




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   (  )       ->	Tuple:

    """simple docstring"""


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


a_   		=     load("accuracy")




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						eval_pred
    __UpperCamelCase  		=						np.argmax(lowercase_					,     axis=1      )
    return metric.compute(predictions=lowercase_					,     references=lowercase_      )

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: List[Any] ,       snake_case					: str				):
          super().__init__()
          __UpperCamelCase  		=						trainer







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







def 					__SCREAMING_SNAKE_CASE   (  )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						get_args()
    set_seed(args.seed      )

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

    print('''Loading tokenizer and model'''      )
    __UpperCamelCase  		=						AutoTokenizer.from_pretrained(args.model_ckpt      )
    __UpperCamelCase  		=						tokenizer.eos_token
    __UpperCamelCase  		=						AutoModelForSequenceClassification.from_pretrained(args.model_ckpt					,     num_labels=7      )
    __UpperCamelCase  		=						model.config.eos_token_id

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

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

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

    __UpperCamelCase  		=						train_test_validation.map(
        lowercase_					,     batched=lowercase_					,     remove_columns=train_test_validation['''train'''].column_names					,     )
    __UpperCamelCase  		=						DataCollatorWithPadding(tokenizer=lowercase_      )

    __UpperCamelCase  		=						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  		=						Trainer(
        model=lowercase_					,     args=lowercase_					,     train_dataset=tokenized_datasets['''train''']					,     eval_dataset=tokenized_datasets['''valid''']					,     tokenizer=lowercase_					,     data_collator=lowercase_					,     compute_metrics=lowercase_					,     )

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


if __name__ == "__main__":
 main()












import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					StableDiffusionXLImgaImgPipeline
      lowerCAmelCase__			: List[str]    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
      lowerCAmelCase__			: int    =					PipelineTesterMixin.required_optional_params - {"latents"}
      lowerCAmelCase__			: Tuple    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__			: int    =					IMAGE_TO_IMAGE_IMAGE_PARAMS
      lowerCAmelCase__			: List[Any]    =					IMAGE_TO_IMAGE_IMAGE_PARAMS







      def       snake_case	(     self					: Union[str, Any]				):
          torch.manual_seed(0				)
          __UpperCamelCase  		=						UNetaDConditionModel(
              block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,       attention_head_dim=(2, 4) ,       use_linear_projection=snake_case ,       addition_embed_type='''text_time''' ,       addition_time_embed_dim=8 ,       transformer_layers_per_block=(1, 2) ,       projection_class_embeddings_input_dim=80 ,       cross_attention_dim=64 ,       )
          __UpperCamelCase  		=						EulerDiscreteScheduler(
              beta_start=0.0_00_85 ,       beta_end=0.0_12 ,       steps_offset=1 ,       beta_schedule='''scaled_linear''' ,       timestep_spacing='''leading''' ,       )
          torch.manual_seed(0				)
          __UpperCamelCase  		=						AutoencoderKL(
              block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,       latent_channels=4 ,       sample_size=128 ,       )
          torch.manual_seed(0				)
          __UpperCamelCase  		=						CLIPTextConfig(
              bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1000 ,       hidden_act='''gelu''' ,       projection_dim=32 ,       )
          __UpperCamelCase  		=						CLIPTextModel(snake_case				)
          __UpperCamelCase  		=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,       local_files_only=snake_case				)

          __UpperCamelCase  		=						CLIPTextModelWithProjection(snake_case				)
          __UpperCamelCase  		=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,       local_files_only=snake_case				)

          __UpperCamelCase  		=						{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''text_encoder_2''': text_encoder_a,
              '''tokenizer_2''': tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[Any] ,       snake_case					: str=0				):
          __UpperCamelCase  		=						floats_tensor((1, 3, 32, 32) ,       rng=random.Random(snake_case				)				).to(snake_case				)
          __UpperCamelCase  		=						image / 2 + 0.5
          if str(snake_case				).startswith('''mps'''				):
              __UpperCamelCase  		=						torch.manual_seed(snake_case				)
          else:
              __UpperCamelCase  		=						torch.Generator(device=snake_case				).manual_seed(snake_case				)
          __UpperCamelCase  		=						{
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 5.0,
              '''output_type''': '''numpy''',
              '''strength''': 0.75,
          }
          return inputs







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCamelCase  		=						self.get_dummy_components()
          __UpperCamelCase  		=						StableDiffusionXLImgaImgPipeline(**snake_case				)
          __UpperCamelCase  		=						sd_pipe.to(snake_case				)
          sd_pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						self.get_dummy_inputs(snake_case				)
          __UpperCamelCase  		=						sd_pipe(**snake_case				).images
          __UpperCamelCase  		=						image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          __UpperCamelCase  		=						np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







      def       snake_case	(     self					: Optional[int]				):
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3				)







      def       snake_case	(     self					: Optional[Any]				):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







      def       snake_case	(     self					: Optional[Any]				):
          pass







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.get_dummy_components()
          __UpperCamelCase  		=						StableDiffusionXLImgaImgPipeline(**snake_case				)
          __UpperCamelCase  		=						sd_pipe.to(snake_case				)
          __UpperCamelCase  		=						sd_pipe.to(snake_case				)
          sd_pipe.set_progress_bar_config(disable=snake_case				)

          # forward without prompt embeds
          __UpperCamelCase  		=						self.get_dummy_inputs(snake_case				)
          __UpperCamelCase  		=						3 * ['''this is a negative prompt''']
          __UpperCamelCase  		=						negative_prompt
          __UpperCamelCase  		=						3 * [inputs['''prompt''']]

          __UpperCamelCase  		=						sd_pipe(**snake_case				)
          __UpperCamelCase  		=						output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __UpperCamelCase  		=						self.get_dummy_inputs(snake_case				)
          __UpperCamelCase  		=						3 * ['''this is a negative prompt''']
          __UpperCamelCase  		=						3 * [inputs.pop('''prompt'''				)]

          (
              (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      
          )  		=						sd_pipe.encode_prompt(snake_case ,       negative_prompt=snake_case				)

          __UpperCamelCase  		=						sd_pipe(
              **snake_case ,       prompt_embeds=snake_case ,       negative_prompt_embeds=snake_case ,       pooled_prompt_embeds=snake_case ,       negative_pooled_prompt_embeds=snake_case ,       )
          __UpperCamelCase  		=						output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1E-4







@slow
@require_torch_gpu
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      def       snake_case	(     self					: Optional[Any]				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def       snake_case	(     self					: str ,       snake_case					: List[Any] ,       snake_case					: str="cpu" ,       snake_case					: int=torch.floataa ,       snake_case					: Optional[int]=0				):
          __UpperCamelCase  		=						torch.Generator(device=snake_case				).manual_seed(snake_case				)
          __UpperCamelCase  		=						np.random.RandomState(snake_case				).standard_normal((1, 4, 64, 64)				)
          __UpperCamelCase  		=						torch.from_numpy(snake_case				).to(device=snake_case ,       dtype=snake_case				)
          __UpperCamelCase  		=						{
              '''prompt''': '''a photograph of an astronaut riding a horse''',
              '''latents''': latents,
              '''generator''': generator,
              '''num_inference_steps''': 3,
              '''guidance_scale''': 7.5,
              '''output_type''': '''numpy''',
          }
          return inputs







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
          pipe.to(snake_case				)
          pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						self.get_inputs(snake_case				)
          __UpperCamelCase  		=						pipe(**snake_case				).images
          __UpperCamelCase  		=						image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]				)
          assert np.abs(image_slice - expected_slice				).max() < 7E-3






