





'''simple docstring'''




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 			SCREAMING_SNAKE_CASE   (a__ ,		a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	StableDiffusionXLImgaImgPipeline
     lowerCAmelCase		      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     lowerCAmelCase		      =	PipelineTesterMixin.required_optional_params - {'''latents'''}
     lowerCAmelCase		      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase		      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     lowerCAmelCase		      =	IMAGE_TO_IMAGE_IMAGE_PARAMS


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						:			str								=	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=_UpperCAmelCase						,     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						,     )
          __A						:			str								=	EulerDiscreteScheduler(
              beta_start=0.00085						,     beta_end=0.012						,     steps_offset=1						,     beta_schedule='scaled_linear'						,     timestep_spacing='leading'						,     )
          torch.manual_seed(0)
          __A						:			Optional[int]								=	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)
          __A						:			Optional[Any]								=	CLIPTextConfig(
              bos_token_id=0						,     eos_token_id=2						,     hidden_size=32						,     intermediate_size=37						,     layer_norm_eps=1e-0_5						,     num_attention_heads=4						,     num_hidden_layers=5						,     pad_token_id=1						,     vocab_size=1000						,     hidden_act='gelu'						,     projection_dim=32						,     )
          __A						:			Union[str, Any]								=	CLIPTextModel(_UpperCAmelCase)
          __A						:			List[Any]								=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,     local_files_only=_UpperCAmelCase)

          __A						:			Optional[int]								=	CLIPTextModelWithProjection(_UpperCAmelCase)
          __A						:			Dict								=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,     local_files_only=_UpperCAmelCase)

          __A						:			Any								=	{
              '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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=0):

          '''simple docstring'''






          __A						:			Optional[int]								=	floats_tensor((1, 3, 32, 32)						,     rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)
          __A						:			Tuple								=	image / 2 + 0.5
          if str(_UpperCAmelCase).startswith('mps'):
               __A						:			Dict								=	torch.manual_seed(_UpperCAmelCase)
          else:
               __A						:			Tuple								=	torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						:			Dict								=	{
              '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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
          __A						:			Tuple								=	self.get_dummy_components()
          __A						:			int								=	StableDiffusionXLImgaImgPipeline(**_UpperCAmelCase)
          __A						:			List[Any]								=	sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			Any								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			List[Any]								=	sd_pipe(**_UpperCAmelCase).images
          __A						:			List[Any]								=	image[0, -3:, -3:, -1]

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

          __A						:			List[str]								=	np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165])

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().test_inference_batch_single_identical(expected_max_diff=3e-3)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.get_dummy_components()
          __A						:			Any								=	StableDiffusionXLImgaImgPipeline(**_UpperCAmelCase)
          __A						:			Dict								=	sd_pipe.to(_UpperCAmelCase)
          __A						:			Dict								=	sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          # forward without prompt embeds
          __A						:			List[str]								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			Tuple								=	3 * ['this is a negative prompt']
          __A						:			Any								=	negative_prompt
          __A						:			Optional[int]								=	3 * [inputs['prompt']]

          __A						:			Optional[int]								=	sd_pipe(**_UpperCAmelCase)
          __A						:			Union[str, Any]								=	output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __A						:			Any								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			Any								=	3 * ['this is a negative prompt']
          __A						:			Tuple								=	3 * [inputs.pop('prompt')]

          (
              (
              __A
          )			,(
              __A
          )			,(
              __A
          )			,(
              __A
          )			,
          )						:			Union[str, Any]								=	sd_pipe.encode_prompt(_UpperCAmelCase						,     negative_prompt=_UpperCAmelCase)

          __A						:			Union[str, Any]								=	sd_pipe(
              **_UpperCAmelCase						,     prompt_embeds=_UpperCAmelCase						,     negative_prompt_embeds=_UpperCAmelCase						,     pooled_prompt_embeds=_UpperCAmelCase						,     negative_pooled_prompt_embeds=_UpperCAmelCase						,     )
          __A						:			Optional[Any]								=	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 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase="cpu"						,     _UpperCAmelCase=torch.floataa						,     _UpperCAmelCase=0):

