



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  UpperCAmelCase__    (    lowercase__						,       lowercase__ = 16						,       lowercase__ = "bert-base-cased"   ) ->      Optional[int]:
						__lowercase					=				AutoTokenizer.from_pretrained(lowercase__   )
						__lowercase					=				load_dataset("""glue"""						,       """mrpc"""   )

						def tokenize_function(lowercase__   ):
												# max_length=None => use the model max length (it's actually the default)
												__lowercase					=				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
						__lowercase					=				datasets.map(
						    lowercase__						,       batched=lowercase__						,       remove_columns=["""idx""", """sentence1""", """sentence2"""]						,       load_from_cache_file=lowercase__   )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__lowercase					=				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.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(lowercase__						,       padding="""max_length"""						,       max_length=128						,       return_tensors="""pt"""   )
												return tokenizer.pad(lowercase__						,       padding="""longest"""						,       return_tensors="""pt"""   )

						# Instantiate dataloaders.
						__lowercase					=				DataLoader(
						    tokenized_datasets["""train"""]						,       shuffle=lowercase__						,       collate_fn=lowercase__						,       batch_size=lowercase__   )
						__lowercase					=				DataLoader(
						    tokenized_datasets["""validation"""]						,       shuffle=lowercase__						,       collate_fn=lowercase__						,       batch_size=lowercase__   )

						return train_dataloader, eval_dataloader






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Optional[int]:
						# Initialize accelerator
						__lowercase					=				Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__lowercase					=				config["""lr"""]
						__lowercase					=				int(config["""num_epochs"""]   )
						__lowercase					=				int(config["""seed"""]   )
						__lowercase					=				int(config["""batch_size"""]   )
						__lowercase					=				args.model_name_or_path

						set_seed(lowercase__   )
						__lowercase	,			__lowercase					=				get_dataloaders(lowercase__						,       lowercase__						,       lowercase__   )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__lowercase					=				AutoModelForSequenceClassification.from_pretrained(lowercase__						,       return_dict=lowercase__   )

						# Instantiate optimizer
						__lowercase					=				(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__lowercase					=				optimizer_cls(params=model.parameters()						,       lr=lowercase__   )

						if accelerator.state.deepspeed_plugin is not None:
												__lowercase					=				accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												__lowercase					=				1
						__lowercase					=				(len(lowercase__   ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												__lowercase					=				get_linear_schedule_with_warmup(
												    optimizer=lowercase__						,       num_warmup_steps=0						,       num_training_steps=lowercase__						,       )
						else:
												__lowercase					=				DummyScheduler(lowercase__						,       total_num_steps=lowercase__						,       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.
						__lowercase	,			__lowercase	,			__lowercase	,			__lowercase	,			__lowercase					=				accelerator.prepare(
						    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__   )

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

						# Now we train the model
						__lowercase					=				evaluate.load("""glue"""						,       """mrpc"""   )
						__lowercase					=				0
						__lowercase					=				{}
						for epoch in range(lowercase__						,       lowercase__   ):
												model.train()
												for step, batch in enumerate(lowercase__   ):
																		__lowercase					=				model(**lowercase__   )
																		__lowercase					=				outputs.loss
																		__lowercase					=				loss / gradient_accumulation_steps
																		accelerator.backward(lowercase__   )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												__lowercase					=				0
												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():
																								__lowercase					=				model(**lowercase__   )
																		__lowercase					=				outputs.logits.argmax(dim=-1   )
																		# It is slightly faster to call this once, than multiple times
																		__lowercase	,			__lowercase					=				accelerator.gather(
																		    (predictions, batch["""labels"""])   )  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(lowercase__   ) - 1:
																														__lowercase					=				predictions[: len(eval_dataloader.dataset   ) - samples_seen]
																														__lowercase					=				references[: len(eval_dataloader.dataset   ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=lowercase__						,       references=lowercase__						,       )

												__lowercase					=				metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"						,       lowercase__   )
												__lowercase					=				eval_metric["""accuracy"""]

												if best_performance < eval_metric["accuracy"]:
																		__lowercase					=				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(lowercase__						,       lowercase__   )






def  UpperCAmelCase__    (    ) ->      Dict:
						__lowercase					=				argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""   )
						parser.add_argument(
						    """--model_name_or_path"""						,       type=lowercase__						,       default="""bert-base-cased"""						,       help="""Path to pretrained model or model identifier from huggingface.co/models."""						,       required=lowercase__						,       )
						parser.add_argument(
						    """--output_dir"""						,       type=lowercase__						,       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=lowercase__						,       default=lowercase__						,       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=lowercase__						,       default=3						,       help="""Number of train epochs."""						,       )
						__lowercase					=				parser.parse_args()
						__lowercase					=				{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
						training_function(lowercase__						,       lowercase__   )


if __name__ == "__main__":
			main()










import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Tuple      =			LxmertTokenizer
			lowercase__      :  List[str]      =			LxmertTokenizerFast
			lowercase__      :  Optional[Any]      =			True
			lowercase__      :  List[Any]      =			True





			def 		snake_case__			(				self	:       Tuple   )						->					Tuple:
									"""simple docstring"""







									super().setUp()

									__lowercase					=				[
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									__lowercase					=				os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["""vocab_file"""]   )
									with open(self.vocab_file			,     """w"""			,     encoding="""utf-8"""   ) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]   )   )





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       int   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				"""UNwant\u00E9d,running"""
									__lowercase					=				"""unwanted, running"""
									return input_text, output_text





			def 		snake_case__			(				self	:       str   )						->					Any:
									"""simple docstring"""







									__lowercase					=				self.tokenizer_class(self.vocab_file   )

									__lowercase					=				tokenizer.tokenize("""UNwant\u00E9d,running"""   )
									self.assertListEqual(lowercase			,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]   )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase   )			,     [7, 4, 5, 10, 8, 9]   )







			def 		snake_case__			(				self	:       Union[str, Any]   )						->					int:
									"""simple docstring"""







									if not self.test_rust_tokenizer:
															return

									__lowercase					=				self.get_tokenizer()
									__lowercase					=				self.get_rust_tokenizer()

									__lowercase					=				"""I was born in 92000, and this is falsé."""

									__lowercase					=				tokenizer.tokenize(lowercase   )
									__lowercase					=				rust_tokenizer.tokenize(lowercase   )
									self.assertListEqual(lowercase			,     lowercase   )

									__lowercase					=				tokenizer.encode(lowercase			,     add_special_tokens=lowercase   )
									__lowercase					=				rust_tokenizer.encode(lowercase			,     add_special_tokens=lowercase   )
									self.assertListEqual(lowercase			,     lowercase   )

									__lowercase					=				self.get_rust_tokenizer()
									__lowercase					=				tokenizer.encode(lowercase   )
									__lowercase					=				rust_tokenizer.encode(lowercase   )
									self.assertListEqual(lowercase			,     lowercase   )





