



'''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 						__UpperCamelCase				(							) ->			List[str]:
	_lowercase					:       int       =		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()


_A				:    Optional[Any]     						=load('''accuracy''')







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	_lowercase  ,    _lowercase					:       List[str]       =		eval_pred
	_lowercase					:       Dict       =		np.argmax(_lowercase,				axis=1					)
	return metric.compute(predictions=_lowercase,				references=_lowercase					)



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	str     ,  UpperCamelCase_				:	int					)  ->       None:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Dict       =		trainer




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	int     ,  **UpperCamelCase_				:	Any					)  ->       Dict:



						'''simple docstring'''





						if control.should_evaluate:
							_lowercase					:       Union[str, Any]       =		deepcopy(UpperCamelCase_					)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,  metric_key_prefix='train'					)
							return control_copy







def 						__UpperCamelCase				(							) ->			Optional[Any]:
	_lowercase					:       Any       =		get_args()
	set_seed(args.seed					)

	_lowercase					:       Dict       =		load_dataset('codeparrot/codecomplex',				split='train'					)
	_lowercase					:       int       =		dataset.train_test_split(test_size=0.2					)
	_lowercase					:       Union[str, Any]       =		train_test['test'].train_test_split(test_size=0.5					)
	_lowercase					:       Dict       =		DatasetDict(
	    {
	        'train': train_test['train'],
	        'test': test_validation['train'],
	        'valid': test_validation['test'],
	    }					)

	print('Loading tokenizer and model'					)
	_lowercase					:       Optional[int]       =		AutoTokenizer.from_pretrained(args.model_ckpt					)
	_lowercase					:       Tuple       =		tokenizer.eos_token
	_lowercase					:       Tuple       =		AutoModelForSequenceClassification.from_pretrained(args.model_ckpt,				num_labels=7					)
	_lowercase					:       Union[str, Any]       =		model.config.eos_token_id

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

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

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

	_lowercase					:       List[str]       =		train_test_validation.map(
	    _lowercase,				batched=_lowercase,				remove_columns=train_test_validation['train'].column_names,				)
	_lowercase					:       Any       =		DataCollatorWithPadding(tokenizer=_lowercase					)

	_lowercase					:       Any       =		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',				)

	_lowercase					:       Union[str, Any]       =		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'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A				:    int     						=logging.get_logger(__name__)

_A				:    Union[str, Any]     						={
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_vision_model"""




					def __init__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	str=1408     ,  UpperCamelCase_				:	Tuple=6144     ,  UpperCamelCase_				:	Union[str, Any]=39     ,  UpperCamelCase_				:	Optional[Any]=16     ,  UpperCamelCase_				:	str=224     ,  UpperCamelCase_				:	Dict=14     ,  UpperCamelCase_				:	Dict="gelu"     ,  UpperCamelCase_				:	int=1E-6     ,  UpperCamelCase_				:	int=0.0     ,  UpperCamelCase_				:	List[str]=1E-10     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		num_hidden_layers
						_lowercase					:       str       =		num_attention_heads
						_lowercase					:       Tuple       =		patch_size
						_lowercase					:       Dict       =		image_size
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       List[Any]       =		attention_dropout
						_lowercase					:       int       =		layer_norm_eps
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       str       =		qkv_bias




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Tuple       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Any       =		config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls     ,  'model_type'					) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
							    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

						return cls.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_qformer"""




					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]=3_0522     ,  UpperCamelCase_				:	Union[str, Any]=768     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	Optional[Any]=12     ,  UpperCamelCase_				:	List[str]=3072     ,  UpperCamelCase_				:	List[str]="gelu"     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	List[str]=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	List[Any]=1E-12     ,  UpperCamelCase_				:	Optional[Any]=0     ,  UpperCamelCase_				:	str="absolute"     ,  UpperCamelCase_				:	List[Any]=2     ,  UpperCamelCase_				:	Any=1408     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





						super().__init__(pad_token_id=UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       List[Any]       =		num_attention_heads
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       Union[str, Any]       =		intermediate_size
						_lowercase					:       List[Any]       =		hidden_dropout_prob
						_lowercase					:       Dict       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       Tuple       =		layer_norm_eps
						_lowercase					:       List[str]       =		position_embedding_type
						_lowercase					:       str       =		cross_attention_frequency
						_lowercase					:       int       =		encoder_hidden_size




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       List[str]       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Optional[int]       =		config_dict['qformer_config']

						if "model_type" in config_dict and hasattr(cls     ,  'model_type'					) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
							    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

						return cls.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip"""
					A_						    =						True




					def __init__(  self				:	Any     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	Dict=None     ,  UpperCamelCase_				:	Union[str, Any]=32     ,  **UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						if vision_config is None:
							_lowercase					:       Any       =		{}
							logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

						if qformer_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

						if text_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

						_lowercase					:       List[Any]       =		InstructBlipVisionConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		InstructBlipQFormerConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		text_config['model_type'] if 'model_type' in text_config else 'opt'
						_lowercase					:       int       =		CONFIG_MAPPING[text_model_type](**UpperCamelCase_					)

						_lowercase					:       str       =		self.text_config.tie_word_embeddings
						_lowercase					:       int       =		self.text_config.is_encoder_decoder

						_lowercase					:       Tuple       =		num_query_tokens
						_lowercase					:       str       =		self.vision_config.hidden_size
						_lowercase					:       Union[str, Any]       =		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						_lowercase					:       List[Any]       =		1.0
						_lowercase					:       int       =		0.02




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Tuple     ,  UpperCamelCase_				:	InstructBlipVisionConfig     ,  UpperCamelCase_				:	InstructBlipQFormerConfig     ,  UpperCamelCase_				:	PretrainedConfig     ,  **UpperCamelCase_				:	Dict     ,  )  ->       List[str]:



						'''simple docstring'''





						return cls(
						    vision_config=vision_config.to_dict()     ,  qformer_config=qformer_config.to_dict()     ,  text_config=text_config.to_dict()     ,  **UpperCamelCase_     ,  )




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		copy.deepcopy(self.__dict__					)
						_lowercase					:       Optional[int]       =		self.vision_config.to_dict()
						_lowercase					:       Optional[Any]       =		self.qformer_config.to_dict()
						_lowercase					:       Tuple       =		self.text_config.to_dict()
						_lowercase					:       Dict       =		self.__class__.model_type
						return output
