



'''simple docstring'''






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase__:     Dict										=    [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
						import run_clm_flax
						import run_flax_glue
						import run_flax_ner
						import run_mlm_flax
						import run_qa
						import run_summarization_flax
						import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase__:     List[Any]										=    logging.getLogger()


def 				snake_case_  (      )   ->     Tuple:
							UpperCAmelCase				: List[Any]											=	argparse.ArgumentParser()
							parser.add_argument('''-f'''						)
							UpperCAmelCase				: List[str]											=	parser.parse_args()
							return args.f


def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Union[str, Any]="eval"						)   ->     str:
							UpperCAmelCase				: Optional[Any]											=	os.path.join(_lowerCAmelCase     ,   f"""{split}_results.json"""						)
							if os.path.exists(_lowerCAmelCase						):
														with open(_lowerCAmelCase     ,   '''r'''						) as f:
																					return json.load(_lowerCAmelCase						)
							raise ValueError(f"""can't find {path}"""						)


UpperCamelCase__:     List[str]										=    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def   A			(		self    :  Dict	)    ->	Optional[int]:
								UpperCAmelCase				: Optional[int]											=	self.get_auto_remove_tmp_dir()
								UpperCAmelCase				: int											=	F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(__snake_case	,    '''argv'''	,    __snake_case	):
															run_flax_glue.main()
															UpperCAmelCase				: Any											=	get_results(__snake_case	)
															self.assertGreaterEqual(result['''eval_accuracy''']	,    0.75	)







	@slow
	def   A			(		self    :  Optional[int]	)    ->	Optional[Any]:
								UpperCAmelCase				: int											=	self.get_auto_remove_tmp_dir()
								UpperCAmelCase				: str											=	F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

								with patch.object(__snake_case	,    '''argv'''	,    __snake_case	):
															run_clm_flax.main()
															UpperCAmelCase				: Dict											=	get_results(__snake_case	)
															self.assertLess(result['''eval_perplexity''']	,    100	)







	@slow
	def   A			(		self    :  int	)    ->	Any:
								UpperCAmelCase				: Optional[Any]											=	self.get_auto_remove_tmp_dir()
								UpperCAmelCase				: Tuple											=	F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

								with patch.object(__snake_case	,    '''argv'''	,    __snake_case	):
															run_summarization_flax.main()
															UpperCAmelCase				: Optional[Any]											=	get_results(__snake_case	,    split='''test'''	)
															self.assertGreaterEqual(result['''test_rouge1''']	,    10	)
															self.assertGreaterEqual(result['''test_rouge2''']	,    2	)
															self.assertGreaterEqual(result['''test_rougeL''']	,    7	)
															self.assertGreaterEqual(result['''test_rougeLsum''']	,    7	)







	@slow
	def   A			(		self    :  List[str]	)    ->	Union[str, Any]:
								UpperCAmelCase				: str											=	self.get_auto_remove_tmp_dir()
								UpperCAmelCase				: List[str]											=	F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

								with patch.object(__snake_case	,    '''argv'''	,    __snake_case	):
															run_mlm_flax.main()
															UpperCAmelCase				: str											=	get_results(__snake_case	)
															self.assertLess(result['''eval_perplexity''']	,    42	)







	@slow
	def   A			(		self    :  Any	)    ->	Optional[Any]:
								UpperCAmelCase				: str											=	self.get_auto_remove_tmp_dir()
								UpperCAmelCase				: Optional[Any]											=	F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

								with patch.object(__snake_case	,    '''argv'''	,    __snake_case	):
															run_ta_mlm_flax.main()
															UpperCAmelCase				: str											=	get_results(__snake_case	)
															self.assertGreaterEqual(result['''eval_accuracy''']	,    0.42	)







	@slow
	def   A			(		self    :  Any	)    ->	Union[str, Any]:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								UpperCAmelCase				: Dict											=	7 if get_gpu_count() > 1 else 2

