





from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

A_												=     _symbol_database.Default()


A_												=     _descriptor_pool.Default().AddSerializedFile(
    B"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)

A_												=     globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
						A_												=     None
						A_												=     B"H\003"
						# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
						A_												=     4_5
						A_												=     1_5_8_1
						A_												=     1_5_1_7
						A_												=     1_5_7_0
						A_												=     1_5_8_4
						A_												=     1_7_9_3
						A_												=     1_7_9_5
						A_												=     1_9_1_6
						A_												=     1_8_6_4
						A_												=     1_9_0_5
						A_												=     1_9_1_9
						A_												=     2_4_2_9
						A_												=     2_2_0_8
						A_												=     2_4_1_8
						A_												=     2_3_2_3
						A_												=     2_4_0_7
# @@protoc_insertion_point(module_scope)








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


A_												=     logging.get_logger(__name__)

A_												=     list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A_												=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "Model type selected in the list: " + ", ".join(lowercase_   )}   )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}   )
					A__				 =    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__				 =    field(
					    default=1_2_8							,       metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}							,       )
					A__				 =    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__				 =    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__				 =    field(
					    default=lowercase_							,       metadata={"help": "Overwrite the cached training and evaluation sets"}   )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}   )
					A__				 =    field(
					    default=0.0							,       metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}   )
					A__				 =    field(
					    default=2_0							,       metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}   )
					A__				 =    field(
					    default=0							,       metadata={
					        "help": (
					            "language id of input for language-specific xlm models (see"
					            " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
					        )
					    }							,       )
					A__				 =    field(default=1							,       metadata={"help": "multiple threads for converting example to features"}   )

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "train"
					A__				 =    "dev"

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    42
					A__				 =    42
					A__				 =    42
					A__				 =    42





					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = Split.train	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  _lowerCAmelCase = "pt"	,  ):
										lowerCamelCase__       =       args
										lowerCamelCase__       =       is_language_sensitive
										lowerCamelCase__       =       SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
										if isinstance(_lowerCAmelCase	,  _lowerCAmelCase	):
															try:
																				lowerCamelCase__       =       Split[mode]
															except KeyError:
																				raise KeyError("mode is not a valid split name"	)
										lowerCamelCase__       =       mode
										# Load data features from cache or dataset file
										lowerCamelCase__       =       "v2" if args.version_2_with_negative else "v1"
										lowerCamelCase__       =       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.
										lowerCamelCase__       =       cached_features_file + ".lock"
										with FileLock(_lowerCAmelCase	):
															if os.path.exists(_lowerCAmelCase	) and not args.overwrite_cache:
																				lowerCamelCase__       =       time.time()
																				lowerCamelCase__       =       torch.load(_lowerCAmelCase	)

																				# Legacy cache files have only features, while new cache files
																				# will have dataset and examples also.
																				lowerCamelCase__       =       self.old_features["features"]
																				lowerCamelCase__       =       self.old_features.get("dataset"	,  _lowerCAmelCase	)
																				lowerCamelCase__       =       self.old_features.get("examples"	,  _lowerCAmelCase	)
																				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:
																									lowerCamelCase__       =       self.processor.get_dev_examples(args.data_dir	)
																				else:
																									lowerCamelCase__       =       self.processor.get_train_examples(args.data_dir	)

																				lowerCamelCase__    ,		lowerCamelCase__       =       squad_convert_examples_to_features(
																				    examples=self.examples	,  tokenizer=_lowerCAmelCase	,  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=_lowerCAmelCase	,  )

																				lowerCamelCase__       =       time.time()
																				torch.save(
																				    {"features": self.features, "dataset": self.dataset, "examples": self.examples}	,  _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	):
										return len(self.features	)






					def __getitem__( self	,  _lowerCAmelCase	):
										# Convert to Tensors and build dataset
										lowerCamelCase__       =       self.features[i]

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

										lowerCamelCase__       =       {
										    "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:
															lowerCamelCase__       =       torch.tensor(feature.start_position	,  dtype=torch.long	)
															lowerCamelCase__       =       torch.tensor(feature.end_position	,  dtype=torch.long	)
															inputs.update({"start_positions": start_positions, "end_positions": end_positions}	)

										return inputs

