


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  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=8   ):
   __a					=  height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   __a					=  width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       ):
        super().__init__()

        self.register_modules(
            unet=__A  ,       scheduler=__A  ,       movq=__A  ,       )
        __a					=  2 ** (len(self.movq.config.block_out_channels    ) - 1)


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        if latents is None:
           __a					=  randn_tensor(__A  ,       generator=__A  ,       device=__A  ,       dtype=__A    )
        else:
           if latents.shape != shape:
              raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
           __a					=  latents.to(__A    )

        __a					=  latents * scheduler.init_noise_sigma
        return latents


     def 	snake_case_  (	self  ,       __A=0    ):
        if is_accelerate_available():
           from accelerate import cpu_offload
        else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

        __a					=  torch.device(f'''cuda:{gpu_id}'''    )

        __a					=  [
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
              cpu_offload(__A  ,       __A    )


     def 	snake_case_  (	self  ,       __A=0    ):
        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."""    )

        __a					=  torch.device(f'''cuda:{gpu_id}'''    )

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

        __a					=  None
        for cpu_offloaded_model in [self.unet, self.movq]:
           __a	,    __a					=  cpu_offload_with_hook(__A  ,       __A  ,       prev_module_hook=__A    )

        # We'll offload the last model manually.
        __a					=  hook


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 	snake_case_  (	self    ):
        if not hasattr(self.unet  ,       """_hf_hook"""    ):
           return self.device
        for module in self.unet.modules():
           if (
               hasattr(__A  ,       """_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(__A    )
     def __call__(	self  ,       __A  ,       __A  ,       __A = 512  ,       __A = 512  ,       __A = 100  ,       __A = 4.0  ,       __A = 1  ,       __A = None  ,       __A = None  ,       __A = "pil"  ,       __A = True  ,       ):
        __a					=  self._execution_device

        __a					=  guidance_scale > 1.0

        if isinstance(__A  ,       __A    ):
           __a					=  torch.cat(__A  ,       dim=0    )
        __a					=  image_embeds.shape[0] * num_images_per_prompt
        if isinstance(__A  ,       __A    ):
           __a					=  torch.cat(__A  ,       dim=0    )

        if do_classifier_free_guidance:
           __a					=  image_embeds.repeat_interleave(__A  ,       dim=0    )
           __a					=  negative_image_embeds.repeat_interleave(__A  ,       dim=0    )

        __a					=  torch.cat([negative_image_embeds, image_embeds]  ,       dim=0    ).to(dtype=self.unet.dtype  ,       device=__A    )

        self.scheduler.set_timesteps(__A  ,       device=__A    )
        __a					=  self.scheduler.timesteps

        __a					=  self.unet.config.in_channels

        __a	,    __a					=  downscale_height_and_width(__A  ,       __A  ,       self.movq_scale_factor    )

        # create initial latent
        __a					=  self.prepare_latents(
            (batch_size, num_channels_latents, height, width)  ,       image_embeds.dtype  ,       __A  ,       __A  ,       __A  ,       self.scheduler  ,       )

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

           __a					=  {"""image_embeds""": image_embeds}
           __a					=  self.unet(
               sample=__A  ,       timestep=__A  ,       encoder_hidden_states=__A  ,       added_cond_kwargs=__A  ,       return_dict=__A  ,       )[0]

           if do_classifier_free_guidance:
              __a	,    __a					=  noise_pred.split(latents.shape[1]  ,       dim=1    )
              __a	,    __a					=  noise_pred.chunk(2    )
              __a	,    __a					=  variance_pred.chunk(2    )
              __a					=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
              __a					=  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"]
           ):
              __a	,    __a					=  noise_pred.split(latents.shape[1]  ,       dim=1    )

           # compute the previous noisy sample x_t -> x_t-1
           __a					=  self.scheduler.step(
               __A  ,       __A  ,       __A  ,       generator=__A  ,       )[0]
        # post-processing
        __a					=  self.movq.decode(__A  ,       force_not_quantize=__A    )["""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"]:
           __a					=  image * 0.5 + 0.5
           __a					=  image.clamp(0  ,       1    )
           __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    ).float().numpy()

        if output_type == "pil":
           __a					=  self.numpy_to_pil(__A    )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=__A    )







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()



