
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Model name or path of model to be trained."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="./"  ,       metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot-clean-train"  ,       metadata={"help": "Name or path of training dataset."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot-clean-valid"  ,       metadata={"help": "Name or path of validation dataset."}       )
		__lowercase	:	Optional[int]     					=    field(default=2  ,       metadata={"help": "Batch size for training."}       )
		__lowercase	:	Optional[int]     					=    field(default=2  ,       metadata={"help": "Batch size for evaluation."}       )
		__lowercase	:	Optional[float]     					=    field(default=0.1  ,       metadata={"help": "Value of weight decay."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=10_000  ,       metadata={"help": "Size of buffer used to shuffle streaming dataset."}       )
		__lowercase	:	Optional[float]     					=    field(default=2e-4  ,       metadata={"help": "Learning rate fo training."}       )
		__lowercase	:	Optional[str]     					=    field(default="cosine"  ,       metadata={"help": "Learning rate."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=750  ,       metadata={"help": "Number of warmup steps in the learning rate schedule."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=16  ,       metadata={"help": "Number of gradient accumulation steps."}       )
		__lowercase	:	Optional[bool]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Use gradient checkpointing to reduce memory footprint."}       )
		__lowercase	:	Optional[int]     					=    field(default=50_000  ,       metadata={"help": "Maximum number of training steps."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=-1  ,       metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=1_024  ,       metadata={"help": "Sequence lengths used for training."}       )
		__lowercase	:	Optional[int]     					=    field(default=1  ,       metadata={"help": "Training seed."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=1_024  ,       metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "States path if the training should continue from a checkpoint folder."}       )
		__lowercase	:	Optional[bool]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "If True the data is pretokenized."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Model name or path of model to be evaluated."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot-clean-valid"  ,       metadata={"help": "Name or path of validation dataset."}       )
		__lowercase	:	Optional[int]     					=    field(default=2  ,       metadata={"help": "Batch size used for evaluation."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=-1  ,       metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=1_024  ,       metadata={"help": "Length of sequences to be evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=1  ,       metadata={"help": "Random seed used for evaluation."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Model name or path of model to be evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Number of workers used for code evaluation."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}  ,       )
		__lowercase	:	Optional[bool]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Sample from the language model's output distribution."}       )
		__lowercase	:	Optional[float]     					=    field(default=0.2  ,       metadata={"help": "Sampling temperature used for generation."}       )
		__lowercase	:	Optional[int]     					=    field(default=256  ,       metadata={"help": "Maximum number of newly generated tokens."}       )
		__lowercase	:	Optional[int]     					=    field(default=0  ,       metadata={"help": "Top-k parameter used for generation."}       )
		__lowercase	:	Optional[float]     					=    field(default=0.9_5  ,       metadata={"help": "Top-p parameter used for nucleus sampling."}       )
		__lowercase	:	Optional[int]     					=    field(default=10  ,       metadata={"help": "Number of generations to run in parallel."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=200  ,       metadata={"help": "Number of completions to generate for each sample."}       )
		__lowercase	:	Optional[int]     					=    field(default=1  ,       metadata={"help": "Random seed used for evaluation."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="eval_results.json"  ,       metadata={"help": "Random seed used for evaluation."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="0"  ,       metadata={"help": "Allow `code_eval` to execute Python code on machine"}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=-1  ,       metadata={
		        "help": (
		            "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
		            " number corresponds to which GPU device id to run on."
		        )
		    }  ,       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[int]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={
		        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
		    }  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default="transformersbook/codeparrot"  ,       metadata={"help": "Folder or name of dataset to process."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot-clean"  ,       metadata={"help": "Folder to save processed processed dataset."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=100_000  ,       metadata={"help": "Number of files to save per JSON output file."}       )
		__lowercase	:	Optional[str]     					=    field(default="content"  ,       metadata={"help": "Column containing text data to process."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=1_000  ,       metadata={"help": "Maximum line length in file, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=100  ,       metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=0.2_5  ,       metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=1.5  ,       metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=0.7  ,       metadata={"help": "Probability for filtering config, test and uncommon files."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Name or path to the tokenizer."}  ,       )
		__lowercase	:	Optional[bool]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "If True, near-duplicate samples are removed."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=0.8_5  ,       metadata={"help": "Jaccard threshold for near-duplicate samples."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="gpt2"  ,       metadata={"help": "Base tokenizer to build new tokenizer from."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="transformersbook/codeparrot-train"  ,       metadata={"help": "Dataset to train tokenizer on."}       )
		__lowercase	:	Optional[str]     					=    field(default="content"  ,       metadata={"help": "Column containing text data to process."}       )
		__lowercase	:	Optional[int]     					=    field(default=200_000  ,       metadata={"help": "Number of examples to train tokenizer on."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=32_768  ,       metadata={"help": "Number of examples to train the tokenizer on."}       )
		__lowercase	:	Optional[str]     					=    field(default="codeparrot"  ,       metadata={"help": "Name of new tokenizer."}       )
		__lowercase	:	Optional[bool]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Push saved tokenizer to the hub."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Name or path to the tokenizer."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot-clean-train"  ,       metadata={"help": "Name or path to the dataset to pretokenize."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="tokenized-codeparrot-train"  ,       metadata={"help": "Repo name of the pretokenized data."}       )
		__lowercase	:	Optional[int]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Number of workers used for code evaluation."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="gpt2-large"  ,       metadata={"help": "Configuration to use for model initialization."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Tokenizer attached to model."}       )
		__lowercase	:	Optional[str]     					=    field(default="codeparrot"  ,       metadata={"help": "Name of the created model."}       )
		__lowercase	:	Optional[bool]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Push saved tokenizer to the hub."}       )


