
"""simple docstring"""



import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
    from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=13     ,	UpperCamelCase_=32     ,	UpperCamelCase_=3     ,	UpperCamelCase_=4     ,	UpperCamelCase_=[10, 20, 30, 40]     ,	UpperCamelCase_=[2, 2, 3, 2]     ,	UpperCamelCase_=True     ,	UpperCamelCase_=True     ,	UpperCamelCase_=37     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=10     ,	UpperCamelCase_=0.02     ,	UpperCamelCase_=["stage2", "stage3", "stage4"]     ,	UpperCamelCase_=[2, 3, 4]     ,	UpperCamelCase_=None     ,	):
          UpperCAmelCase__	:      str     							=					parent
          UpperCAmelCase__	:      Optional[int]     							=					batch_size
          UpperCAmelCase__	:      Tuple     							=					image_size
          UpperCAmelCase__	:      List[str]     							=					num_channels
          UpperCAmelCase__	:      Dict     							=					num_stages
          UpperCAmelCase__	:      Tuple     							=					hidden_sizes
          UpperCAmelCase__	:      Optional[int]     							=					depths
          UpperCAmelCase__	:      List[Any]     							=					is_training
          UpperCAmelCase__	:      Dict     							=					use_labels
          UpperCAmelCase__	:      Optional[int]     							=					intermediate_size
          UpperCAmelCase__	:      List[Any]     							=					hidden_act
          UpperCAmelCase__	:      Any     							=					num_labels
          UpperCAmelCase__	:      Optional[int]     							=					initializer_range
          UpperCAmelCase__	:      Any     							=					out_features
          UpperCAmelCase__	:      Tuple     							=					out_indices
          UpperCAmelCase__	:      List[str]     							=					scope



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          UpperCAmelCase__	:      int     							=					None
          if self.use_labels:
              UpperCAmelCase__	:      int     							=					ids_tensor([self.batch_size]     ,	self.num_labels      )

          UpperCAmelCase__	:      List[str]     							=					self.get_config()

          return config, pixel_values, labels



      def  __snake_case							(   self      ):
          return ConvNextVaConfig(
              num_channels=self.num_channels     ,	hidden_sizes=self.hidden_sizes     ,	depths=self.depths     ,	num_stages=self.num_stages     ,	hidden_act=self.hidden_act     ,	is_decoder=UpperCamelCase_     ,	initializer_range=self.initializer_range     ,	out_features=self.out_features     ,	out_indices=self.out_indices     ,	num_labels=self.num_labels     ,	)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					ConvNextVaModel(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      List[str]     							=					model(UpperCamelCase_      )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,	)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[Any]     							=					ConvNextVaForImageClassification(UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      Optional[int]     							=					model(UpperCamelCase_     ,	labels=UpperCamelCase_      )
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					ConvNextVaBackbone(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      str     							=					model(UpperCamelCase_      )

          # verify hidden states
          self.parent.assertEqual(len(result.feature_maps      )     ,	len(config.out_features      )      )
          self.parent.assertListEqual(list(result.feature_maps[0].shape      )     ,	[self.batch_size, self.hidden_sizes[1], 4, 4]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )     ,	len(config.out_features      )      )
          self.parent.assertListEqual(model.channels     ,	config.hidden_sizes[1:]      )

          # verify backbone works with out_features=None
          UpperCAmelCase__	:      Union[str, Any]     							=					None
          UpperCAmelCase__	:      Tuple     							=					ConvNextVaBackbone(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      List[Any]     							=					model(UpperCamelCase_      )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps      )     ,	1      )
          self.parent.assertListEqual(list(result.feature_maps[0].shape      )     ,	[self.batch_size, self.hidden_sizes[-1], 1, 1]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )     ,	1      )
          self.parent.assertListEqual(model.channels     ,	[config.hidden_sizes[-1]]      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					self.prepare_config_and_inputs()
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					config_and_inputs
          UpperCAmelCase__	:      Union[str, Any]     							=					{'pixel_values': pixel_values}
          return config, inputs_dict



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.prepare_config_and_inputs()
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					config_and_inputs
          UpperCAmelCase__	:      Optional[Any]     							=					{'pixel_values': pixel_values, 'labels': labels}
          return config, inputs_dict





