






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


lowerCamelCase			:					Dict         =	load('''accuracy''')





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase			, __lowerCAmelCase								=					eval_pred
     __lowerCAmelCase								=					np.argmax(__snake_case					, axis=1       )
     return metric.compute(predictions=__snake_case					, references=__snake_case       )



class 							_UpperCamelCase			(lowercase_			):


      def __init__(	self					,						__UpperCamelCase	)->	Optional[int]:
           super().__init__()
           __lowerCAmelCase								=					trainer


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						**__UpperCamelCase	)->	int:







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





def       __lowerCAmelCase      (						):
     __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(__snake_case       ):
          __lowerCAmelCase								=					tokenizer(example["src"]					, truncation=__snake_case					, max_length=1024       )
          __lowerCAmelCase								=					labels.straint(example["complexity"]       )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

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

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

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

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


if __name__ == "__main__":
 main()











# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase			:					List[str]         =	'''Create a default config file for Accelerate with only a few flags set.'''





def       __lowerCAmelCase      (						__snake_case="no"					, __snake_case = default_json_config_file					, __snake_case = False       ):
     __lowerCAmelCase								=					Path(__snake_case       )
     path.parent.mkdir(parents=__snake_case					, exist_ok=__snake_case       )
     if path.exists():
          print(
              F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""       )
          return False
     __lowerCAmelCase								=					mixed_precision.lower()
     if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
          raise ValueError(
              F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""       )
     __lowerCAmelCase								=					{
         "compute_environment": "LOCAL_MACHINE",
         "mixed_precision": mixed_precision,
     }
     if torch.cuda.is_available():
          __lowerCAmelCase								=					torch.cuda.device_count()
          __lowerCAmelCase								=					num_gpus
          __lowerCAmelCase								=					False
          if num_gpus > 1:
               __lowerCAmelCase								=					"MULTI_GPU"
          else:
               __lowerCAmelCase								=					"NO"
     elif is_xpu_available() and use_xpu:
          __lowerCAmelCase								=					torch.xpu.device_count()
          __lowerCAmelCase								=					num_xpus
          __lowerCAmelCase								=					False
          if num_xpus > 1:
               __lowerCAmelCase								=					"MULTI_XPU"
          else:
               __lowerCAmelCase								=					"NO"
     elif is_npu_available():
          __lowerCAmelCase								=					torch.npu.device_count()
          __lowerCAmelCase								=					num_npus
          __lowerCAmelCase								=					False
          if num_npus > 1:
               __lowerCAmelCase								=					"MULTI_NPU"
          else:
               __lowerCAmelCase								=					"NO"
     else:
          __lowerCAmelCase								=					0
          __lowerCAmelCase								=					True
          __lowerCAmelCase								=					1
          __lowerCAmelCase								=					"NO"
     __lowerCAmelCase								=					ClusterConfig(**__snake_case       )
     config.to_json_file(__snake_case       )
     return path





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __lowerCAmelCase								=					parser.add_parser("default"					, parents=__snake_case					, help=__snake_case					, formatter_class=__snake_case       )
     parser.add_argument(
         "--config_file"					, default=__snake_case					, help=(
             "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
             "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
             "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
             "with 'huggingface'."
         )					, dest="save_location"					, )

     parser.add_argument(
         "--mixed_precision"					, choices=["no", "fp16", "bf16"]					, type=__snake_case					, help="Whether or not to use mixed precision training. "
         "Choose between FP16 and BF16 (bfloat16) training. "
         "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."					, default="no"					, )
     parser.set_defaults(func=__snake_case       )
     return parser





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					write_basic_config(args.mixed_precision					, args.save_location       )
     if config_file:
          print(F"""accelerate configuration saved at {config_file}"""       )






