


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   ( ):
							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=1_0   )
							parser.add_argument('--weight_decay'					,    type=__lowerCAmelCase					,    default=0.01   )
							parser.add_argument('--output_dir'					,    type=__lowerCAmelCase					,    default='./results'   )
							return parser.parse_args()


snake_case							:       Optional[int]     			=      load('''accuracy''')






def 							__lowercase   ( __lowerCAmelCase							:				str   ):
							a__      ,					a__             =     eval_pred
							a__             =     np.argmax(__lowerCAmelCase					,    axis=1   )
							return metric.compute(predictions=__lowerCAmelCase					,    references=__lowerCAmelCase   )







class        snake_case_   (lowerCamelCase_      ):
					def __init__(      self   :Dict			,__snake_case   :Optional[Any]      )					->    None:
												super().__init__()
												a__             =     trainer



					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]			,__snake_case   :Any			,__snake_case   :Dict			,**__snake_case   :List[str]      )					->    int:
												if control.should_evaluate:
																			a__             =     deepcopy(__snake_case      )
																			self._trainer.evaluate(eval_dataset=self._trainer.train_dataset			,metric_key_prefix='train'      )
																			return control_copy






def 							__lowercase   ( ):
							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							:				int   ):
														a__             =     tokenizer(example['src']					,    truncation=__lowerCAmelCase					,    max_length=1_0_2_4   )
														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=0.01					,    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()








import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


snake_case							:       int     			=      False







@skip_mps
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Union[str, Any]          =			StableDiffusionAttendAndExcitePipeline
					UpperCAmelCase__					:    List[str]          =			False
					UpperCAmelCase__					:    int          =			TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__					:    List[Any]          =			TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
					UpperCAmelCase__					:    List[str]          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__					:    str          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					@classmethod
					def  lowerCamelCase__(      cls   :int      )					->    int:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    int:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Dict      )					->    Optional[Any]:
												torch.manual_seed(0      )
												a__             =     UNetaDConditionModel(
												    block_out_channels=(32, 64)			,layers_per_block=1			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,cross_attention_dim=32			,attention_head_dim=(2, 4)			,use_linear_projection=__snake_case			,)
												a__             =     DDIMScheduler(
												    beta_start=0.0_00_85			,beta_end=0.0_12			,beta_schedule='scaled_linear'			,clip_sample=__snake_case			,set_alpha_to_one=__snake_case			,)
												torch.manual_seed(0      )
												a__             =     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=1_28			,)
												torch.manual_seed(0      )
												a__             =     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=10_00			,hidden_act='gelu'			,projection_dim=5_12			,)
												a__             =     CLIPTextModel(__snake_case      )
												a__             =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

												a__             =     {
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}

												return components
					def  lowerCamelCase__(      self   :Any			,__snake_case   :Union[str, Any]			,__snake_case   :Tuple=0      )					->    Any:
												if str(__snake_case      ).startswith('mps'      ):
																			a__             =     torch.manual_seed(__snake_case      )
												else:
																			a__             =     torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )
												a__             =     a__             =     {
												    'prompt': 'a cat and a frog',
												    'token_indices': [2, 5],
												    'generator': generator,
												    'num_inference_steps': 1,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												    'max_iter_to_alter': 2,
												    'thresholds': {0: 0.7},
												}
												return inputs
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Any:
												a__             =     'cpu'

												a__             =     self.get_dummy_components()
												a__             =     self.pipeline_class(**__snake_case      )
												pipe.to(__snake_case      )
												pipe.set_progress_bar_config(disable=__snake_case      )

												a__             =     self.get_dummy_inputs(__snake_case      )
												a__             =     pipe(**__snake_case      ).images
												a__             =     image[0, -3:, -3:, -1]

												self.assertEqual(image.shape			,(1, 64, 64, 3)      )
												a__             =     np.array(
												    [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]      )
												a__             =     np.abs(image_slice.flatten() - expected_slice      ).max()
												self.assertLessEqual(__snake_case			,1E-3      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    List[str]:
												super().test_cpu_offload_forward_pass(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    List[str]:
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[int]:
												self._test_inference_batch_single_identical(batch_size=2			,expected_max_diff=7E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    Dict:
												super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Dict:
												super().test_save_load_local(expected_max_difference=5E-4      )



					def  lowerCamelCase__(      self   :List[Any]      )					->    List[Any]:
												super().test_save_load_optional_components(expected_max_difference=4E-4      )







@require_torch_gpu
@slow
class        snake_case_   (unittest.TestCase      ):
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    Tuple:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[int]      )					->    Tuple:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Tuple:
												a__             =     torch.manual_seed(51      )

												a__             =     StableDiffusionAttendAndExcitePipeline.from_pretrained(
												    'CompVis/stable-diffusion-v1-4'			,safety_checker=__snake_case			,torch_dtype=torch.floataa      )
												pipe.to('cuda'      )

												a__             =     'a painting of an elephant with glasses'
												a__             =     [5, 7]

												a__             =     pipe(
												    prompt=__snake_case			,token_indices=__snake_case			,guidance_scale=7.5			,generator=__snake_case			,num_inference_steps=5			,max_iter_to_alter=5			,output_type='numpy'			,).images[0]

												a__             =     load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'      )
												assert np.abs((expected_image - image).max()      ) < 5E-1




