"""simple docstring"""







import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import CvtForImageClassification, CvtModel
       from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor







class  UpperCamelCase	(    lowercase_						):
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.config_class(**self.inputs_dict    )
        self.parent.assertTrue(hasattr(__UpperCamelCase  ,'embed_dim'    )    )
        self.parent.assertTrue(hasattr(__UpperCamelCase  ,'num_heads'    )    )







class  UpperCamelCase	:
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=13  ,__UpperCamelCase=64  ,__UpperCamelCase=3  ,__UpperCamelCase=[16, 48, 96]  ,__UpperCamelCase=[1, 3, 6]  ,__UpperCamelCase=[1, 2, 10]  ,__UpperCamelCase=[7, 3, 3]  ,__UpperCamelCase=[4, 2, 2]  ,__UpperCamelCase=[2, 1, 1]  ,__UpperCamelCase=[2, 2, 2]  ,__UpperCamelCase=[False, False, True]  ,__UpperCamelCase=[0.0, 0.0, 0.0]  ,__UpperCamelCase=0.02  ,__UpperCamelCase=1e-12  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=2  ,)			->   int:


        '''simple docstring'''


        lowercase_			:       Tuple									=			parent
        lowercase_			:       Optional[Any]									=			batch_size
        lowercase_			:       Tuple									=			image_size
        lowercase_			:       Optional[int]									=			patch_sizes
        lowercase_			:       Union[str, Any]									=			patch_stride
        lowercase_			:       List[str]									=			patch_padding
        lowercase_			:       str									=			is_training
        lowercase_			:       str									=			use_labels
        lowercase_			:       List[str]									=			num_labels
        lowercase_			:       Tuple									=			num_channels
        lowercase_			:       Tuple									=			embed_dim
        lowercase_			:       str									=			num_heads
        lowercase_			:       List[Any]									=			stride_kv
        lowercase_			:       int									=			depth
        lowercase_			:       int									=			cls_token
        lowercase_			:       List[str]									=			attention_drop_rate
        lowercase_			:       List[Any]									=			initializer_range
        lowercase_			:       Optional[Any]									=			layer_norm_eps
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        lowercase_			:       List[str]									=			None
        if self.use_labels:
             lowercase_			:       Tuple									=			ids_tensor([self.batch_size]  ,self.num_labels    )

        lowercase_			:       Optional[Any]									=			self.get_config()
        return config, pixel_values, labels
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        return CvtConfig(
            image_size=self.image_size  ,num_labels=self.num_labels  ,num_channels=self.num_channels  ,embed_dim=self.embed_dim  ,num_heads=self.num_heads  ,patch_sizes=self.patch_sizes  ,patch_padding=self.patch_padding  ,patch_stride=self.patch_stride  ,stride_kv=self.stride_kv  ,depth=self.depth  ,cls_token=self.cls_token  ,attention_drop_rate=self.attention_drop_rate  ,initializer_range=self.initializer_range  ,)
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Any									=			CvtModel(config=__UpperCamelCase    )
        model.to(__UpperCamelCase    )
        model.eval()
        lowercase_			:       str									=			model(__UpperCamelCase    )
        lowercase_			:       List[Any]									=			(self.image_size, self.image_size)
        lowercase_			:       List[str]									=			image_size[0], image_size[1]
        for i in range(len(self.depth    )    ):
             lowercase_			:       Optional[Any]									=			floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
             lowercase_			:       Dict									=			floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
        self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.embed_dim[-1], height, width)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        lowercase_			:       List[str]									=			self.num_labels
        lowercase_			:       Dict									=			CvtForImageClassification(__UpperCamelCase    )
        model.to(__UpperCamelCase    )
        model.eval()
        lowercase_			:       Optional[int]									=			model(__UpperCamelCase  ,labels=__UpperCamelCase    )
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)    )







   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Dict									=			self.prepare_config_and_inputs()
        lowercase_			:       List[str]									=			config_and_inputs
        lowercase_			:       int									=			{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class  UpperCamelCase	(    lowercase_		,   lowercase_		,   unittest.TestCase						):
   lowercase							=						(CvtModel, CvtForImageClassification) if is_torch_available() else ()
   lowercase							=						(
       {'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
       if is_torch_available()
       else {}
   )

   lowercase							=						False
   lowercase							=						False
   lowercase							=						False
   lowercase							=						False
   lowercase							=						False
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			CvtModelTester(self    )
        lowercase_			:       Union[str, Any]									=			ConfigTester(self  ,config_class=__UpperCamelCase  ,has_text_modality=__UpperCamelCase  ,hidden_size=37    )
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        return
   @unittest.skip(reason='Cvt does not output attentions'    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        pass
   @unittest.skip(reason='Cvt does not use inputs_embeds'    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        pass
   @unittest.skip(reason='Cvt does not support input and output embeddings'    )
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        pass
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_			:       Dict									=			model_class(__UpperCamelCase    )
             lowercase_			:       Optional[int]									=			inspect.signature(model.forward    )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_			:       Union[str, Any]									=			[*signature.parameters.keys()]

