



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class        SCREAMING_SNAKE_CASE(							a__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= KandinskyVaaPipeline
	lowerCamelCase__    				= [
	    "image_embeds",
	    "negative_image_embeds",
	]
	lowerCamelCase__    				= ["image_embeds", "negative_image_embeds"]
	lowerCamelCase__    				= [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	lowerCamelCase__    				= False







	@property
	def   A			(		self    :  Tuple	)    ->	Optional[int]:
								return 32







	@property
	def   A			(		self    :  Optional[int]	)    ->	int:
								return 32







	@property
	def   A			(		self    :  List[str]	)    ->	List[str]:
								return self.time_input_dim







	@property
	def   A			(		self    :  List[Any]	)    ->	Union[str, Any]:
								return self.time_input_dim * 4







	@property
	def   A			(		self    :  List[str]	)    ->	int:
								return 100







	@property
	def   A			(		self    :  Any	)    ->	str:
								torch.manual_seed(0	)

								UpperCAmelCase				: List[Any]											=	{
								    '''in_channels''': 4,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''image''',
								    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
								    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
								    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
								    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
								    '''layers_per_block''': 1,
								    '''encoder_hid_dim''': self.text_embedder_hidden_size,
								    '''encoder_hid_dim_type''': '''image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								UpperCAmelCase				: List[Any]											=	UNetaDConditionModel(**lowerCamelCase_	)
								return model







	@property
	def   A			(		self    :  List[Any]	)    ->	Optional[int]:
								return {
								    "block_out_channels": [32, 64],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}







	@property
	def   A			(		self    :  List[str]	)    ->	List[str]:
								torch.manual_seed(0	)
								UpperCAmelCase				: Optional[int]											=	VQModel(**self.dummy_movq_kwargs	)
								return model







	def   A			(		self    :  Tuple	)    ->	Tuple:
								UpperCAmelCase				: Union[str, Any]											=	self.dummy_unet
								UpperCAmelCase				: Optional[int]											=	self.dummy_movq

								UpperCAmelCase				: str											=	DDIMScheduler(
								    num_train_timesteps=1000	,    beta_schedule='''linear'''	,    beta_start=0.0_00_85	,    beta_end=0.0_12	,    clip_sample=lowerCamelCase_	,    set_alpha_to_one=lowerCamelCase_	,    steps_offset=1	,    prediction_type='''epsilon'''	,    thresholding=lowerCamelCase_	,    )

								UpperCAmelCase				: Dict											=	{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}
								return components







	def   A			(		self    :  Dict	,    __snake_case    :  List[str]	,    __snake_case    :  str=0	)    ->	Dict:
								UpperCAmelCase				: Optional[int]											=	floats_tensor((1, self.text_embedder_hidden_size)	,    rng=random.Random(lowerCamelCase_	)	).to(lowerCamelCase_	)
								UpperCAmelCase				: Optional[int]											=	floats_tensor((1, self.text_embedder_hidden_size)	,    rng=random.Random(seed + 1	)	).to(
								    lowerCamelCase_	)
								if str(lowerCamelCase_	).startswith('''mps'''	):
															UpperCAmelCase				: Optional[Any]											=	torch.manual_seed(lowerCamelCase_	)
								else:
															UpperCAmelCase				: Any											=	torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
								UpperCAmelCase				: Tuple											=	{
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 64,
								    '''width''': 64,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs







	def   A			(		self    :  str	)    ->	Optional[Any]:
								UpperCAmelCase				: Any											=	'''cpu'''

								UpperCAmelCase				: List[str]											=	self.get_dummy_components()

								UpperCAmelCase				: Any											=	self.pipeline_class(**lowerCamelCase_	)
								UpperCAmelCase				: Dict											=	pipe.to(lowerCamelCase_	)

								pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								UpperCAmelCase				: Any											=	pipe(**self.get_dummy_inputs(lowerCamelCase_	)	)
								UpperCAmelCase				: Optional[Any]											=	output.images

								UpperCAmelCase				: Any											=	pipe(
								    **self.get_dummy_inputs(lowerCamelCase_	)	,    return_dict=lowerCamelCase_	,    )[0]

								UpperCAmelCase				: Dict											=	image[0, -3:, -3:, -1]
								UpperCAmelCase				: Dict											=	image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								UpperCAmelCase				: str											=	np.array(
								    [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]	)

								assert (
								    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""

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







	"""simple docstring"""






	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def   A			(		self    :  Optional[Any]	)    ->	str:
								UpperCAmelCase				: Union[str, Any]											=	load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''	)

								UpperCAmelCase				: Dict											=	KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''	,    torch_dtype=torch.floataa	)
								pipe_prior.to(lowerCamelCase_	)

								UpperCAmelCase				: Union[str, Any]											=	KandinskyVaaPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder'''	,    torch_dtype=torch.floataa	)
								UpperCAmelCase				: List[Any]											=	pipeline.to(lowerCamelCase_	)
								pipeline.set_progress_bar_config(disable=lowerCamelCase_	)

								UpperCAmelCase				: Dict											=	'''red cat, 4k photo'''

								UpperCAmelCase				: List[str]											=	torch.Generator(device='''cuda'''	).manual_seed(0	)
								UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	pipe_prior(
								    lowerCamelCase_	,    generator=lowerCamelCase_	,    num_inference_steps=5	,    negative_prompt=''''''	,    ).to_tuple()

								UpperCAmelCase				: Tuple											=	torch.Generator(device='''cuda'''	).manual_seed(0	)
								UpperCAmelCase				: Optional[Any]											=	pipeline(
								    image_embeds=lowerCamelCase_	,    negative_image_embeds=lowerCamelCase_	,    generator=lowerCamelCase_	,    num_inference_steps=100	,    output_type='''np'''	,    )

								UpperCAmelCase				: Optional[Any]											=	output.images[0]

								assert image.shape == (512, 512, 3)

								assert_mean_pixel_difference(lowerCamelCase_	,    lowerCamelCase_	)










'''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_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Any											=	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()


UpperCamelCase__:     Any										=    load("accuracy")


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

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  Optional[int]	)    ->	None:
								super().__init__()
								UpperCAmelCase				: Dict											=	trainer







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


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	get_args()
							set_seed(args.seed						)

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

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

							if args.freeze:
														for param in model.roberta.parameters():
																					UpperCAmelCase				: Any											=	False

							UpperCAmelCase				: Optional[Any]											=	ClassLabel(num_classes=7     ,   names=list(set(train_test_validation['''train''']['''complexity''']						)						)						)

							def tokenize(_lowerCAmelCase						:		Any						):
														UpperCAmelCase				: List[Any]											=	tokenizer(example['''src''']     ,   truncation=_lowerCAmelCase     ,   max_length=1024						)
														UpperCAmelCase				: Optional[Any]											=	labels.straint(example['''complexity''']						)
														return {
														    "input_ids": inputs["input_ids"],
														    "attention_mask": inputs["attention_mask"],
														    "label": label,
														}

							UpperCAmelCase				: List[str]											=	train_test_validation.map(
							    _lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   remove_columns=train_test_validation['''train'''].column_names     ,   )
							UpperCAmelCase				: int											=	DataCollatorWithPadding(tokenizer=_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	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'''     ,   )

							UpperCAmelCase				: int											=	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()





