


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()



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



