



"""simple docstring"""
import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)



def        lowerCAmelCase_(   )	->       str:
				_lowerCamelCase        =				argparse.ArgumentParser()
				parser.add_argument('''--model_ckpt'''						,   type=lowercase_						,   default='''microsoft/unixcoder-base-nine'''							)
				parser.add_argument('''--num_epochs'''						,   type=lowercase_						,   default=5							)
				parser.add_argument('''--batch_size'''						,   type=lowercase_						,   default=6							)
				parser.add_argument('''--gradient_accumulation_steps'''						,   type=lowercase_						,   default=1							)
				parser.add_argument('''--freeze'''						,   type=lowercase_						,   default=lowercase_							)
				parser.add_argument('''--learning_rate'''						,   type=lowercase_						,   default=5e-4							)
				parser.add_argument('''--seed'''						,   type=lowercase_						,   default=0							)
				parser.add_argument('''--lr_scheduler_type'''						,   type=lowercase_						,   default='''cosine'''							)
				parser.add_argument('''--num_warmup_steps'''						,   type=lowercase_						,   default=10							)
				parser.add_argument('''--weight_decay'''						,   type=lowercase_						,   default=0.0_1							)
				parser.add_argument('''--output_dir'''						,   type=lowercase_						,   default='''./results'''							)
				return parser.parse_args()


__SCREAMING_SNAKE_CASE  :						Any    							=      load('''accuracy''')



def        lowerCAmelCase_(   lowercase_     :							str							)	->       str:
				_lowerCamelCase  ,     _lowerCamelCase        =				eval_pred
				_lowerCamelCase        =				np.argmax(lowercase_						,   axis=1							)
				return metric.compute(predictions=lowercase_						,   references=lowercase_							)


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									super().__init__()
									_lowerCamelCase        =				trainer





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ):
									if control.should_evaluate:
													_lowerCamelCase        =				deepcopy(lowerCamelCase__   )
													self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,       metric_key_prefix='''train'''   )
													return control_copy



def        lowerCAmelCase_(   )	->       Any:
				_lowerCamelCase        =				get_args()
				set_seed(args.seed							)

				_lowerCamelCase        =				load_dataset('''codeparrot/codecomplex'''						,   split='''train'''							)
				_lowerCamelCase        =				dataset.train_test_split(test_size=0.2							)
				_lowerCamelCase        =				train_test['''test'''].train_test_split(test_size=0.5							)
				_lowerCamelCase        =				DatasetDict(
				    {
				        '''train''': train_test['''train'''],
				        '''test''': test_validation['''train'''],
				        '''valid''': test_validation['''test'''],
				    }							)

				print('''Loading tokenizer and model'''							)
				_lowerCamelCase        =				AutoTokenizer.from_pretrained(args.model_ckpt							)
				_lowerCamelCase        =				tokenizer.eos_token
				_lowerCamelCase        =				AutoModelForSequenceClassification.from_pretrained(args.model_ckpt						,   num_labels=7							)
				_lowerCamelCase        =				model.config.eos_token_id

				if args.freeze:
								for param in model.roberta.parameters():
												_lowerCamelCase        =				False

				_lowerCamelCase        =				ClassLabel(num_classes=7						,   names=list(set(train_test_validation['''train''']['''complexity''']							)							)							)

				def tokenize(lowercase_     :							Union[str, Any]							):
								_lowerCamelCase        =				tokenizer(example['''src''']						,   truncation=lowercase_						,   max_length=10_24							)
								_lowerCamelCase        =				labels.straint(example['''complexity''']							)
								return {
								    "input_ids": inputs["input_ids"],
								    "attention_mask": inputs["attention_mask"],
								    "label": label,
								}

				_lowerCamelCase        =				train_test_validation.map(
				    lowercase_						,   batched=lowercase_						,   remove_columns=train_test_validation['''train'''].column_names						,   )
				_lowerCamelCase        =				DataCollatorWithPadding(tokenizer=lowercase_							)

				_lowerCamelCase        =				TrainingArguments(
				    output_dir=args.output_dir						,   learning_rate=args.learning_rate						,   lr_scheduler_type=args.lr_scheduler_type						,   evaluation_strategy='''epoch'''						,   save_strategy='''epoch'''						,   logging_strategy='''epoch'''						,   per_device_train_batch_size=args.batch_size						,   per_device_eval_batch_size=args.batch_size						,   num_train_epochs=args.num_epochs						,   gradient_accumulation_steps=args.gradient_accumulation_steps						,   weight_decay=0.0_1						,   metric_for_best_model='''accuracy'''						,   run_name='''complexity-java'''						,   report_to='''wandb'''						,   )

				_lowerCamelCase        =				Trainer(
				    model=lowercase_						,   args=lowercase_						,   train_dataset=tokenized_datasets['''train''']						,   eval_dataset=tokenized_datasets['''valid''']						,   tokenizer=lowercase_						,   data_collator=lowercase_						,   compute_metrics=lowercase_						,   )

				print('''Training...'''							)
				trainer.add_callback(CustomCallback(lowercase_							)							)
				trainer.train()


if __name__ == "__main__":
			main()





"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
			from .pipeline_kandinsky import KandinskyPipeline
			from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
			from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
			from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
			from .text_encoder import MultilingualCLIP