          '''simple docstring'''






          __A						:			int								=	torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						:			Any								=	np.random.RandomState(_UpperCAmelCase).standard_normal((1, 4, 64, 64))
          __A						:			Union[str, Any]								=	torch.from_numpy(_UpperCAmelCase).to(device=_UpperCAmelCase						,     dtype=_UpperCAmelCase)
          __A						:			Union[str, Any]								=	{
              '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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base')
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			int								=	self.get_inputs(_UpperCAmelCase)
          __A						:			int								=	pipe(**_UpperCAmelCase).images
          __A						:			Union[str, Any]								=	image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __A						:			str								=	np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
          assert np.abs(image_slice - expected_slice).max() < 7e-3






'''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 	_lowerCAmelCase      (  )					->					Union[str, Any]:
     __A						:			Optional[int]								=	argparse.ArgumentParser()
     parser.add_argument('--model_ckpt'			,							type=__snake_case			,							default='microsoft/unixcoder-base-nine'      )
     parser.add_argument('--num_epochs'			,							type=__snake_case			,							default=5      )
     parser.add_argument('--batch_size'			,							type=__snake_case			,							default=6      )
     parser.add_argument('--gradient_accumulation_steps'			,							type=__snake_case			,							default=1      )
     parser.add_argument('--freeze'			,							type=__snake_case			,							default=__snake_case      )
     parser.add_argument('--learning_rate'			,							type=__snake_case			,							default=5e-4      )
     parser.add_argument('--seed'			,							type=__snake_case			,							default=0      )
     parser.add_argument('--lr_scheduler_type'			,							type=__snake_case			,							default='cosine'      )
     parser.add_argument('--num_warmup_steps'			,							type=__snake_case			,							default=10      )
     parser.add_argument('--weight_decay'			,							type=__snake_case			,							default=0.01      )
     parser.add_argument('--output_dir'			,							type=__snake_case			,							default='./results'      )
     return parser.parse_args()


lowercase__   :				Tuple      =   load('''accuracy''')






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					Any:
     __A			,__A						:			List[Any]								=	eval_pred
     __A						:			Dict								=	np.argmax(__snake_case			,							axis=1      )
     return metric.compute(predictions=__snake_case			,							references=__snake_case      )


class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A						:			int								=	trainer


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          if control.should_evaluate:
               __A						:			str								=	deepcopy(_UpperCAmelCase)
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,     metric_key_prefix='train')
               return control_copy






def 	_lowerCAmelCase      (  )					->					str:
     __A						:			List[Any]								=	get_args()
     set_seed(args.seed      )

     __A						:			Union[str, Any]								=	load_dataset('codeparrot/codecomplex'			,							split='train'      )
     __A						:			Optional[int]								=	dataset.train_test_split(test_size=0.2      )
     __A						:			Union[str, Any]								=	train_test['test'].train_test_split(test_size=0.5      )
     __A						:			Optional[Any]								=	DatasetDict(
         {
             'train': train_test['train'],
             'test': test_validation['train'],
             'valid': test_validation['test'],
         }      )

     print('Loading tokenizer and model'      )
     __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(args.model_ckpt      )
     __A						:			Tuple								=	tokenizer.eos_token
     __A						:			Dict								=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt			,							num_labels=7      )
     __A						:			Optional[int]								=	model.config.eos_token_id

     if args.freeze:
          for param in model.roberta.parameters():
               __A						:			Optional[Any]								=	False

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

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

     __A						:			str								=	train_test_validation.map(
         __snake_case			,							batched=__snake_case			,							remove_columns=train_test_validation['train'].column_names			,							)
     __A						:			str								=	DataCollatorWithPadding(tokenizer=__snake_case      )

     __A						:			str								=	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						:			Tuple								=	Trainer(
         model=__snake_case			,							args=__snake_case			,							train_dataset=tokenized_datasets['train']			,							eval_dataset=tokenized_datasets['valid']			,							tokenizer=__snake_case			,							data_collator=__snake_case			,							compute_metrics=__snake_case			,							)

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


if __name__ == "__main__":
  main()