






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


__UpperCAmelCase								=						16
__UpperCAmelCase								=						32




def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							Union[str, Any] = 16     ,       __snake_case    :							Tuple = "bert-base-cased"  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						AutoTokenizer.from_pretrained(lowerCamelCase_  )
					UpperCAmelCase_    :      Dict            =						load_dataset('glue'     ,       'mrpc'  )

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

					# Apply the method we just defined to all the examples in all the splits of the dataset
					UpperCAmelCase_    :      List[str]            =						datasets.map(
					    lowerCamelCase_     ,       batched=lowerCamelCase_     ,       remove_columns=['idx', 'sentence1', 'sentence2']     ,       load_from_cache_file=lowerCamelCase_  )

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

					def collate_fn(__snake_case    :							Optional[Any]  ):
										# 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(lowerCamelCase_     ,       padding='max_length'     ,       max_length=128     ,       return_tensors='pt'  )
										return tokenizer.pad(lowerCamelCase_     ,       padding='longest'     ,       return_tensors='pt'  )

					# Instantiate dataloaders.
					UpperCAmelCase_    :      Tuple            =						DataLoader(
					    tokenized_datasets['train']     ,       shuffle=lowerCamelCase_     ,       collate_fn=lowerCamelCase_     ,       batch_size=lowerCamelCase_  )
					UpperCAmelCase_    :      str            =						DataLoader(
					    tokenized_datasets['validation']     ,       shuffle=lowerCamelCase_     ,       collate_fn=lowerCamelCase_     ,       batch_size=lowerCamelCase_  )

					return train_dataloader, eval_dataloader




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							Dict  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						Accelerator()

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

					set_seed(lowerCamelCase_  )
					UpperCAmelCase_    :      str            =						get_dataloaders(lowerCamelCase_     ,       lowerCamelCase_     ,       lowerCamelCase_  )

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

					# Instantiate optimizer
					UpperCAmelCase_    :      Union[str, Any]            =						(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					UpperCAmelCase_    :      int            =						optimizer_cls(params=model.parameters()     ,       lr=lowerCamelCase_  )

					if accelerator.state.deepspeed_plugin is not None:
										UpperCAmelCase_    :      Any            =						accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										UpperCAmelCase_    :      Tuple            =						1
					UpperCAmelCase_    :      Union[str, Any]            =						(len(lowerCamelCase_  ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										UpperCAmelCase_    :      List[str]            =						get_linear_schedule_with_warmup(
										    optimizer=lowerCamelCase_     ,       num_warmup_steps=0     ,       num_training_steps=lowerCamelCase_     ,       )
					else:
										UpperCAmelCase_    :      Dict            =						DummyScheduler(lowerCamelCase_     ,       total_num_steps=lowerCamelCase_     ,       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.
					UpperCAmelCase_    :      Union[str, Any]            =						accelerator.prepare(
					    lowerCamelCase_     ,       lowerCamelCase_     ,       lowerCamelCase_     ,       lowerCamelCase_     ,       lowerCamelCase_  )

					# We need to keep track of how many total steps we have iterated over
					UpperCAmelCase_    :      Any            =						0
					# We also need to keep track of the stating epoch so files are named properly
					UpperCAmelCase_    :      Union[str, Any]            =						0

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

															overall_step += 1

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

										UpperCAmelCase_    :      Optional[Any]            =						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:"     ,       lowerCamelCase_  )
										UpperCAmelCase_    :      List[str]            =						eval_metric['accuracy']

										if best_performance < eval_metric["accuracy"]:
															UpperCAmelCase_    :      Union[str, Any]            =						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(lowerCamelCase_     ,       lowerCamelCase_  )




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[Any]            =						argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
					parser.add_argument(
					    '--model_name_or_path'     ,       type=lowerCamelCase_     ,       default='bert-base-cased'     ,       help='Path to pretrained model or model identifier from huggingface.co/models.'     ,       required=lowerCamelCase_     ,       )
					parser.add_argument(
					    '--output_dir'     ,       type=lowerCamelCase_     ,       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=lowerCamelCase_     ,       default=lowerCamelCase_     ,       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=lowerCamelCase_     ,       default=3     ,       help='Number of train epochs.'     ,       )
					UpperCAmelCase_    :      List[str]            =						parser.parse_args()
					UpperCAmelCase_    :      List[str]            =						{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
					training_function(lowerCamelCase_     ,       lowerCamelCase_  )


if __name__ == "__main__":
		main()










import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class _lowerCamelCase(						unittest.TestCase   ):
       lowercase_ :	Dict     =			JukeboxTokenizer
       lowercase_ :	Dict     =			{
           """artist""": """Zac Brown Band""",
           """genres""": """Country""",
           """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
       }
       @require_torch
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           import torch

           _lowercase       :							str      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics')
           _lowercase       :							Optional[Any]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
                   76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                   44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                   47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                   76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                   30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                   27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                   45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                   41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                   76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                   76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                   64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                   30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                   27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                   34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                   27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                   41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                   44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                   76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                   32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                   40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                   45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                   31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                   45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                   34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                   31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                   40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                   38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                   76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                   41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                   27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                   46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                   41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                   46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                   41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                   40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                   27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                   76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                   41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                   76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                   27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                   34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                   44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                   40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                   46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                   38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                   40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                   27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                   76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                   76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                   76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                   76, 76]]),
               torch.tensor([[0, 0, 0, 10_69, 11]]),
               torch.tensor([[0, 0, 0, 10_69, 11]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]))




       @require_torch
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           import torch

           _lowercase       :							List[str]      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics')
           _lowercase       :							List[str]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
                   31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                   31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                   40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                   79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                   77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                   27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                   37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                   32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                   77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                   77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                   46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                   77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                   77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                   77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                   77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                   64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                   40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                   40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                   38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                   31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                   41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                   46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                   41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                   31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                   31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                   44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                   31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                   38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                   40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                   27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                   31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                   34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                   31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                   31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                   45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                   31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                   15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                   11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                   45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                   41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                   44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                   46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                   27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                   35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                   77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                   31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                   41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                   77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                   40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                   77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                   27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                   77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                   77, 77, 77, 77, 77, 77]]),
               torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
               torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]))


