
'''simple docstring'''


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 lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase = None			,       )     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		{}

					if train_file is not None:
										lowerCamelCase_					:      int     =		[train_file]
					if eval_file is not None:
										lowerCamelCase_					:      Union[str, Any]     =		[eval_file]
					if test_file is not None:
										lowerCamelCase_					:      str     =		[test_file]

					lowerCamelCase_					:      Any     =		datasets.load_dataset('''csv'''			,       data_files=_lowercase				)
					lowerCamelCase_					:      List[str]     =		list(ds[list(files.keys()				)[0]].features.keys()				)
					lowerCamelCase_					:      Any     =		features_name.pop(_lowercase				)
					lowerCamelCase_					:      Optional[Any]     =		list(set(ds[list(files.keys()				)[0]][label_name]				)				)
					lowerCamelCase_					:      List[str]     =		{label: i for i, label in enumerate(_lowercase				)}
					lowerCamelCase_					:      Dict     =		tokenizer.model_input_names
					lowerCamelCase_					:      Optional[Any]     =		{}

					if len(_lowercase				) == 1:
										for k in files.keys():
															lowerCamelCase_					:      str     =		ds[k].map(
															    lambda _lowercase				: tokenizer.batch_encode_plus(
															        example[features_name[0]]			,       truncation=_lowercase			,       max_length=_lowercase			,       padding='''max_length'''				)			,       batched=_lowercase			,       )
					elif len(_lowercase				) == 2:
										for k in files.keys():
															lowerCamelCase_					:      int     =		ds[k].map(
															    lambda _lowercase				: tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])			,       truncation=_lowercase			,       max_length=_lowercase			,       padding='''max_length'''			,       )			,       batched=_lowercase			,       )

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

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

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

					lowerCamelCase_					:      List[Any]     =		(
					    tf.data.Dataset.from_generator(
					        _lowercase			,       ({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:
										lowerCamelCase_					:      Any     =		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

					lowerCamelCase_					:      Optional[int]     =		(
					    tf.data.Dataset.from_generator(
					        _lowercase			,       ({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:
										lowerCamelCase_					:      List[str]     =		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

					lowerCamelCase_					:      List[Any]     =		(
					    tf.data.Dataset.from_generator(
					        _lowercase			,       ({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:
										lowerCamelCase_					:      Dict     =		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

					return train_ds, val_ds, test_ds, labelaid


__lowercase							:   List[str]					=     logging.getLogger(__name__)

@dataclass
class 	__lowercase				:
			lowerCamelCase			:		int	      =			field(metadata={"help": "Which column contains the label"}    )
			lowerCamelCase			:		str	      =			field(default=_lowercase      ,     metadata={"help": "The path of the training file"}    )
			lowerCamelCase			:		Optional[str]	      =			field(default=_lowercase      ,     metadata={"help": "The path of the development file"}    )
			lowerCamelCase			:		Optional[str]	      =			field(default=_lowercase      ,     metadata={"help": "The path of the test file"}    )
			lowerCamelCase			:		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."
			        )
			    }      ,     )
			lowerCamelCase			:		bool	      =			field(
			    default=_lowercase      ,     metadata={"help": "Overwrite the cached training and evaluation sets"}    )

@dataclass
class 	__lowercase				:
			lowerCamelCase			:		str	      =			field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
			lowerCamelCase			:		Optional[str]	      =			field(
			    default=_lowercase      ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
			lowerCamelCase			:		Optional[str]	      =			field(
			    default=_lowercase      ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
			lowerCamelCase			:		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.
			lowerCamelCase			:		Optional[str]	      =			field(
			    default=_lowercase      ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,     )




def lowercase_	(   )     ->							Any:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
					lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      int     =		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.

					lowerCamelCase_					:      Dict     =		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			,       )

					lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		get_tfds(
					    train_file=data_args.train_file			,       eval_file=data_args.dev_file			,       test_file=data_args.test_file			,       tokenizer=_lowercase			,       label_column_id=data_args.label_column_id			,       max_seq_length=data_args.max_seq_length			,       )

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

					with training_args.strategy.scope():
										lowerCamelCase_					:      str     =		TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path			,       from_pt=bool('''.bin''' in model_args.model_name_or_path				)			,       config=_lowercase			,       cache_dir=model_args.cache_dir			,       )

					def compute_metrics(_lowercase				) -> Dict:
										lowerCamelCase_					:      Optional[Any]     =		np.argmax(p.predictions			,       axis=1				)

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

					# Initialize our Trainer
					lowerCamelCase_					:      Optional[int]     =		TFTrainer(
					    model=_lowercase			,       args=_lowercase			,       train_dataset=_lowercase			,       eval_dataset=_lowercase			,       compute_metrics=_lowercase			,       )

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

					# Evaluation
					lowerCamelCase_					:      str     =		{}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''				)
										lowerCamelCase_					:      Tuple     =		trainer.evaluate()
										lowerCamelCase_					:      Dict     =		os.path.join(training_args.output_dir			,       '''eval_results.txt'''				)

										with open(_lowercase			,       '''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(_lowercase				)

					return results


if __name__ == "__main__":
					main()




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   int					=     {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   Any					=     ['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


