


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        SCREAMING_SNAKE_CASE_						(			)	->		int:

       """simple docstring"""

       a_					:		Dict							=  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()


UpperCAmelCase_      : Optional[int]  					=       load('accuracy')







def        SCREAMING_SNAKE_CASE_						(			__A :			Any				)	->		str:

       """simple docstring"""

       a_							,		a_					:		str							=  eval_pred
       a_					:		int							=  np.argmax(__A  ,    axis=1				)
       return metric.compute(predictions=__A  ,    references=__A				)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			None:
             super().__init__()
             a_					:		List[str]							=  trainer

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







def        SCREAMING_SNAKE_CASE_						(			)	->		int:

       """simple docstring"""

       a_					:		List[Any]							=  get_args()
       set_seed(args.seed				)

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

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

       if args.freeze:
              for param in model.roberta.parameters():
                     a_					:		Union[str, Any]							=  False

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

       def tokenize(__A :			str				):
              a_					:		Optional[Any]							=  tokenizer(example['src']  ,    truncation=__A  ,    max_length=10_24				)
              a_					:		Union[str, Any]							=  labels.straint(example['complexity']				)
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

       a_					:		List[str]							=  train_test_validation.map(
           __A  ,    batched=__A  ,    remove_columns=train_test_validation['train'].column_names  ,    )
       a_					:		Tuple							=  DataCollatorWithPadding(tokenizer=__A				)

       a_					:		Optional[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=0.01  ,    metric_for_best_model='accuracy'  ,    run_name='complexity-java'  ,    report_to='wandb'  ,    )

       a_					:		Optional[Any]							=  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      : Dict  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Union[str, Any]  					=       {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[str]        =						'''switch_transformers'''
      snake_case__				:     Optional[int]        =						['''past_key_values''']
      snake_case__				:     Optional[Any]        =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
      def __init__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_2_1_2_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=6_4     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=6_4     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=3     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=8     ,							SCREAMING_SNAKE_CASE__				:       Dict=False     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0.01     ,							SCREAMING_SNAKE_CASE__				:       str="float32"     ,							SCREAMING_SNAKE_CASE__				:       str=False     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=1_2_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Dict=1E-6     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.001     ,							SCREAMING_SNAKE_CASE__				:       Any=0.001     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1.0     ,							SCREAMING_SNAKE_CASE__				:       Any="relu"     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1     ,							**SCREAMING_SNAKE_CASE__				:       Dict     ,							)		->			Optional[Any]:
             a_					:		Optional[int]							=  vocab_size
             a_					:		List[str]							=  d_model
             a_					:		Tuple							=  d_kv
             a_					:		Optional[Any]							=  d_ff

             a_					:		List[Any]							=  num_sparse_encoder_layers

             a_					:		Any							=  num_layers
             a_					:		str							=  (
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             a_					:		List[Any]							=  num_sparse_decoder_layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_encoder_layers > 0:
                    a_					:		Optional[int]							=  self.num_layers // self.num_sparse_encoder_layers
             else:
                    a_					:		List[Any]							=  self.num_layers  # HACK: this will create 0 sparse layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_decoder_layers > 0:
                    a_					:		Union[str, Any]							=  self.num_decoder_layers // self.num_sparse_decoder_layers
             else:
                    a_					:		List[str]							=  self.num_decoder_layers  # HACK: this will create 0 sparse layers

             a_					:		Dict							=  num_heads
             a_					:		str							=  num_experts
             a_					:		Any							=  expert_capacity
             a_					:		List[Any]							=  router_bias
             a_					:		str							=  router_jitter_noise
             if router_dtype not in ["float32", "float16", "bfloat16"]:
                    raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""     )
             a_					:		Optional[int]							=  router_dtype

             a_					:		int							=  router_ignore_padding_tokens
             a_					:		Any							=  relative_attention_num_buckets
             a_					:		List[str]							=  relative_attention_max_distance

             a_					:		Optional[Any]							=  dropout_rate
             a_					:		Tuple							=  layer_norm_epsilon
             a_					:		Dict							=  initializer_factor
             a_					:		Any							=  feed_forward_proj
             a_					:		Tuple							=  use_cache
             a_					:		str							=  add_router_probs

             a_					:		Optional[int]							=  router_z_loss_coef
             a_					:		List[str]							=  router_aux_loss_coef

             a_					:		int							=  self.feed_forward_proj.split('-'     )
             a_					:		int							=  act_info[-1]
             a_					:		Optional[int]							=  act_info[0] == 'gated'

             if len(SCREAMING_SNAKE_CASE__     ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__     ) > 2:
                    raise ValueError(
                        F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                        'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                        '\'gated-gelu\' or \'relu\''     )

             # for backwards compatibility
             if feed_forward_proj == "gated-gelu":
                    a_					:		Any							=  'gelu_new'

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							is_encoder_decoder=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)


