






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


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)

__UpperCamelCase      :      Optional[int]     						=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase      :      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"
        ),
    },
}
__UpperCamelCase      :      Any     						=						{
    "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"
        ),
    },
}
__UpperCamelCase      :      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"
        ),
    },
}

__UpperCamelCase      :      List[str]     						=						{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__UpperCamelCase      :      List[Any]     						=						{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__UpperCamelCase      :      Tuple     						=						{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


__UpperCamelCase      :      int     						=						{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCamelCase      :      Union[str, Any]     						=						{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCamelCase      :      List[str]     						=						{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class        __UpperCamelCase    (     __UpperCAmelCase   ):
	__snake_case	:Optional[Any]   =       VOCAB_FILES_NAMES
	__snake_case	:int   =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:Tuple   =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:List[str]   =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class        __UpperCamelCase    (     __UpperCAmelCase   ):
	__snake_case	:Optional[int]   =       VOCAB_FILES_NAMES
	__snake_case	:int   =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:List[str]   =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:int   =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

__UpperCamelCase      :      Optional[int]     						=						collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__UpperCamelCase      :      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(__UpperCAmelCase   )
class        __UpperCamelCase    :







	def __call__(  self      :							Any       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[str] = None       ,				_lowerCAmelCase      :							Optional[str] = None       ,				_lowerCAmelCase      :							Union[bool, str] = False       ,				_lowerCAmelCase      :							Union[bool, str] = False       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Optional[Union[str, TensorType]] = None       ,				_lowerCAmelCase      :							Optional[bool] = None       ,				**_lowerCAmelCase      :							List[str]       ,				)		-> Any:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							BatchEncoding       ,				_lowerCAmelCase      :							DPRReaderOutput       ,				_lowerCAmelCase      :							int = 16       ,				_lowerCAmelCase      :							int = 64       ,				_lowerCAmelCase      :							int = 4       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase			       =  reader_input["""input_ids"""]
		__lowercase       ,						__lowercase       ,						__lowercase			       =  reader_output[:3]
		__lowercase			       =  len(lowerCAmelCase_			)
		__lowercase			       =  sorted(range(lowerCAmelCase_			)       ,				reverse=lowerCAmelCase_       ,				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(lowerCAmelCase_			)

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







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							List[int]       ,				_lowerCAmelCase      :							List[int]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				)		-> Dict:
		"""simple docstring"""







		__lowercase			       =  []
		for start_index, start_score in enumerate(lowerCAmelCase_			):
			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(lowerCAmelCase_       ,				key=lambda _lowerCAmelCase			: x[1]       ,				reverse=lowerCAmelCase_			)
		__lowercase			       =  []
		for (start_index, end_index), score in scores:
			if start_index > end_index:
				raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'			)
			__lowercase			       =  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(lowerCAmelCase_			) == top_spans:
				break
		return chosen_span_intervals


@add_end_docstrings(__UpperCAmelCase   )
class        __UpperCamelCase    (     __UpperCAmelCase   ,			__UpperCAmelCase   ):
	__snake_case	:Tuple   =       VOCAB_FILES_NAMES
	__snake_case	:Tuple   =       READER_PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:Dict   =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:Union[str, Any]   =       READER_PRETRAINED_INIT_CONFIGURATION
	__snake_case	:Dict   =       ['input_ids', 'attention_mask']









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

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

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:str   =       field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()   )}   )
	__snake_case	:str   =       field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
	__snake_case	: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.'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.task_name.lower()


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       'train'
	__snake_case	:int   =       'dev'
	__snake_case	:Any   =       'test'


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:GlueDataTrainingArguments
	__snake_case	:str
	__snake_case	:List[InputFeatures]







	def __init__(  self      :							Dict       ,				_lowerCAmelCase      :							GlueDataTrainingArguments       ,				_lowerCAmelCase      :							PreTrainedTokenizerBase       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Union[str, Split] = Split.train       ,				_lowerCAmelCase      :							Optional[str] = None       ,				)		-> List[Any]:
		"""simple docstring"""







		warnings.warn(
		    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
		    """library. You can have a look at this example script for pointers: """
		    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""       ,				_lowerCAmelCase       ,				)
		__lowercase             =  args
		__lowercase             =  glue_processors[args.task_name]()
		__lowercase             =  glue_output_modes[args.task_name]
		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			try:
				__lowercase             =  Split[mode]
			except KeyError:
				raise KeyError("""mode is not a valid split name"""			)
        # Load data features from cache or dataset file
		__lowercase             =  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}_{args.task_name}'       ,				)
		__lowercase             =  self.processor.get_labels()
		if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
		    "RobertaTokenizer",
		    "RobertaTokenizerFast",
		    "XLMRobertaTokenizer",
		    "BartTokenizer",
		    "BartTokenizerFast",
		):
			# HACK(label indices are swapped in RoBERTa pretrained model)
			__lowercase       ,						__lowercase             =  label_list[2], label_list[1]
		__lowercase             =  label_list

		# Make sure only the first process in distributed training processes the dataset,
		# and the others will use the cache.
		__lowercase             =  cached_features_file + """.lock"""
		with FileLock(_lowerCAmelCase			):
			if os.path.exists(_lowerCAmelCase			) and not args.overwrite_cache:
				__lowercase             =  time.time()
				__lowercase             =  torch.load(_lowerCAmelCase			)
				logger.info(
				    F'Loading features from cached file {cached_features_file} [took %.3f s]'       ,				time.time() - start			)
			else:
				logger.info(F'Creating features from dataset file at {args.data_dir}'			)

				if mode == Split.dev:
					__lowercase             =  self.processor.get_dev_examples(args.data_dir			)
				elif mode == Split.test:
					__lowercase             =  self.processor.get_test_examples(args.data_dir			)
				else:
					__lowercase             =  self.processor.get_train_examples(args.data_dir			)
				if limit_length is not None:
					__lowercase             =  examples[:limit_length]
				__lowercase             =  glue_convert_examples_to_features(
				    _lowerCAmelCase       ,				_lowerCAmelCase       ,				max_length=args.max_seq_length       ,				label_list=_lowerCAmelCase       ,				output_mode=self.output_mode       ,				)
				__lowercase             =  time.time()
				torch.save(self.features       ,				_lowerCAmelCase			)
				# ^ 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      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		return len(self.features			)







	def __getitem__(  self      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]			)		-> InputFeatures:
		"""simple docstring"""







		return self.features[i]







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		return self.label_list

