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 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase = None							,			):







				"""simple docstring"""



				__lowercase 			=							{}

				if train_file is not None:
								__lowercase 			=							[train_file]
				if eval_file is not None:
								__lowercase 			=							[eval_file]
				if test_file is not None:
								__lowercase 			=							[test_file]

				__lowercase 			=							datasets.load_dataset('''csv'''							,			data_files=lowercase							)
				__lowercase 			=							list(ds[list(files.keys()							)[0]].features.keys()							)
				__lowercase 			=							features_name.pop(lowercase							)
				__lowercase 			=							list(set(ds[list(files.keys()							)[0]][label_name]							)							)
				__lowercase 			=							{label: i for i, label in enumerate(lowercase							)}
				__lowercase 			=							tokenizer.model_input_names
				__lowercase 			=							{}

				if len(lowercase							) == 1:
								for k in files.keys():
												__lowercase 			=							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():
												__lowercase 			=							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]:
												__lowercase 			=							{k: v for k, v in ex.items() if k in input_names}
												__lowercase 			=							labelaid[ex[label_name]]
												yield (d, label)

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

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

				__lowercase 			=							(
				    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:
								__lowercase 			=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

				__lowercase 			=							(
				    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:
								__lowercase 			=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

				__lowercase 			=							(
				    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:
								__lowercase 			=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

				return train_ds, val_ds, test_ds, labelaid


__a  :     Tuple       =  logging.getLogger(__name__)




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : int			 =						field(metadata={'''help''': '''Which column contains the label'''} )
			__a  : str			 =						field(default=_UpperCAmelCase						,metadata={'''help''': '''The path of the training file'''} )
			__a  : Optional[str]			 =						field(default=_UpperCAmelCase						,metadata={'''help''': '''The path of the development file'''} )
			__a  : Optional[str]			 =						field(default=_UpperCAmelCase						,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=_UpperCAmelCase						,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=_UpperCAmelCase						,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
			__a  : Optional[str]			 =						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
			__a  : bool			 =						field(default=_UpperCAmelCase						,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=_UpperCAmelCase						,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
				__lowercase			,     __lowercase			,     __lowercase 			=							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.

				__lowercase 			=							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							,			)

				__lowercase			,     __lowercase			,     __lowercase			,     __lowercase 			=							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							,			)

				__lowercase 			=							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():
								__lowercase 			=							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:
								__lowercase 			=							np.argmax(p.predictions							,			axis=1							)

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

				# Initialize our Trainer
				__lowercase 			=							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
				__lowercase 			=							{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''							)
								__lowercase 			=							trainer.evaluate()
								__lowercase 			=							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()
import warnings
from functools import wraps
from typing import Callable





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				@wraps(lowercase							)
				def _inner_fn(*lowercase							,			**lowercase							):
								warnings.warn(
								    (F"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")							,			lowercase							,			)
								return fn(*lowercase							,			**lowercase							)

				return _inner_fn