative else 'v1'
											A      						=    os.path.join(
											    cache_dir if cache_dir is not None else args.data_dir						, f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'''						, )

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											A      						=    cached_features_file + '.lock'
											with FileLock(__UpperCamelCase       ):
																		if os.path.exists(__UpperCamelCase       ) and not args.overwrite_cache:
																									A      						=    time.time()
																									A      						=    torch.load(__UpperCamelCase       )

																									# Legacy cache files have only features, while new cache files
																									# will have dataset and examples also.
																									A      						=    self.old_features['features']
																									A      						=    self.old_features.get('dataset'						, __UpperCamelCase       )
																									A      						=    self.old_features.get('examples'						, __UpperCamelCase       )
																									logger.info(
																									    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						, time.time() - start       )

																									if self.dataset is None or self.examples is None:
																																logger.warning(
																																    f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
																																    ' future run'       )
																		else:
																									if mode == Split.dev:
																																A      						=    self.processor.get_dev_examples(args.data_dir       )
																									else:
																																A      						=    self.processor.get_train_examples(args.data_dir       )

																									A						,   A      						=    squad_convert_examples_to_features(
																									    examples=self.examples						, tokenizer=__UpperCamelCase						, max_seq_length=args.max_seq_length						, doc_stride=args.doc_stride						, max_query_length=args.max_query_length						, is_training=mode == Split.train						, threads=args.threads						, return_dataset=__UpperCamelCase						, )

																									A      						=    time.time()
																									torch.save(
																									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						, __UpperCamelCase						, )
																									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																									logger.info(
																									    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''       )


				def __len__(							self      :  Optional[Any]       )		->     Tuple:
											return len(self.features       )


				def __getitem__(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     Dict[str, torch.Tensor]:
											# Convert to Tensors and build dataset
											A      						=    self.features[i]

											A      						=    torch.tensor(feature.input_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.attention_mask						, dtype=torch.long       )
											A      						=    torch.tensor(feature.token_type_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.cls_index						, dtype=torch.long       )
											A      						=    torch.tensor(feature.p_mask						, dtype=torch.float       )
											A      						=    torch.tensor(feature.is_impossible						, dtype=torch.float       )

											A      						=    {
											    'input_ids': input_ids,
											    'attention_mask': attention_mask,
											    'token_type_ids': token_type_ids,
											}

											if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
																		del inputs["token_type_ids"]

											if self.args.model_type in ["xlnet", "xlm"]:
																		inputs.update({'cls_index': cls_index, 'p_mask': p_mask}       )
																		if self.args.version_2_with_negative:
																									inputs.update({'is_impossible': is_impossible}       )
																		if self.is_language_sensitive:
																									inputs.update({'langs': (torch.ones(input_ids.shape						, dtype=torch.intaa       ) * self.args.lang_id)}       )

											if self.mode == Split.train:
																		A      						=    torch.tensor(feature.start_position						, dtype=torch.long       )
																		A      						=    torch.tensor(feature.end_position						, dtype=torch.long       )
																		inputs.update({'start_positions': start_positions, 'end_positions': end_positions}       )

											return inputs