@require_torch
class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   str           =     (
          (
              ConvNextVaModel,
              ConvNextVaForImageClassification,
              ConvNextVaBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase							:   Optional[int]           =     (
          {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCamelCase							:   int           =     False
      UpperCamelCase							:   str           =     False
      UpperCamelCase							:   Optional[int]           =     False
      UpperCamelCase							:   List[str]           =     False
      UpperCamelCase							:   Tuple           =     False



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					ConvNextVaModelTester(self      )
          UpperCAmelCase__	:      Optional[int]     							=					ConfigTester(self     ,	config_class=UpperCamelCase_     ,	has_text_modality=UpperCamelCase_     ,	hidden_size=37      )



      def  __snake_case							(   self      ):
          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  __snake_case							(   self      ):
          return



      @unittest.skip(reason='ConvNextV2 does not use inputs_embeds'      )
      def  __snake_case							(   self      ):
          pass



      @unittest.skip(reason='ConvNextV2 does not support input and output embeddings'      )
      def  __snake_case							(   self      ):
          pass



      @unittest.skip(reason='ConvNextV2 does not use feedforward chunking'      )
      def  __snake_case							(   self      ):
          pass



      def  __snake_case							(   self      ):
          if not self.model_tester.is_training:
              return

          for model_class in self.all_model_classes:
              UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					self.model_tester.prepare_config_and_inputs_with_labels()
              UpperCAmelCase__	:      Optional[int]     							=					True

              if model_class.__name__ in [
                  *get_values(UpperCamelCase_      ),
                  *get_values(UpperCamelCase_      ),
              ]:
                  continue

              UpperCAmelCase__	:      Optional[int]     							=					model_class(UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.train()
              UpperCAmelCase__	:      Optional[int]     							=					self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_     ,	return_labels=UpperCamelCase_      )
              UpperCAmelCase__	:      List[Any]     							=					model(**UpperCamelCase_      ).loss
              loss.backward()



      def  __snake_case							(   self      ):
          if not self.model_tester.is_training:
              return

          for model_class in self.all_model_classes:
              UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					self.model_tester.prepare_config_and_inputs_with_labels()
              UpperCAmelCase__	:      Optional[Any]     							=					False
              UpperCAmelCase__	:      Optional[Any]     							=					True

              if (
                  model_class.__name__
                  in [*get_values(UpperCamelCase_      ), *get_values(UpperCamelCase_      )]
                  or not model_class.supports_gradient_checkpointing
              ):
                  continue

              UpperCAmelCase__	:      Tuple     							=					model_class(UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.gradient_checkpointing_enable()
              model.train()
              UpperCAmelCase__	:      Union[str, Any]     							=					self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_     ,	return_labels=UpperCamelCase_      )
              UpperCAmelCase__	:      Any     							=					model(**UpperCamelCase_      ).loss
              loss.backward()



      def  __snake_case							(   self      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase__	:      int     							=					['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCamelCase_      )



      def  __snake_case							(   self      ):
          def check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
              UpperCAmelCase__	:      Union[str, Any]     							=					model_class(UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.eval()

              with torch.no_grad():
                  UpperCAmelCase__	:      List[Any]     							=					model(**self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_      )      )

              UpperCAmelCase__	:      Tuple     							=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              UpperCAmelCase__	:      Tuple     							=					self.model_tester.num_stages
              self.assertEqual(len(UpperCamelCase_      )     ,	expected_num_stages + 1      )

              # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]      )     ,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,	)

          UpperCAmelCase__							,							UpperCAmelCase__	:      Tuple     							=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__	:      Tuple     							=					True
              check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )

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

              check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__	:      int     							=					ConvNextVaModel.from_pretrained(UpperCamelCase_      )
              self.assertIsNotNone(UpperCamelCase_      )




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      List[Any]     							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image





@require_torch
@require_vision
class 				a			(					unittest.TestCase      ):



      @cached_property
      def  __snake_case							(   self      ):
          return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'      ) if is_vision_available() else None



      @slow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'      ).to(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					self.default_image_processor
          UpperCAmelCase__	:      Union[str, Any]     							=					prepare_img()
          UpperCAmelCase__	:      Optional[int]     							=					preprocessor(images=UpperCamelCase_     ,	return_tensors='pt'      ).to(UpperCamelCase_      )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__	:      Optional[Any]     							=					model(**UpperCamelCase_      )

          # verify the logits
          UpperCAmelCase__	:      Optional[int]     							=					torch.Size((1, 1_000)      )
          self.assertEqual(outputs.logits.shape     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					torch.tensor([0.9996, 0.1966, -0.4386]      ).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 					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()

