





"""simple docstring"""






import argparse
import json
import os

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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A 			=  16
A 			=  32

def     __A      (			a_						:Accelerator   ,       a_						:int = 16   ,       a_						:str = "bert-base-cased")		->		Any:
				__a :						Union[str, Any]			       =  AutoTokenizer.from_pretrained(a_)
				__a :						Optional[int]			       =  load_dataset('''glue'''   ,       '''mrpc''')

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				__a :						Optional[int]			       =  datasets.map(
				    a_   ,       batched=a_   ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']   ,       load_from_cache_file=a_)

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

				def collate_fn(a_						:Dict):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(a_   ,       padding='''max_length'''   ,       max_length=1_28   ,       return_tensors='''pt''')
								return tokenizer.pad(a_   ,       padding='''longest'''   ,       return_tensors='''pt''')

				# Instantiate dataloaders.
				__a :						int			       =  DataLoader(
				    tokenized_datasets['''train''']   ,       shuffle=a_   ,       collate_fn=a_   ,       batch_size=a_)
				__a :						int			       =  DataLoader(
				    tokenized_datasets['''validation''']   ,       shuffle=a_   ,       collate_fn=a_   ,       batch_size=a_)

				return train_dataloader, eval_dataloader

def     __A      (			a_						:Optional[Any]   ,       a_						:Union[str, Any])		->		List[Any]:
				# Initialize accelerator
				__a :						str			       =  Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__a :						Optional[int]			       =  config['''lr''']
				__a :						Optional[int]			       =  int(config['''num_epochs'''])
				__a :						Dict			       =  int(config['''seed'''])
				__a :						str			       =  int(config['''batch_size'''])
				__a :						List[str]			       =  args.model_name_or_path

				set_seed(a_)
				__a       ,						__a :						List[Any]			       =  get_dataloaders(a_   ,       a_   ,       a_)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__a :						List[str]			       =  AutoModelForSequenceClassification.from_pretrained(a_   ,       return_dict=a_)

				# Instantiate optimizer
				__a :						Tuple			       =  (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				__a :						Any			       =  optimizer_cls(params=model.parameters()   ,       lr=a_)

				if accelerator.state.deepspeed_plugin is not None:
								__a :						int			       =  accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								__a :						Dict			       =  1
				__a :						List[Any]			       =  (len(a_) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								__a :						Optional[int]			       =  get_linear_schedule_with_warmup(
								    optimizer=a_   ,       num_warmup_steps=0   ,       num_training_steps=a_   ,       )
				else:
								__a :						Optional[int]			       =  DummyScheduler(a_   ,       total_num_steps=a_   ,       warmup_num_steps=0)

				# 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.
				__a       ,						__a       ,						__a       ,						__a       ,						__a :						Tuple			       =  accelerator.prepare(
				    a_   ,       a_   ,       a_   ,       a_   ,       a_)

				# We need to keep track of how many total steps we have iterated over
				__a :						Tuple			       =  0
				# We also need to keep track of the stating epoch so files are named properly
				__a :						Tuple			       =  0

				# Now we train the model
				__a :						Tuple			       =  evaluate.load('''glue'''   ,       '''mrpc''')
				__a :						Optional[Any]			       =  0
				__a :						Tuple			       =  {}
				for epoch in range(a_   ,       a_):
								model.train()
								for step, batch in enumerate(a_):
												__a :						Any			       =  model(**a_)
												__a :						int			       =  outputs.loss
												__a :						List[Any]			       =  loss / gradient_accumulation_steps
												accelerator.backward(a_)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

								model.eval()
								__a :						str			       =  0
								for step, batch in enumerate(a_):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device)
												with torch.no_grad():
																__a :						int			       =  model(**a_)
												__a :						List[Any]			       =  outputs.logits.argmax(dim=-1)
												# It is slightly faster to call this once, than multiple times
												__a       ,						__a :						Optional[Any]			       =  accelerator.gather(
												    (predictions, batch['''labels''']))  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																if step == len(a_) - 1:
																				__a :						Any			       =  predictions[: len(eval_dataloader.dataset) - samples_seen]
																				__a :						Any			       =  references[: len(eval_dataloader.dataset) - samples_seen]
																else:
																				samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=a_   ,       references=a_   ,       )

								__a :						Tuple			       =  metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""   ,       a_)
								__a :						int			       =  eval_metric['''accuracy''']

								if best_performance < eval_metric["accuracy"]:
												__a :						Dict			       =  eval_metric['''accuracy''']

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir   ,       '''all_results.json''')   ,       '''w''') as f:
												json.dump(a_   ,       a_)

def     __A      (			)		->		Union[str, Any]:
				__a :						str			       =  argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''')
				parser.add_argument(
				    '''--model_name_or_path'''   ,       type=a_   ,       default='''bert-base-cased'''   ,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,       required=a_   ,       )
				parser.add_argument(
				    '''--output_dir'''   ,       type=a_   ,       default='''.'''   ,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,       )
				parser.add_argument(
				    '''--performance_lower_bound'''   ,       type=a_   ,       default=a_   ,       help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''   ,       )
				parser.add_argument(
				    '''--num_epochs'''   ,       type=a_   ,       default=3   ,       help='''Number of train epochs.'''   ,       )
				__a :						Optional[Any]			       =  parser.parse_args()
				__a :						Optional[int]			       =  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(a_   ,       a_)


if __name__ == "__main__":
		main()






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A 			=  {
    '''configuration_jukebox''': [
        '''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''JukeboxConfig''',
        '''JukeboxPriorConfig''',
        '''JukeboxVQVAEConfig''',
    ],
    '''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A 			=  [
		    '''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''JukeboxModel''',
		    '''JukeboxPreTrainedModel''',
		    '''JukeboxVQVAE''',
		    '''JukeboxPrior''',
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		A 			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)