


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      VQModel
     _lowerCamelCase   =      """sample"""


     @property
     def 	snake_case_  (	self  ,       __A=(32, 32)    ):
        __a					=  4
        __a					=  3

        __a					=  floats_tensor((batch_size, num_channels) + sizes    ).to(__A    )

        return {"sample": image}


     @property
     def 	snake_case_  (	self    ):
        return (3, 32, 32)


     @property
     def 	snake_case_  (	self    ):
        return (3, 32, 32)


     def 	snake_case_  (	self    ):
        __a					=  {
            """block_out_channels""": [32, 64],
            """in_channels""": 3,
            """out_channels""": 3,
            """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
            """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
            """latent_channels""": 3,
        }
        __a					=  self.dummy_input
        return init_dict, inputs_dict


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        __a	,    __a					=  VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,       output_loading_info=__A    )
        self.assertIsNotNone(__A    )
        self.assertEqual(len(loading_info["""missing_keys"""]    )  ,       0    )

        model.to(__A    )
        __a					=  model(**self.dummy_input    )

        assert image is not None, "Make sure output is not None"


     def 	snake_case_  (	self    ):
        __a					=  VQModel.from_pretrained("""fusing/vqgan-dummy"""    )
        model.to(__A    ).eval()

        torch.manual_seed(0    )
        if torch.cuda.is_available():
           torch.cuda.manual_seed_all(0    )

        __a					=  torch.randn(1  ,       model.config.in_channels  ,       model.config.sample_size  ,       model.config.sample_size    )
        __a					=  image.to(__A    )
        with torch.no_grad():
           __a					=  model(__A    ).sample

        __a					=  output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        __a					=  torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]    )
        # fmt: on
        self.assertTrue(torch.allclose(__A  ,       __A  ,       atol=1E-3    )    )







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   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""--model_ckpt"""		,				type=lowerCAmelCase__		,				default="""microsoft/unixcoder-base-nine"""   )
   parser.add_argument("""--num_epochs"""		,				type=lowerCAmelCase__		,				default=5   )
   parser.add_argument("""--batch_size"""		,				type=lowerCAmelCase__		,				default=6   )
   parser.add_argument("""--gradient_accumulation_steps"""		,				type=lowerCAmelCase__		,				default=1   )
   parser.add_argument("""--freeze"""		,				type=lowerCAmelCase__		,				default=lowerCAmelCase__   )
   parser.add_argument("""--learning_rate"""		,				type=lowerCAmelCase__		,				default=5E-4   )
   parser.add_argument("""--seed"""		,				type=lowerCAmelCase__		,				default=0   )
   parser.add_argument("""--lr_scheduler_type"""		,				type=lowerCAmelCase__		,				default="""cosine"""   )
   parser.add_argument("""--num_warmup_steps"""		,				type=lowerCAmelCase__		,				default=10   )
   parser.add_argument("""--weight_decay"""		,				type=lowerCAmelCase__		,				default=0.0_1   )
   parser.add_argument("""--output_dir"""		,				type=lowerCAmelCase__		,				default="""./results"""   )
   return parser.parse_args()


SCREAMING_SNAKE_CASE							    =							load('accuracy')




def  a   (lowerCAmelCase__   ):
   __a	,    __a					=  eval_pred
   __a					=  np.argmax(lowerCAmelCase__		,				axis=1   )
   return metric.compute(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  trainer


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       **__A    ):
        if control.should_evaluate:
           __a					=  deepcopy(__A    )
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,       metric_key_prefix="""train"""    )
           return control_copy







def  a   ():
   __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(lowerCAmelCase__   ):
      __a					=  tokenizer(example["""src"""]		,				truncation=lowerCAmelCase__		,				max_length=1_024   )
      __a					=  labels.straint(example["""complexity"""]   )
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

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

   __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=0.0_1		,				metric_for_best_model="""accuracy"""		,				run_name="""complexity-java"""		,				report_to="""wandb"""		,				)

   __a					=  Trainer(
       model=lowerCAmelCase__		,				args=lowerCAmelCase__		,				train_dataset=tokenized_datasets["""train"""]		,				eval_dataset=tokenized_datasets["""valid"""]		,				tokenizer=lowerCAmelCase__		,				data_collator=lowerCAmelCase__		,				compute_metrics=lowerCAmelCase__		,				)

   print("""Training..."""   )
   trainer.add_callback(CustomCallback(lowerCAmelCase__   )   )
   trainer.train()


if __name__ == "__main__":
  main()



