"""simple docstring"""







from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)  # pylint: disable=invalid-name

__SCREAMING_SNAKE_CASE								="\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  str=8   ):
     lowercase_			:       List[Any]									=			height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     lowercase_			:       Tuple									=			width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,)			->   List[str]:


        '''simple docstring'''


        super().__init__()

        self.register_modules(
            unet=__UpperCamelCase  ,scheduler=__UpperCamelCase  ,movq=__UpperCamelCase  ,)
        lowercase_			:       Any									=			2 ** (len(self.movq.config.block_out_channels    ) - 1)
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        if latents is None:
             lowercase_			:       Optional[int]									=			randn_tensor(__UpperCamelCase  ,generator=__UpperCamelCase  ,device=__UpperCamelCase  ,dtype=__UpperCamelCase    )
        else:
             if latents.shape != shape:
                  raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
             lowercase_			:       Any									=			latents.to(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			latents * scheduler.init_noise_sigma
        return latents
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=0    )			->   Any:


        '''simple docstring'''


        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('Please install accelerate via `pip install accelerate`'    )

        lowercase_			:       Union[str, Any]									=			torch.device(f'''cuda:{gpu_id}'''    )

        lowercase_			:       List[str]									=			[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=0    )			->   Union[str, Any]:


        '''simple docstring'''


        if is_accelerate_available() and is_accelerate_version('>='  ,'0.17.0.dev0'    ):
             from accelerate import cpu_offload_with_hook
        else:
             raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

        lowercase_			:       List[str]									=			torch.device(f'''cuda:{gpu_id}'''    )

        if self.device.type != "cpu":
             self.to('cpu'  ,silence_dtype_warnings=__UpperCamelCase    )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        lowercase_			:       Union[str, Any]									=			None
        for cpu_offloaded_model in [self.unet, self.movq]:
             lowercase_							,						lowercase_			:       Optional[int]									=			cpu_offload_with_hook(__UpperCamelCase  ,__UpperCamelCase  ,prev_module_hook=__UpperCamelCase    )

        # We'll offload the last model manually.
        lowercase_			:       Union[str, Any]									=			hook
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        if not hasattr(self.unet  ,'_hf_hook'    ):
             return self.device
        for module in self.unet.modules():
             if (
                 hasattr(__UpperCamelCase  ,'_hf_hook'    )
                 and hasattr(module._hf_hook  ,'execution_device'    )
                 and module._hf_hook.execution_device is not None
             ):
                  return torch.device(module._hf_hook.execution_device    )
        return self.device







   @torch.no_grad()
   @replace_example_docstring(__UpperCamelCase    )
   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = 512  ,__UpperCamelCase = 512  ,__UpperCamelCase = 100  ,__UpperCamelCase = 4.0  ,__UpperCamelCase = 1  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = "pil"  ,__UpperCamelCase = True  ,)			->   List[str]:


        '''simple docstring'''


        lowercase_			:       str									=			self._execution_device

        lowercase_			:       List[Any]									=			guidance_scale > 1.0

        if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Optional[int]									=			torch.cat(__UpperCamelCase  ,dim=0    )
        lowercase_			:       List[Any]									=			image_embeds.shape[0] * num_images_per_prompt
        if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Union[str, Any]									=			torch.cat(__UpperCamelCase  ,dim=0    )

        if do_classifier_free_guidance:
             lowercase_			:       Tuple									=			image_embeds.repeat_interleave(__UpperCamelCase  ,dim=0    )
             lowercase_			:       Any									=			negative_image_embeds.repeat_interleave(__UpperCamelCase  ,dim=0    )

        lowercase_			:       Optional[int]									=			torch.cat([negative_image_embeds, image_embeds]  ,dim=0    ).to(dtype=self.unet.dtype  ,device=__UpperCamelCase    )

        self.scheduler.set_timesteps(__UpperCamelCase  ,device=__UpperCamelCase    )
        lowercase_			:       Any									=			self.scheduler.timesteps

        lowercase_			:       Any									=			self.unet.config.in_channels

        lowercase_							,						lowercase_			:       Optional[Any]									=			downscale_height_and_width(__UpperCamelCase  ,__UpperCamelCase  ,self.movq_scale_factor    )

        # create initial latent
        lowercase_			:       Optional[Any]									=			self.prepare_latents(
            (batch_size, num_channels_latents, height, width)  ,image_embeds.dtype  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,self.scheduler  ,)

        for i, t in enumerate(self.progress_bar(__UpperCamelCase    )    ):
             # expand the latents if we are doing classifier free guidance
             lowercase_			:       int									=			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

             lowercase_			:       List[Any]									=			{'image_embeds': image_embeds}
             lowercase_			:       Optional[int]									=			self.unet(
                 sample=__UpperCamelCase  ,timestep=__UpperCamelCase  ,encoder_hidden_states=__UpperCamelCase  ,added_cond_kwargs=__UpperCamelCase  ,return_dict=__UpperCamelCase  ,)[0]

             if do_classifier_free_guidance:
                  lowercase_							,						lowercase_			:       Optional[Any]									=			noise_pred.split(latents.shape[1]  ,dim=1    )
                  lowercase_							,						lowercase_			:       int									=			noise_pred.chunk(2    )
                  lowercase_							,						lowercase_			:       Any									=			variance_pred.chunk(2    )
                  lowercase_			:       Union[str, Any]									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  lowercase_			:       int									=			torch.cat([noise_pred, variance_pred_text]  ,dim=1    )

             if not (
                 hasattr(self.scheduler.config  ,'variance_type'    )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                  lowercase_							,						lowercase_			:       List[Any]									=			noise_pred.split(latents.shape[1]  ,dim=1    )

             # compute the previous noisy sample x_t -> x_t-1
             lowercase_			:       Optional[int]									=			self.scheduler.step(
                 __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,generator=__UpperCamelCase  ,)[0]
        # post-processing
        lowercase_			:       int									=			self.movq.decode(__UpperCamelCase  ,force_not_quantize=__UpperCamelCase    )['sample']

        if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''    )

        if output_type in ["np", "pil"]:
             lowercase_			:       Optional[Any]									=			image * 0.5 + 0.5
             lowercase_			:       Any									=			image.clamp(0  ,1    )
             lowercase_			:       List[Any]									=			image.cpu().permute(0  ,2  ,3  ,1    ).float().numpy()

        if output_type == "pil":
             lowercase_			:       List[str]									=			self.numpy_to_pil(__UpperCamelCase    )

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=__UpperCamelCase    )


"""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()

