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							():

					'''simple docstring'''





					SCREAMING_SNAKE_CASE    			=       argparse.ArgumentParser()
					parser.add_argument("--model_ckpt"      , type=UpperCAmelCase__      , default="microsoft/unixcoder-base-nine"  )
					parser.add_argument("--num_epochs"      , type=UpperCAmelCase__      , default=5  )
					parser.add_argument("--batch_size"      , type=UpperCAmelCase__      , default=6  )
					parser.add_argument("--gradient_accumulation_steps"      , type=UpperCAmelCase__      , default=1  )
					parser.add_argument("--freeze"      , type=UpperCAmelCase__      , default=UpperCAmelCase__  )
					parser.add_argument("--learning_rate"      , type=UpperCAmelCase__      , default=5e-4  )
					parser.add_argument("--seed"      , type=UpperCAmelCase__      , default=0  )
					parser.add_argument("--lr_scheduler_type"      , type=UpperCAmelCase__      , default="cosine"  )
					parser.add_argument("--num_warmup_steps"      , type=UpperCAmelCase__      , default=1_0  )
					parser.add_argument("--weight_decay"      , type=UpperCAmelCase__      , default=0.01  )
					parser.add_argument("--output_dir"      , type=UpperCAmelCase__      , default="./results"  )
					return parser.parse_args()


_lowerCamelCase					:   str							=		load('''accuracy''')





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any  ):

					'''simple docstring'''





					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       eval_pred
					SCREAMING_SNAKE_CASE    			=       np.argmax(UpperCAmelCase__      , axis=1  )
					return metric.compute(predictions=UpperCAmelCase__      , references=UpperCAmelCase__  )


class 						lowercase    (     a     ):




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]      )					->				None:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE    			=       trainer





							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Dict	,							**_UpperCamelCase   :					Union[str, Any]      )					->				Dict:



												'''simple docstring'''

												if control.should_evaluate:
																	SCREAMING_SNAKE_CASE    			=       deepcopy(_UpperCamelCase      )
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,							metric_key_prefix="train"      )
																	return control_copy





def 	__lowerCamelCase							():

					'''simple docstring'''





					SCREAMING_SNAKE_CASE    			=       get_args()
					set_seed(args.seed  )

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

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

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

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

					def tokenize(UpperCAmelCase__     :							List[Any]  ):
										SCREAMING_SNAKE_CASE    			=       tokenizer(example["src"]      , truncation=UpperCAmelCase__      , max_length=1_0_2_4  )
										SCREAMING_SNAKE_CASE    			=       labels.straint(example["complexity"]  )
										return {
										    "input_ids": inputs["input_ids"],
										    "attention_mask": inputs["attention_mask"],
										    "label": label,
										}

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

					SCREAMING_SNAKE_CASE    			=       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"      , )

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

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


if __name__ == "__main__":
							main()





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					str=18	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Optional[int]=400	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[int]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					List[str]=[0.5, 0.5, 0.5]	,							)					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 18, "width": 18}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std





							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							DPTImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DPTImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"height": 18, "width": 18}      )

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict	,							size=42      )
												self.assertEqual(image_processor.size	,							{"height": 42, "width": 42}      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							Image.Image      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)




							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							np.ndarray      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)





							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							torch.Tensor      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)




