



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					, )	->    Dict:
					__lowerCAmelCase						 ={}

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

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

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

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

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

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

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

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

					return train_ds, val_ds, test_ds, labelaid


lowercase_													=      logging.getLogger(__name__)






@dataclass
class     __a			:
	SCREAMING_SNAKE_CASE  	=							field(metadata={"help": "Which column contains the label"}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "The path of the training file"}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "The path of the development file"}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "The path of the test file"}					)
	SCREAMING_SNAKE_CASE  	=							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  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Overwrite the cached training and evaluation sets"}					)






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






def        __lowerCAmelCase		(	)	->    Tuple:
					# 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.
					__lowerCAmelCase						 =HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
					__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."""						)

					# 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						 =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						 =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					, )

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

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

					# Initialize our Trainer
					__lowerCAmelCase						 =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
					__lowerCAmelCase						 ={}
					if training_args.do_eval:
										logger.info("""*** Evaluate ***"""						)
										__lowerCAmelCase						 =trainer.evaluate()
										__lowerCAmelCase						 =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()









def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int						)	->    bool:
					return numa ^ numa < 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




