




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 			__magic_name__				( ):






			'''simple docstring'''







			a    =			argparse.ArgumentParser()
			parser.add_argument("--model_ckpt",  type=A,  default="microsoft/unixcoder-base-nine"   )
			parser.add_argument("--num_epochs",  type=A,  default=5   )
			parser.add_argument("--batch_size",  type=A,  default=6   )
			parser.add_argument("--gradient_accumulation_steps",  type=A,  default=1   )
			parser.add_argument("--freeze",  type=A,  default=A   )
			parser.add_argument("--learning_rate",  type=A,  default=5E-4   )
			parser.add_argument("--seed",  type=A,  default=0   )
			parser.add_argument("--lr_scheduler_type",  type=A,  default="cosine"   )
			parser.add_argument("--num_warmup_steps",  type=A,  default=10   )
			parser.add_argument("--weight_decay",  type=A,  default=0.01   )
			parser.add_argument("--output_dir",  type=A,  default="./results"   )
			return parser.parse_args()


__lowerCAmelCase		:				List[str]       			=		load('accuracy')




def 			__magic_name__				( A		:					List[Any]   ):






			'''simple docstring'''







			a    ,		a    =			eval_pred
			a    =			np.argmax(A,  axis=1   )
			return metric.compute(predictions=A,  references=A   )




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Union[str, Any]							)					->     None:
							super().__init__()
							a    =			trainer





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     int					,      **__lowerCamelCase				:     int							)					->     Any:
							if control.should_evaluate:
										a    =			deepcopy(__lowerCamelCase							)
										self._trainer.evaluate(eval_dataset=self._trainer.train_dataset					,      metric_key_prefix="train"							)
										return control_copy




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			get_args()
			set_seed(args.seed   )

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

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

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

			a    =			ClassLabel(num_classes=7,  names=list(set(train_test_validation["train"]["complexity"]   )   )   )

			def tokenize(A		:					str   ):
						a    =			tokenizer(example["src"],  truncation=A,  max_length=1024   )
						a    =			labels.straint(example["complexity"]   )
						return {
						    "input_ids": inputs["input_ids"],
						    "attention_mask": inputs["attention_mask"],
						    "label": label,
						}

			a    =			train_test_validation.map(
			    A,  batched=A,  remove_columns=train_test_validation["train"].column_names,  )
			a    =			DataCollatorWithPadding(tokenizer=A   )

			a    =			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=0.01,  metric_for_best_model="accuracy",  run_name="complexity-java",  report_to="wandb",  )

			a    =			Trainer(
			    model=A,  args=A,  train_dataset=tokenized_datasets["train"],  eval_dataset=tokenized_datasets["valid"],  tokenizer=A,  data_collator=A,  compute_metrics=A,  )

			print("Training..."   )
			trainer.add_callback(CustomCallback(A   )   )
			trainer.train()


if __name__ == "__main__":
		main()












from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={"""help""": """The output directory where the model will be written."""}    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The encoder model checkpoint for weights initialization."""
				            """Don't set if you want to train an encoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The decoder model checkpoint for weights initialization."""
				            """Don't set if you want to train a decoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			HfArgumentParser((ModelArguments,)   )
			((a)    ,		)    =			parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

			# Use explicit specified encoder config
			if model_args.encoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.encoder_config_name   )
			# Use pretrained encoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

			# Use explicit specified decoder config
			if model_args.decoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.decoder_config_name   )
			# Use pretrained decoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

			# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
			a    =			True
			a    =			True

			a    =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
			    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,  decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,  encoder_config=A,  decoder_config=A,  )

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			a    =			decoder_config.decoder_start_token_id
			a    =			decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a    =			decoder_config.bos_token_id
			if pad_token_id is None:
						a    =			decoder_config.eos_token_id

			# This is necessary to make Flax's generate() work
			a    =			decoder_config.eos_token_id
			a    =			decoder_start_token_id
			a    =			pad_token_id

			a    =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

			a    =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path   )
			a    =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id   )

			model.save_pretrained(model_args.output_dir   )
			image_processor.save_pretrained(model_args.output_dir   )
			tokenizer.save_pretrained(model_args.output_dir   )


if __name__ == "__main__":
		main()






