

"""simple docstring"""



from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					["vqvae"]

    def __init__(     self: Any    ,						UpperCamelCase: AutoencoderKL    ,						UpperCamelCase: UNetaDConditionModel    ,						UpperCamelCase: Mel    ,						UpperCamelCase: Union[DDIMScheduler, DDPMScheduler]    ,						):






         """simple docstring"""


         super().__init__()
         self.register_modules(unet=UpperCamelCase    ,						scheduler=UpperCamelCase    ,						mel=UpperCamelCase    ,						vqvae=UpperCamelCase       )

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 50 if isinstance(self.scheduler    ,						UpperCamelCase       ) else 10_00

    @torch.no_grad()
    def __call__(     self: List[str]    ,						UpperCamelCase: int = 1    ,						UpperCamelCase: str = None    ,						UpperCamelCase: np.ndarray = None    ,						UpperCamelCase: int = 0    ,						UpperCamelCase: int = 0    ,						UpperCamelCase: int = None    ,						UpperCamelCase: torch.Generator = None    ,						UpperCamelCase: float = 0    ,						UpperCamelCase: float = 0    ,						UpperCamelCase: torch.Generator = None    ,						UpperCamelCase: float = 0    ,						UpperCamelCase: torch.Tensor = None    ,						UpperCamelCase: torch.Tensor = None    ,						UpperCamelCase: List[str]=True    ,						):






         """simple docstring"""


         A__										=  steps or self.get_default_steps()
         self.scheduler.set_timesteps(UpperCamelCase       )
         A__										=  step_generator or generator
         # For backwards compatibility
         if type(self.unet.config.sample_size       ) == int:
              A__										=  (self.unet.config.sample_size, self.unet.config.sample_size)
         if noise is None:
              A__										=  randn_tensor(
                  (
                      batch_size,
                      self.unet.config.in_channels,
                      self.unet.config.sample_size[0],
                      self.unet.config.sample_size[1],
                  )    ,						generator=UpperCamelCase    ,						device=self.device    ,						)
         A__										=  noise
         A__										=  None

         if audio_file is not None or raw_audio is not None:
              self.mel.load_audio(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  self.mel.audio_slice_to_image(UpperCamelCase       )
              A__										=  np.frombuffer(input_image.tobytes()    ,						dtype="""uint8"""       ).reshape(
                  (input_image.height, input_image.width)       )
              A__										=  (input_image / 2_55) * 2 - 1
              A__										=  torch.tensor(input_image[np.newaxis, :, :]    ,						dtype=torch.float       ).to(self.device       )

              if self.vqvae is not None:
                   A__										=  self.vqvae.encode(torch.unsqueeze(UpperCamelCase    ,						0       )       ).latent_dist.sample(
                       generator=UpperCamelCase       )[0]
                   A__										=  self.vqvae.config.scaling_factor * input_images

              if start_step > 0:
                   A__										=  self.scheduler.add_noise(UpperCamelCase    ,						UpperCamelCase    ,						self.scheduler.timesteps[start_step - 1]       )

              A__										=  (
                  self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
              )
              A__										=  int(mask_start_secs * pixels_per_second       )
              A__										=  int(mask_end_secs * pixels_per_second       )
              A__										=  self.scheduler.add_noise(UpperCamelCase    ,						UpperCamelCase    ,						torch.tensor(self.scheduler.timesteps[start_step:]       )       )

         for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]       )       ):
              if isinstance(self.unet    ,						UpperCamelCase       ):
                   A__										=  self.unet(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )["""sample"""]
              else:
                   A__										=  self.unet(UpperCamelCase    ,						UpperCamelCase       )["""sample"""]

              if isinstance(self.scheduler    ,						UpperCamelCase       ):
                   A__										=  self.scheduler.step(
                       model_output=UpperCamelCase    ,						timestep=UpperCamelCase    ,						sample=UpperCamelCase    ,						eta=UpperCamelCase    ,						generator=UpperCamelCase    ,						)["""prev_sample"""]
              else:
                   A__										=  self.scheduler.step(
                       model_output=UpperCamelCase    ,						timestep=UpperCamelCase    ,						sample=UpperCamelCase    ,						generator=UpperCamelCase    ,						)["""prev_sample"""]

              if mask is not None:
                   if mask_start > 0:
                        A__										=  mask[:, step, :, :mask_start]
                   if mask_end > 0:
                        A__										=  mask[:, step, :, -mask_end:]

         if self.vqvae is not None:
              # 0.18215 was scaling factor used in training to ensure unit variance
              A__										=  1 / self.vqvae.config.scaling_factor * images
              A__										=  self.vqvae.decode(UpperCamelCase       )["""sample"""]

