






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)

__snake_case    :    List[Any]         =     _symbol_database.Default()


__snake_case    :    Tuple         =     _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"""
)

__snake_case    :    Any         =     globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
					__snake_case    :    Optional[Any]         =     None
					__snake_case    :    str         =     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"
					__snake_case    :    Any         =     45
					__snake_case    :    Any         =     15_81
					__snake_case    :    Tuple         =     15_17
					__snake_case    :    Union[str, Any]         =     15_70
					__snake_case    :    Any         =     15_84
					__snake_case    :    Any         =     17_93
					__snake_case    :    str         =     17_95
					__snake_case    :    str         =     19_16
					__snake_case    :    int         =     18_64
					__snake_case    :    List[Any]         =     19_05
					__snake_case    :    Union[str, Any]         =     19_19
					__snake_case    :    Any         =     24_29
					__snake_case    :    str         =     22_08
					__snake_case    :    Optional[int]         =     24_18
					__snake_case    :    str         =     23_23
					__snake_case    :    Optional[Any]         =     24_07
# @@protoc_insertion_point(module_scope)










import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


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


@dataclass
class     __SCREAMING_SNAKE_CASE					:
						_SCREAMING_SNAKE_CASE	:       str   =					field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default='''NER'''		,					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
						_SCREAMING_SNAKE_CASE	:       bool   =					field(default=__lowercase		,					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.
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,					)




@dataclass
class     __SCREAMING_SNAKE_CASE					:
						_SCREAMING_SNAKE_CASE	:       str   =					field(
						    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		,					)
						_SCREAMING_SNAKE_CASE	:       int   =					field(
						    default=128		,					metadata={
						        '''help''': (
						            '''The maximum total input sequence length after tokenization. Sequences longer '''
						            '''than this will be truncated, sequences shorter will be padded.'''
						        )
						    }		,					)
						_SCREAMING_SNAKE_CASE	:       bool   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})







def 	_UpperCamelCase   (    )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
						if len(sys.argv		) == 2 and sys.argv[1].endswith('.json'		):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						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.'		)

						lowerCAmelCase__    						=						import_module('tasks'		)
						try:
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       model_args.task_type		)
												lowerCAmelCase__    						=						token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    F"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
												    F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"		)

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    ,       datefmt='%m/%d/%Y %H:%M:%S'    ,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,       )
						logger.warning(
						    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'    ,       training_args.local_rank    ,       training_args.device    ,       training_args.n_gpu    ,       bool(training_args.local_rank != -1		)    ,       training_args.fpaa    ,       )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank		):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('Training/evaluation parameters %s'    ,       UpperCamelCase_		)

						# Set seed
						set_seed(training_args.seed		)

						# Prepare CONLL-2003 task
						lowerCAmelCase__    						=						token_classification_task.get_labels(data_args.labels		)
						lowerCAmelCase__    						=						dict(enumerate(UpperCamelCase_		)		)
						lowerCAmelCase__    						=						len(UpperCamelCase_		)

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

						lowerCAmelCase__    						=						AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,       num_labels=UpperCamelCase_    ,       idalabel=UpperCamelCase_    ,       labelaid={label: i for i, label in enumerate(UpperCamelCase_		)}    ,       cache_dir=model_args.cache_dir    ,       )
						lowerCAmelCase__    						=						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    ,       use_fast=model_args.use_fast    ,       )
						lowerCAmelCase__    						=						AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path    ,       from_tf=bool('.ckpt' in model_args.model_name_or_path		)    ,       config=UpperCamelCase_    ,       cache_dir=model_args.cache_dir    ,       )

						# Get datasets
						lowerCAmelCase__    						=						(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       model_type=config.model_type    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.train    ,       )
						    if training_args.do_train
						    else None
						)
						lowerCAmelCase__    						=						(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       model_type=config.model_type    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.dev    ,       )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					np.ndarray		) -> Tuple[List[int], List[int]]:
												lowerCAmelCase__    						=						np.argmax(UpperCamelCase_    ,       axis=2		)

												lowerCAmelCase__						,       lowerCAmelCase__    						=						preds.shape

												lowerCAmelCase__    						=						[[] for _ in range(UpperCamelCase_		)]
												lowerCAmelCase__    						=						[[] for _ in range(UpperCamelCase_		)]

												for i in range(UpperCamelCase_		):
																		for j in range(UpperCamelCase_		):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]		)
																														preds_list[i].append(label_map[preds[i][j]]		)

												return preds_list, out_label_list

						def compute_metrics(UpperCamelCase_       :					EvalPrediction		) -> Dict:
												lowerCAmelCase__						,       lowerCAmelCase__    						=						align_predictions(p.predictions    ,       p.label_ids		)
												return {
												    "accuracy_score": accuracy_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "precision": precision_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "recall": recall_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "f1": fa_score(UpperCamelCase_    ,       UpperCamelCase_		),
												}

						# Data collator
						lowerCAmelCase__    						=						DataCollatorWithPadding(UpperCamelCase_    ,       pad_to_multiple_of=8		) if training_args.fpaa else None

						# Initialize our Trainer
						lowerCAmelCase__    						=						Trainer(
						    model=UpperCamelCase_    ,       args=UpperCamelCase_    ,       train_dataset=UpperCamelCase_    ,       eval_dataset=UpperCamelCase_    ,       compute_metrics=UpperCamelCase_    ,       data_collator=UpperCamelCase_    ,       )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
						lowerCAmelCase__    						=						{}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'		)

												lowerCAmelCase__    						=						trainer.evaluate()

												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'eval_results.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								logger.info('***** Eval results *****'		)
																								for key, value in result.items():
																														logger.info('  %s = %s'    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																														writer.write('%s = %s\n' % (key, value)		)

																		results.update(UpperCamelCase_		)

    # Predict
						if training_args.do_predict:
												lowerCAmelCase__    						=						TokenClassificationDataset(
												    token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       model_type=config.model_type    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.test    ,       )

												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						trainer.predict(UpperCamelCase_		)
												lowerCAmelCase__						,       lowerCAmelCase__    						=						align_predictions(UpperCamelCase_    ,       UpperCamelCase_		)

												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'test_results.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								for key, value in metrics.items():
																														logger.info('  %s = %s'    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																														writer.write('%s = %s\n' % (key, value)		)

        # Save predictions
												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'test_predictions.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								with open(os.path.join(data_args.data_dir    ,       'test.txt'		)    ,       'r'		) as f:
																														token_classification_task.write_predictions_to_file(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						return results







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict		)  ->   Union[str, Any]:

						"""simple docstring"""



						main()


if __name__ == "__main__":
					main()





