from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """timesformer"""

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2_24  ,				__SCREAMING_SNAKE_CASE			:   str=16  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=3  ,				__SCREAMING_SNAKE_CASE			:   int=8  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=7_68  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=30_72  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   Tuple=1E-6  ,				__SCREAMING_SNAKE_CASE			:   str=True  ,				__SCREAMING_SNAKE_CASE			:   str="divided_space_time"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	image_size
   __a							=	patch_size
   __a							=	num_channels
   __a							=	num_frames

   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	layer_norm_eps
   __a							=	qkv_bias

   __a							=	attention_type
   __a							=	drop_path_rate



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 	__A		(      ):



 """simple docstring"""


 __a							=	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()


SCREAMING_SNAKE_CASE			:						Tuple				  =		load("""accuracy""")



def 	__A		(      _A       ):



 """simple docstring"""


 __a							,  __a							=	eval_pred
 __a							=	np.argmax(_A				,					axis=1       )
 return metric.compute(predictions=_A				,					references=_A       )
class  A_			(							a_			):

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int							):
   super().__init__()
   __a							=	trainer

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				**__SCREAMING_SNAKE_CASE			:   int							):
   if control.should_evaluate:
    __a							=	deepcopy(__SCREAMING_SNAKE_CASE							)
    self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,				metric_key_prefix="train"							)
    return control_copy



def 	__A		(      ):



 """simple docstring"""


 __a							=	get_args()
 set_seed(args.seed       )

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

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

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

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

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

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

 __a							=	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							=	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()