								UpperCAmelCase				: int											=	self.get_auto_remove_tmp_dir()
								UpperCAmelCase				: List[Any]											=	F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

								with patch.object(__snake_case	,    '''argv'''	,    __snake_case	):
															run_flax_ner.main()
															UpperCAmelCase				: Any											=	get_results(__snake_case	)
															self.assertGreaterEqual(result['''eval_accuracy''']	,    0.75	)
															self.assertGreaterEqual(result['''eval_f1''']	,    0.3	)







	@slow
	def   A			(		self    :  Tuple	)    ->	Tuple:
								UpperCAmelCase				: List[Any]											=	self.get_auto_remove_tmp_dir()
								UpperCAmelCase				: Optional[int]											=	F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

								with patch.object(__snake_case	,    '''argv'''	,    __snake_case	):
															run_qa.main()
															UpperCAmelCase				: List[str]											=	get_results(__snake_case	)
															self.assertGreaterEqual(result['''eval_f1''']	,    30	)
															self.assertGreaterEqual(result['''eval_exact''']	,    30	)










'''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 				snake_case_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Any											=	argparse.ArgumentParser()
							parser.add_argument('''--model_ckpt'''     ,   type=_lowerCAmelCase     ,   default='''microsoft/unixcoder-base-nine'''						)
							parser.add_argument('''--num_epochs'''     ,   type=_lowerCAmelCase     ,   default=5						)
							parser.add_argument('''--batch_size'''     ,   type=_lowerCAmelCase     ,   default=6						)
							parser.add_argument('''--gradient_accumulation_steps'''     ,   type=_lowerCAmelCase     ,   default=1						)
							parser.add_argument('''--freeze'''     ,   type=_lowerCAmelCase     ,   default=_lowerCAmelCase						)
							parser.add_argument('''--learning_rate'''     ,   type=_lowerCAmelCase     ,   default=5e-4						)
							parser.add_argument('''--seed'''     ,   type=_lowerCAmelCase     ,   default=0						)
							parser.add_argument('''--lr_scheduler_type'''     ,   type=_lowerCAmelCase     ,   default='''cosine'''						)
							parser.add_argument('''--num_warmup_steps'''     ,   type=_lowerCAmelCase     ,   default=10						)
							parser.add_argument('''--weight_decay'''     ,   type=_lowerCAmelCase     ,   default=0.0_1						)
							parser.add_argument('''--output_dir'''     ,   type=_lowerCAmelCase     ,   default='''./results'''						)
							return parser.parse_args()


UpperCamelCase__:     Any										=    load("accuracy")


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     int:
							UpperCAmelCase							,	UpperCAmelCase				: Dict											=	eval_pred
							UpperCAmelCase				: Optional[Any]											=	np.argmax(_lowerCAmelCase     ,   axis=1						)
							return metric.compute(predictions=_lowerCAmelCase     ,   references=_lowerCAmelCase						)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  Optional[int]	)    ->	None:
								super().__init__()
								UpperCAmelCase				: Dict											=	trainer







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    **__snake_case    :  List[Any]	)    ->	Dict:
								if control.should_evaluate:
															UpperCAmelCase				: Optional[int]											=	deepcopy(__snake_case	)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,    metric_key_prefix='''train'''	)
															return control_copy


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	get_args()
							set_seed(args.seed						)

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

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

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

							UpperCAmelCase				: Optional[Any]											=	ClassLabel(num_classes=7     ,   names=list(set(train_test_validation['''train''']['''complexity''']						)						)						)

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

							UpperCAmelCase				: List[str]											=	train_test_validation.map(
							    _lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   remove_columns=train_test_validation['''train'''].column_names     ,   )
							UpperCAmelCase				: int											=	DataCollatorWithPadding(tokenizer=_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	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'''     ,   )

							UpperCAmelCase				: int											=	Trainer(
							    model=_lowerCAmelCase     ,   args=_lowerCAmelCase     ,   train_dataset=tokenized_datasets['''train''']     ,   eval_dataset=tokenized_datasets['''valid''']     ,   tokenizer=_lowerCAmelCase     ,   data_collator=_lowerCAmelCase     ,   compute_metrics=_lowerCAmelCase     ,   )

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


if __name__ == "__main__":
						main()