         A__										=  (images / 2 + 0.5).clamp(0    ,						1       )
         A__										=  images.cpu().permute(0    ,						2    ,						3    ,						1       ).numpy()
         A__										=  (images * 2_55).round().astype("""uint8"""       )
         A__										=  list(
             (Image.fromarray(_[:, :, 0]       ) for _ in images)
             if images.shape[3] == 1
             else (Image.fromarray(UpperCamelCase    ,						mode="""RGB"""       ).convert("""L"""       ) for _ in images)       )

         A__										=  [self.mel.image_to_audio(UpperCamelCase       ) for _ in images]
         if not return_dict:
              return images, (self.mel.get_sample_rate(), audios)

         return BaseOutput(**AudioPipelineOutput(np.array(UpperCamelCase       )[:, np.newaxis, :]       )    ,						**ImagePipelineOutput(UpperCamelCase       )       )

    @torch.no_grad()
    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: List[Image.Image]    ,						UpperCamelCase: int = 50       ):






         """simple docstring"""


         assert isinstance(self.scheduler    ,						UpperCamelCase       )
         self.scheduler.set_timesteps(UpperCamelCase       )
         A__										=  np.array(
             [np.frombuffer(image.tobytes()    ,						dtype="""uint8"""       ).reshape((1, image.height, image.width)       ) for image in images]       )
         A__										=  (sample / 2_55) * 2 - 1
         A__										=  torch.Tensor(UpperCamelCase       ).to(self.device       )

         for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,						(0,)       )       ):
              A__										=  t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
              A__										=  self.scheduler.alphas_cumprod[t]
              A__										=  (
                  self.scheduler.alphas_cumprod[prev_timestep]
                  if prev_timestep >= 0
                  else self.scheduler.final_alpha_cumprod
              )
              A__										=  1 - alpha_prod_t
              A__										=  self.unet(UpperCamelCase    ,						UpperCamelCase       )["""sample"""]
              A__										=  (1 - alpha_prod_t_prev) ** 0.5 * model_output
              A__										=  (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
              A__										=  sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

         return sample





    @staticmethod
    def 				UpperCamelCase	(     UpperCamelCase: torch.Tensor    ,						UpperCamelCase: torch.Tensor    ,						UpperCamelCase: float       ):






         """simple docstring"""


         A__										=  acos(torch.dot(torch.flatten(UpperCamelCase       )    ,						torch.flatten(UpperCamelCase       )       ) / torch.norm(UpperCamelCase       ) / torch.norm(UpperCamelCase       )       )
         return sin((1 - alpha) * theta       ) * xa / sin(UpperCamelCase       ) + sin(alpha * theta       ) * xa / sin(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  _snake_case     (    ):
     A__										=  argparse.ArgumentParser()
     parser.add_argument("""--model_ckpt"""					,   type=UpperCAmelCase_					,   default="""microsoft/unixcoder-base-nine"""			)
     parser.add_argument("""--num_epochs"""					,   type=UpperCAmelCase_					,   default=5			)
     parser.add_argument("""--batch_size"""					,   type=UpperCAmelCase_					,   default=6			)
     parser.add_argument("""--gradient_accumulation_steps"""					,   type=UpperCAmelCase_					,   default=1			)
     parser.add_argument("""--freeze"""					,   type=UpperCAmelCase_					,   default=UpperCAmelCase_			)
     parser.add_argument("""--learning_rate"""					,   type=UpperCAmelCase_					,   default=5e-4			)
     parser.add_argument("""--seed"""					,   type=UpperCAmelCase_					,   default=0			)
     parser.add_argument("""--lr_scheduler_type"""					,   type=UpperCAmelCase_					,   default="""cosine"""			)
     parser.add_argument("""--num_warmup_steps"""					,   type=UpperCAmelCase_					,   default=10			)
     parser.add_argument("""--weight_decay"""					,   type=UpperCAmelCase_					,   default=0.01			)
     parser.add_argument("""--output_dir"""					,   type=UpperCAmelCase_					,   default="""./results"""			)
     return parser.parse_args()


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						load('accuracy')





def  _snake_case     (    UpperCAmelCase_  :					Tuple			):
     A__			,				A__										=  eval_pred
     A__										=  np.argmax(UpperCAmelCase_					,   axis=1			)
     return metric.compute(predictions=UpperCAmelCase_					,   references=UpperCAmelCase_			)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Optional[int]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         super().__init__()
         A__										=  trainer





    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: Dict    ,						**UpperCamelCase: int       ):






         """simple docstring"""


         if control.should_evaluate:
              A__										=  deepcopy(UpperCamelCase       )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,						metric_key_prefix="""train"""       )
              return control_copy





def  _snake_case     (    ):
     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(UpperCAmelCase_  :					int			):
          A__										=  tokenizer(example["""src"""]					,   truncation=UpperCAmelCase_					,   max_length=1024			)
          A__										=  labels.straint(example["""complexity"""]			)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

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

     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=0.01					,   metric_for_best_model="""accuracy"""					,   run_name="""complexity-java"""					,   report_to="""wandb"""					,   )

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

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


if __name__ == "__main__":
 main()






