


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__	(      )    ->	int:





			"""simple docstring"""
			a__ :List[Any]						  =    argparse.ArgumentParser()
			parser.add_argument("--model_ckpt"	,  type=a	,  default="microsoft/unixcoder-base-nine"				)
			parser.add_argument("--num_epochs"	,  type=a	,  default=5				)
			parser.add_argument("--batch_size"	,  type=a	,  default=6				)
			parser.add_argument("--gradient_accumulation_steps"	,  type=a	,  default=1				)
			parser.add_argument("--freeze"	,  type=a	,  default=a				)
			parser.add_argument("--learning_rate"	,  type=a	,  default=5e-4				)
			parser.add_argument("--seed"	,  type=a	,  default=0				)
			parser.add_argument("--lr_scheduler_type"	,  type=a	,  default="cosine"				)
			parser.add_argument("--num_warmup_steps"	,  type=a	,  default=10				)
			parser.add_argument("--weight_decay"	,  type=a	,  default=0.0_1				)
			parser.add_argument("--output_dir"	,  type=a	,  default="./results"				)
			return parser.parse_args()


snake_case__			 =						load('''accuracy''')


def 				lowerCamelCase__	(      a	:   str				)    ->	Optional[int]:





			"""simple docstring"""
			a__  ,  a__ :Union[str, Any]						  =    eval_pred
			a__ :Any						  =    np.argmax(a	,  axis=1				)
			return metric.compute(predictions=a	,  references=a				)


class 					lowerCAmelCase_ (       _a):




						def __init__(	self						:   List[Any]   ,     __A						:   Optional[Any]						)      ->None:






									"""simple docstring"""






									super().__init__()
									a__ :Dict						  =    trainer






						def  _snake_case					(	self						:   Any   ,     __A						:   Tuple   ,     __A						:   int   ,     __A						:   Optional[Any]   ,     **__A						:   str						)      ->List[Any]:






									"""simple docstring"""






									if control.should_evaluate:
												a__ :Tuple						  =    deepcopy(__A						)
												self._trainer.evaluate(eval_dataset=self._trainer.train_dataset   ,     metric_key_prefix="train"						)
												return control_copy






def 				lowerCamelCase__	(      )    ->	Dict:





			"""simple docstring"""
			a__ :str						  =    get_args()
			set_seed(args.seed				)

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

			print("Loading tokenizer and model"				)
			a__ :List[str]						  =    AutoTokenizer.from_pretrained(args.model_ckpt				)
			a__ :Dict						  =    tokenizer.eos_token
			a__ :Tuple						  =    AutoModelForSequenceClassification.from_pretrained(args.model_ckpt	,  num_labels=7				)
			a__ :Dict						  =    model.config.eos_token_id

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

			a__ :Any						  =    ClassLabel(num_classes=7	,  names=list(set(train_test_validation["train"]["complexity"]				)				)				)

			def tokenize(a	:   List[Any]				):
						a__ :Optional[int]						  =    tokenizer(example["src"]	,  truncation=a	,  max_length=1_024				)
						a__ :List[Any]						  =    labels.straint(example["complexity"]				)
						return {
						    "input_ids": inputs["input_ids"],
						    "attention_mask": inputs["attention_mask"],
						    "label": label,
						}

			a__ :Dict						  =    train_test_validation.map(
			    a	,  batched=a	,  remove_columns=train_test_validation["train"].column_names	,  )
			a__ :Optional[Any]						  =    DataCollatorWithPadding(tokenizer=a				)

			a__ :List[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=0.0_1	,  metric_for_best_model="accuracy"	,  run_name="complexity-java"	,  report_to="wandb"	,  )

			a__ :str						  =    Trainer(
			    model=a	,  args=a	,  train_dataset=tokenized_datasets["train"]	,  eval_dataset=tokenized_datasets["valid"]	,  tokenizer=a	,  data_collator=a	,  compute_metrics=a	,  )

			print("Training..."				)
			trainer.add_callback(CustomCallback(a				)				)
			trainer.train()


if __name__ == "__main__":
		main()






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		snake_case__			 =						argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)
		# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
		parser.add_argument(
		    '''--original_config_file''',
		    default=None,
		    type=str,
		    help='''The YAML config file corresponding to the original architecture.''',
		)
		parser.add_argument(
		    '''--num_in_channels''',
		    default=None,
		    type=int,
		    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
		)
		parser.add_argument(
		    '''--scheduler_type''',
		    default='''pndm''',
		    type=str,
		    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
		)
		parser.add_argument(
		    '''--pipeline_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
		        '''. If `None` pipeline will be automatically inferred.'''
		    ),
		)
		parser.add_argument(
		    '''--image_size''',
		    default=None,
		    type=int,
		    help=(
		        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
		        ''' Base. Use 768 for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--prediction_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
		        ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--extract_ema''',
		    action='''store_true''',
		    help=(
		        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
		        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
		        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
		    ),
		)
		parser.add_argument(
		    '''--upcast_attention''',
		    action='''store_true''',
		    help=(
		        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
		        ''' diffusion 2.1.'''
		    ),
		)
		parser.add_argument(
		    '''--from_safetensors''',
		    action='''store_true''',
		    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
		)
		parser.add_argument(
		    '''--to_safetensors''',
		    action='''store_true''',
		    help='''Whether to store pipeline in safetensors format or not.''',
		)
		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
		parser.add_argument(
		    '''--stable_unclip''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
		)
		parser.add_argument(
		    '''--stable_unclip_prior''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
		)
		parser.add_argument(
		    '''--clip_stats_path''',
		    type=str,
		    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
		    required=False,
		)
		parser.add_argument(
		    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
		)
		parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
		parser.add_argument(
		    '''--vae_path''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
		)
		snake_case__			 =						parser.parse_args()

		snake_case__			 =						download_from_original_stable_diffusion_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    prediction_type=args.prediction_type,
		    model_type=args.pipeline_type,
		    extract_ema=args.extract_ema,
		    scheduler_type=args.scheduler_type,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    stable_unclip=args.stable_unclip,
		    stable_unclip_prior=args.stable_unclip_prior,
		    clip_stats_path=args.clip_stats_path,
		    controlnet=args.controlnet,
		    vae_path=args.vae_path,
		)

		if args.half:
				pipe.to(torch_dtype=torch.floataa)

		if args.controlnet:
				# only save the controlnet model
				pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
		else:
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