             lowercase_			:       Tuple									=			['pixel_values']
             self.assertListEqual(arg_names[:1]  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        def check_hidden_states_output(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Any									=			model_class(__UpperCamelCase    )
             model.to(__UpperCamelCase    )
             model.eval()

             with torch.no_grad():
                  lowercase_			:       Optional[Any]									=			model(**self._prepare_for_class(__UpperCamelCase  ,__UpperCamelCase    )    )

             lowercase_			:       Optional[Any]									=			outputs.hidden_states

             lowercase_			:       str									=			len(self.model_tester.depth    )
             self.assertEqual(len(__UpperCamelCase    )  ,__UpperCamelCase    )

             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-3:]    )  ,[
                     self.model_tester.embed_dim[0],
                     self.model_tester.image_size // 4,
                     self.model_tester.image_size // 4,
                 ]  ,)

        lowercase_			:       Any									=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_			:       Any									=			True
             check_hidden_states_output(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_			:       Tuple									=			True

             check_hidden_states_output(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase    )
   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        pass







   @slow
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_			:       str									=			CvtModel.from_pretrained(__UpperCamelCase    )
             self.assertIsNotNone(__UpperCamelCase    )






def     lowercase__( ):
     lowercase_			:       int									=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
     return image







@require_torch
@require_vision
class  UpperCamelCase	(    unittest.TestCase						):
   @cached_property
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )







   @slow
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(__UpperCamelCase    )

        lowercase_			:       List[str]									=			self.default_image_processor
        lowercase_			:       str									=			prepare_img()
        lowercase_			:       Union[str, Any]									=			image_processor(images=__UpperCamelCase  ,return_tensors='pt'    ).to(__UpperCamelCase    )

        # forward pass
        with torch.no_grad():
             lowercase_			:       List[str]									=			model(**__UpperCamelCase    )

        # verify the logits
        lowercase_			:       Union[str, Any]									=			torch.Size((1, 1000)    )
        self.assertEqual(outputs.logits.shape  ,__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			torch.tensor([0.9285, 0.9015, -0.3150]    ).to(__UpperCamelCase    )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,__UpperCamelCase  ,atol=1e-4    )    )

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


__SCREAMING_SNAKE_CASE								=load("accuracy")



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     lowercase_							,						lowercase_			:       Any									=			eval_pred
     lowercase_			:       Optional[Any]									=			np.argmax(__SCREAMING_SNAKE_CASE  ,    axis=1   )
     return metric.compute(predictions=__SCREAMING_SNAKE_CASE  ,    references=__SCREAMING_SNAKE_CASE   )







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase    )			->   None:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       str									=			trainer







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        if control.should_evaluate:
             lowercase_			:       List[str]									=			deepcopy(__UpperCamelCase    )
             self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,metric_key_prefix='train'    )
             return control_copy






def     lowercase__( ):
     lowercase_			:       Dict									=			get_args()
     set_seed(args.seed   )

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

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

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

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

     def tokenize(__SCREAMING_SNAKE_CASE							:  str   ):
          lowercase_			:       Dict									=			tokenizer(example['src']  ,    truncation=__SCREAMING_SNAKE_CASE  ,    max_length=10_24   )
          lowercase_			:       int									=			labels.straint(example['complexity']   )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     lowercase_			:       Optional[int]									=			train_test_validation.map(
         __SCREAMING_SNAKE_CASE  ,    batched=__SCREAMING_SNAKE_CASE  ,    remove_columns=train_test_validation['train'].column_names  ,    )
     lowercase_			:       Any									=			DataCollatorWithPadding(tokenizer=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       int									=			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'  ,    )

     lowercase_			:       Optional[int]									=			Trainer(
         model=__SCREAMING_SNAKE_CASE  ,    args=__SCREAMING_SNAKE_CASE  ,    train_dataset=tokenized_datasets['train']  ,    eval_dataset=tokenized_datasets['valid']  ,    tokenizer=__SCREAMING_SNAKE_CASE  ,    data_collator=__SCREAMING_SNAKE_CASE  ,    compute_metrics=__SCREAMING_SNAKE_CASE  ,    )

     print('Training...'   )
     trainer.add_callback(CustomCallback(__SCREAMING_SNAKE_CASE   )   )
     trainer.train()


if __name__ == "__main__":
       main()

