

'''simple docstring'''






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCAmelCase__   		=      logging.get_logger(__name__)






@add_end_docstrings(lowerCamelCase__ )
class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  def __init__( self  :	List[str]   ,**lowercase__  :	Tuple     ):
   super().__init__(**lowercase__     )

   if self.framework == "tf":
    raise ValueError(F"The {self.__class__} is only available in PyTorch."     )

   requires_backends(self   ,'''vision'''     )
   self.check_model_type(lowercase__     )






  def __call__( self  :	List[str]   ,lowercase__  :	Union[str, "Image.Image", List[Dict[str, Any]]]   ,lowercase__  :	Union[str, List[str]] = None   ,**lowercase__  :	str   ,):
   if "text_queries" in kwargs:
    __lowercase				    =					kwargs.pop('''text_queries'''     )

   if isinstance(lowercase__   ,(str, Image.Image)     ):
    __lowercase				    =					{'''image''': image, '''candidate_labels''': candidate_labels}
   else:
    __lowercase				    =					image
   __lowercase				    =					super().__call__(lowercase__   ,**lowercase__     )
   return results






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,**lowercase__  :	List[Any]     ):
   __lowercase				    =					{}
   if "threshold" in kwargs:
    __lowercase				    =					kwargs['''threshold''']
   if "top_k" in kwargs:
    __lowercase				    =					kwargs['''top_k''']
   return {}, {}, postprocess_params






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Optional[Any]     ):
   __lowercase				    =					load_image(inputs['''image''']     )
   __lowercase				    =					inputs['''candidate_labels''']
   if isinstance(lowercase__   ,lowercase__     ):
    __lowercase				    =					candidate_labels.split(''','''     )

   __lowercase				    =					torch.tensor([[image.height, image.width]]   ,dtype=torch.intaa     )
   for i, candidate_label in enumerate(lowercase__     ):
    __lowercase				    =					self.tokenizer(lowercase__   ,return_tensors=self.framework     )
    __lowercase				    =					self.image_processor(lowercase__   ,return_tensors=self.framework     )
    yield {
        "is_last": i == len(lowercase__     ) - 1,
        "target_size": target_size,
        "candidate_label": candidate_label,
        **text_inputs,
        **image_features,
    }






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	List[str]     ):
   __lowercase				    =					model_inputs.pop('''target_size'''     )
   __lowercase				    =					model_inputs.pop('''candidate_label'''     )
   __lowercase				    =					model_inputs.pop('''is_last'''     )

   __lowercase				    =					self.model(**lowercase__     )

   __lowercase				    =					{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
   return model_outputs






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	int   ,lowercase__  :	List[Any]=0.1   ,lowercase__  :	List[str]=None     ):
   __lowercase				    =					[]
   for model_output in model_outputs:
    __lowercase				    =					model_output['''candidate_label''']
    __lowercase				    =					BaseModelOutput(lowercase__     )
    __lowercase				    =					self.image_processor.post_process_object_detection(
        outputs=lowercase__   ,threshold=lowercase__   ,target_sizes=model_output['''target_size''']     )[0]

    for index in outputs["scores"].nonzero():
     __lowercase				    =					outputs['''scores'''][index].item()
     __lowercase				    =					self._get_bounding_box(outputs['''boxes'''][index][0]     )

     __lowercase				    =					{'''score''': score, '''label''': label, '''box''': box}
     results.append(lowercase__     )

   __lowercase				    =					sorted(lowercase__   ,key=lambda lowercase__     : x["score"]   ,reverse=lowercase__     )
   if top_k:
    __lowercase				    =					results[:top_k]

   return results






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	"torch.Tensor"     ):
   if self.framework != "pt":
    raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''     )
   __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase				    =					box.int().tolist()
   __lowercase				    =					{
       '''xmin''': xmin,
       '''ymin''': ymin,
       '''xmax''': xmax,
       '''ymax''': ymax,
   }
   return bbox



"""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 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      List[Any]     							=					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()


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

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

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

    if args.freeze:
        for param in model.roberta.parameters():
            UpperCAmelCase__	:      Optional[int]     							=					False

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

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

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, 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'    ,)

    UpperCAmelCase__	:      Dict     							=					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()

