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 							UpperCamelCase				(    )		->						Union[str, Any]:




       '''simple docstring'''






       _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=0.01   )
       parser.add_argument('--output_dir'       ,					type=lowerCAmelCase_       ,					default='./results'   )
       return parser.parse_args()


UpperCAmelCase_								=							load('''accuracy''')

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       _A,			_A=	eval_pred
       _A=	np.argmax(lowerCAmelCase_       ,					axis=1   )
       return metric.compute(predictions=lowerCAmelCase_       ,					references=lowerCAmelCase_   )





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__       ):
            super().__init__()
            _A=	trainer





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            if control.should_evaluate:
                   _A=	deepcopy(lowerCAmelCase__       )
                   self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,      metric_key_prefix='train'       )
                   return control_copy


def 							UpperCamelCase				(    )		->						List[Any]:




       '''simple docstring'''






       _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=10_24   )
              _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, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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


enable_full_determinism()





class      lowerCAmelCase						(					_a			,		_a			,		_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=AltDiffusionPipeline
     _SCREAMING_SNAKE_CASE     :     int		=TEXT_TO_IMAGE_PARAMS
     _SCREAMING_SNAKE_CASE     :     List[Any]		=TEXT_TO_IMAGE_BATCH_PARAMS
     _SCREAMING_SNAKE_CASE     :     Dict		=TEXT_TO_IMAGE_IMAGE_PARAMS
     _SCREAMING_SNAKE_CASE     :     Any		=TEXT_TO_IMAGE_IMAGE_PARAMS



     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	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')				,      cross_attention_dim=32				,      )
            _A=	DDIMScheduler(
                beta_start=0.00085				,      beta_end=0.012				,      beta_schedule='scaled_linear'				,      clip_sample=lowerCAmelCase__				,      set_alpha_to_one=lowerCAmelCase__				,      )
            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				,      )

            # TODO: address the non-deterministic text encoder (fails for save-load tests)
            # torch.manual_seed(0)
            # text_encoder_config = RobertaSeriesConfig(
            #     hidden_size=32,
            #     project_dim=32,
            #     intermediate_size=37,
            #     layer_norm_eps=1e-05,
            #     num_attention_heads=4,
            #     num_hidden_layers=5,
            #     vocab_size=5002,
            # )
            # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

            torch.manual_seed(0       )
            _A=	CLIPTextConfig(
                bos_token_id=0				,      eos_token_id=2				,      hidden_size=32				,      projection_dim=32				,      intermediate_size=37				,      layer_norm_eps=1E-05				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=5002				,      )
            _A=	CLIPTextModel(lowerCAmelCase__       )

            _A=	XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
            _A=	77

            _A=	{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=0       ):
            if str(lowerCAmelCase__       ).startswith('mps'       ):
                   _A=	torch.manual_seed(lowerCAmelCase__       )
            else:
                   _A=	torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )
            _A=	{
                'prompt': 'A painting of a squirrel eating a burger',
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 6.0,
                'output_type': 'numpy',
            }
            return inputs



     def 			a__  (					self       ):
            super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )



     def 			a__  (					self       ):
            super().test_inference_batch_single_identical(expected_max_diff=3E-3       )



     def 			a__  (					self       ):
            _A=	'cpu'  # ensure determinism for the device-dependent torch.Generator

            _A=	self.get_dummy_components()
            torch.manual_seed(0       )
            _A=	RobertaSeriesConfig(
                hidden_size=32				,      project_dim=32				,      intermediate_size=37				,      layer_norm_eps=1E-05				,      num_attention_heads=4				,      num_hidden_layers=5				,      vocab_size=5002				,      )
            # TODO: remove after fixing the non-deterministic text encoder
            _A=	RobertaSeriesModelWithTransformation(lowerCAmelCase__       )
            _A=	text_encoder

            _A=	AltDiffusionPipeline(**lowerCAmelCase__       )
            _A=	alt_pipe.to(lowerCAmelCase__       )
            alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__       )
            _A=	'A photo of an astronaut'
            _A=	alt_pipe(**lowerCAmelCase__       )
            _A=	output.images
            _A=	image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            _A=	np.array(
                [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]       )

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





     def 			a__  (					self       ):
            _A=	'cpu'  # ensure determinism for the device-dependent torch.Generator

            _A=	self.get_dummy_components()
            _A=	PNDMScheduler(skip_prk_steps=lowerCAmelCase__       )
            torch.manual_seed(0       )
            _A=	RobertaSeriesConfig(
                hidden_size=32				,      project_dim=32				,      intermediate_size=37				,      layer_norm_eps=1E-05				,      num_attention_heads=4				,      num_hidden_layers=5				,      vocab_size=5002				,      )
            # TODO: remove after fixing the non-deterministic text encoder
            _A=	RobertaSeriesModelWithTransformation(lowerCAmelCase__       )
            _A=	text_encoder
            _A=	AltDiffusionPipeline(**lowerCAmelCase__       )
            _A=	alt_pipe.to(lowerCAmelCase__       )
            alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__       )
            _A=	alt_pipe(**lowerCAmelCase__       )
            _A=	output.images
            _A=	image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            _A=	np.array(
                [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]       )

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





@slow
@require_torch_gpu
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()



     def 			a__  (					self       ):
            # make sure here that pndm scheduler skips prk
            _A=	AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'				,      safety_checker=lowerCAmelCase__       )
            _A=	alt_pipe.to(lowerCAmelCase__       )
            alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	'A painting of a squirrel eating a burger'
            _A=	torch.manual_seed(0       )
            _A=	alt_pipe([prompt]				,      generator=lowerCAmelCase__				,      guidance_scale=6.0				,      num_inference_steps=20				,      output_type='np'       )

            _A=	output.images

            _A=	image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A=	np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]       )

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





     def 			a__  (					self       ):
            _A=	DDIMScheduler.from_pretrained('BAAI/AltDiffusion'				,      subfolder='scheduler'       )

            _A=	AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'				,      scheduler=lowerCAmelCase__				,      safety_checker=lowerCAmelCase__       )
            _A=	alt_pipe.to(lowerCAmelCase__       )
            alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	'A painting of a squirrel eating a burger'
            _A=	torch.manual_seed(0       )

            _A=	alt_pipe([prompt]				,      generator=lowerCAmelCase__				,      num_inference_steps=2				,      output_type='numpy'       )
            _A=	output.images

            _A=	image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A=	np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]       )

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