import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase :			Union[str, Any]              =	16
_UpperCAmelCase :			Dict              =	32


def 				A	( lowercase   ,	lowercase = 16			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					AutoTokenizer.from_pretrained('bert-base-cased'			)
		UpperCamelCase          =					load_dataset('glue'   ,	'mrpc'			)

		def tokenize_function(lowercase			):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase          =					tokenizer(examples['sentence1']   ,	examples['sentence2']   ,	truncation=lowercase   ,	max_length=lowercase			)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				UpperCamelCase          =					datasets.map(
				    lowercase   ,	batched=lowercase   ,	remove_columns=['idx', 'sentence1', 'sentence2']   ,	)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		UpperCamelCase          =					tokenized_datasets.rename_column('label'   ,	'labels'			)

		def collate_fn(lowercase			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				UpperCamelCase          =					128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						UpperCamelCase          =					16
				elif accelerator.mixed_precision != "no":
						UpperCamelCase          =					8
				else:
						UpperCamelCase          =					None

				return tokenizer.pad(
				    lowercase   ,	padding='longest'   ,	max_length=lowercase   ,	pad_to_multiple_of=lowercase   ,	return_tensors='pt'   ,	)

		# Instantiate dataloaders.
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['train']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=lowercase			)
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['validation']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=(accelerator.mixed_precision == 'fp8')   ,	)

		return train_dataloader, eval_dataloader


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision			)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCamelCase          =					config['lr']
		UpperCamelCase          =					int(config['num_epochs']			)
		UpperCamelCase          =					int(config['seed']			)
		UpperCamelCase          =					int(config['batch_size']			)

		UpperCamelCase          =					evaluate.load('glue'   ,	'mrpc'			)

		# If the batch size is too big we use gradient accumulation
		UpperCamelCase          =					1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				UpperCamelCase          =					batch_size // MAX_GPU_BATCH_SIZE
				UpperCamelCase          =					MAX_GPU_BATCH_SIZE

		set_seed(lowercase			)
		UpperCamelCase     ,  UpperCamelCase          =					get_dataloaders(lowercase   ,	lowercase			)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		UpperCamelCase          =					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,	return_dict=lowercase			)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		UpperCamelCase          =					model.to(accelerator.device			)
		# Instantiate optimizer
		UpperCamelCase          =					AdamW(params=model.parameters()   ,	lr=lowercase			)

		# Instantiate scheduler
		UpperCamelCase          =					get_linear_schedule_with_warmup(
		    optimizer=lowercase   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowercase			) * num_epochs) // gradient_accumulation_steps   ,	)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.

		UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					accelerator.prepare(
		    lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)

		# Now we train the model
		for epoch in range(lowercase			):
				model.train()
				for step, batch in enumerate(lowercase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.loss
						UpperCamelCase          =					loss / gradient_accumulation_steps
						accelerator.backward(lowercase			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(lowercase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						with torch.no_grad():
								UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.logits.argmax(dim=-1			)
						UpperCamelCase     ,  UpperCamelCase          =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
						metric.add_batch(
						    predictions=lowercase   ,	references=lowercase   ,	)

				UpperCamelCase          =					metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''   ,	lowercase			)


def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					argparse.ArgumentParser(description='Simple example of training script.'			)
		parser.add_argument(
		    '--mixed_precision'   ,	type=lowercase   ,	default=lowercase   ,	choices=['no', 'fp16', 'bf16', 'fp8']   ,	help='Whether to use mixed precision. Choose'
		    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
		    'and an Nvidia Ampere GPU.'   ,	)
		parser.add_argument('--cpu'   ,	action='store_true'   ,	help='If passed, will train on the CPU.'			)
		UpperCamelCase          =					parser.parse_args()
		UpperCamelCase          =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowercase   ,	lowercase			)


if __name__ == "__main__":
					main()
