



'''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 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   PreTrainedTokenizer       ,      _lowerCamelCase:   int       ,      _lowerCamelCase:   Optional[int] = None       ,      ):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		{}

		if train_file is not None:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		[train_file]
		if eval_file is not None:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		[eval_file]
		if test_file is not None:
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[test_file]

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

		if len(_lowerCamelCase					) == 1:
				for k in files.keys():
						__SCREAMING_SNAKE_CASE       :		List[Any]           =		ds[k].map(
						    lambda _lowerCamelCase					: tokenizer.batch_encode_plus(
						        example[features_name[0]]       ,      truncation=_lowerCamelCase       ,      max_length=_lowerCamelCase       ,      padding="""max_length"""					)       ,      batched=_lowerCamelCase       ,      )
		elif len(_lowerCamelCase					) == 2:
				for k in files.keys():
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		ds[k].map(
						    lambda _lowerCamelCase					: tokenizer.batch_encode_plus(
						        (example[features_name[0]], example[features_name[1]])       ,      truncation=_lowerCamelCase       ,      max_length=_lowerCamelCase       ,      padding="""max_length"""       ,      )       ,      batched=_lowerCamelCase       ,      )

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

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

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

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(
		    tf.data.Dataset.from_generator(
		        _lowerCamelCase       ,      ({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:
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]					)					)					)

		__SCREAMING_SNAKE_CASE       :		Any           =		(
		    tf.data.Dataset.from_generator(
		        _lowerCamelCase       ,      ({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:
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]					)					)					)

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

		return train_ds, val_ds, test_ds, labelaid


UpperCamelCase__       :	str    = logging.getLogger(__name__)






@dataclass
class 						_UpperCamelCase  :


			'''simple docstring'''

			_A      :		int			    =		field(metadata={'''help''': '''Which column contains the label'''}    )
			_A      :		str			    =		field(default=lowerCamelCase__    ,					metadata={'''help''': '''The path of the training file'''}    )
			_A      :		Optional[str]			    =		field(default=lowerCamelCase__    ,					metadata={'''help''': '''The path of the development file'''}    )
			_A      :		Optional[str]			    =		field(default=lowerCamelCase__    ,					metadata={'''help''': '''The path of the test file'''}    )
			_A      :		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.'''
			        )
			    }    ,					)
			_A      :		bool			    =		field(
			    default=lowerCamelCase__    ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )






@dataclass
class 						_UpperCamelCase  :


			'''simple docstring'''

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


def 				lowerCAmelCase_   (	):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Any           =		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.

		__SCREAMING_SNAKE_CASE       :		int           =		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       ,      )

		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[str]           =		get_tfds(
		    train_file=data_args.train_file       ,      eval_file=data_args.dev_file       ,      test_file=data_args.test_file       ,      tokenizer=_lowerCamelCase       ,      label_column_id=data_args.label_column_id       ,      max_seq_length=data_args.max_seq_length       ,      )

		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      num_labels=len(_lowerCamelCase					)       ,      labelaid=_lowerCamelCase       ,      idalabel={id: label for label, id in labelaid.items()}       ,      finetuning_task="""text-classification"""       ,      cache_dir=model_args.cache_dir       ,      )

		with training_args.strategy.scope():
				__SCREAMING_SNAKE_CASE       :		int           =		TFAutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path       ,      from_pt=bool(""".bin""" in model_args.model_name_or_path					)       ,      config=_lowerCamelCase       ,      cache_dir=model_args.cache_dir       ,      )

		def compute_metrics(_lowerCamelCase:   EvalPrediction					) -> Dict:
				__SCREAMING_SNAKE_CASE       :		Any           =		np.argmax(p.predictions       ,      axis=1					)

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

		# Initialize our Trainer
		__SCREAMING_SNAKE_CASE       :		int           =		TFTrainer(
		    model=_lowerCamelCase       ,      args=_lowerCamelCase       ,      train_dataset=_lowerCamelCase       ,      eval_dataset=_lowerCamelCase       ,      compute_metrics=_lowerCamelCase       ,      )

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

		# Evaluation
		__SCREAMING_SNAKE_CASE       :		Tuple           =		{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		trainer.evaluate()
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		os.path.join(training_args.output_dir       ,      """eval_results.txt"""					)

				with open(_lowerCamelCase       ,      """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(_lowerCamelCase					)

		return results


if __name__ == "__main__":
						main()




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		assert (
		    isinstance(_lowerCamelCase       ,      _lowerCamelCase					) and number_of_steps > 0
		), F"number_of_steps needs to be positive integer, your input {number_of_steps}"
		if number_of_steps == 1:
				return 1
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		1, 1
		for _ in range(number_of_steps - 1					):
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		current + previous, current
		return current


if __name__ == "__main__":
						import doctest

						doctest.testmod()