



import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

_snake_case		=  logging.getLogger(__name__)



@dataclass
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		field(
							    default=128   ,					metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Overwrite the cached preprocessed datasets or not.'})
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'Whether to pad all samples to `max_seq_length`. '
							            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
							            'value if set.'
							        )
							    }   ,					)



@dataclass
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'})
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Train language if it is different from the evaluation language.'})
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Pretrained config name or path if not the same as model_name'})
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}   ,					)
							lowerCamelCase__   			=		field(
							    default='main'   ,					metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}   ,					)






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
							_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry("run_xnli"				, _lowerCamelCase  )

							# Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				, datefmt="%m/%d/%Y %H:%M:%S"				, handlers=[logging.StreamHandler(sys.stdout  )]				, )

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							_lowerCAmelCase     :							str    =    training_args.get_process_log_level()
							logger.setLevel(_lowerCamelCase  )
							datasets.utils.logging.set_verbosity(_lowerCamelCase  )
							transformers.utils.logging.set_verbosity(_lowerCamelCase  )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
							    + F"distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"  )
							logger.info(F"Training/evaluation parameters {training_args}"  )

							# Detecting last checkpoint.
							_lowerCAmelCase     :							Optional[Any]    =    None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														_lowerCAmelCase     :							Tuple    =    get_last_checkpoint(training_args.output_dir  )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
																					raise ValueError(
																					    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
																					    "Use --overwrite_output_dir to overcome."  )
														elif last_checkpoint is not None:
																					logger.info(
																					    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
																					    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."  )

    # Set seed before initializing model.
							set_seed(training_args.seed  )

							# In distributed training, the load_dataset function guarantees that only one local process can concurrently
							# download the dataset.
							# Downloading and loading xnli dataset from the hub.
							if training_args.do_train:
														if model_args.train_language is None:
																					_lowerCAmelCase     :							List[Any]    =    load_dataset(
																					    "xnli"				, model_args.language				, split="train"				, cache_dir=model_args.cache_dir				, use_auth_token=True if model_args.use_auth_token else None				, )
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    load_dataset(
																					    "xnli"				, model_args.train_language				, split="train"				, cache_dir=model_args.cache_dir				, use_auth_token=True if model_args.use_auth_token else None				, )
														_lowerCAmelCase     :							Optional[Any]    =    train_dataset.features["label"].names

							if training_args.do_eval:
														_lowerCAmelCase     :							int    =    load_dataset(
														    "xnli"				, model_args.language				, split="validation"				, cache_dir=model_args.cache_dir				, use_auth_token=True if model_args.use_auth_token else None				, )
														_lowerCAmelCase     :							Dict    =    eval_dataset.features["label"].names

							if training_args.do_predict:
														_lowerCAmelCase     :							Optional[Any]    =    load_dataset(
														    "xnli"				, model_args.language				, split="test"				, cache_dir=model_args.cache_dir				, use_auth_token=True if model_args.use_auth_token else None				, )
														_lowerCAmelCase     :							List[str]    =    predict_dataset.features["label"].names

							# Labels
							_lowerCAmelCase     :							List[Any]    =    len(_lowerCamelCase  )

							# Load pretrained model and tokenizer
							# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							_lowerCAmelCase     :							str    =    AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path				, num_labels=_lowerCamelCase				, idalabel={str(_lowerCamelCase  ): label for i, label in enumerate(_lowerCamelCase  )}				, labelaid={label: i for i, label in enumerate(_lowerCamelCase  )}				, finetuning_task="xnli"				, cache_dir=model_args.cache_dir				, revision=model_args.model_revision				, use_auth_token=True if model_args.use_auth_token else None				, )
							_lowerCAmelCase     :							Optional[int]    =    AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				, do_lower_case=model_args.do_lower_case				, cache_dir=model_args.cache_dir				, use_fast=model_args.use_fast_tokenizer				, revision=model_args.model_revision				, use_auth_token=True if model_args.use_auth_token else None				, )
							_lowerCAmelCase     :							Any    =    AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path				, from_tf=bool(".ckpt" in model_args.model_name_or_path  )				, config=_lowerCamelCase				, cache_dir=model_args.cache_dir				, revision=model_args.model_revision				, use_auth_token=True if model_args.use_auth_token else None				, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes				, )

							# Preprocessing the datasets
							# Padding strategy
							if data_args.pad_to_max_length:
														_lowerCAmelCase     :							List[Any]    =    "max_length"
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														_lowerCAmelCase     :							Optional[Any]    =    False

							def preprocess_function(_lowerCamelCase  ):
														# Tokenize the texts
														return tokenizer(
														    examples["premise"]				, examples["hypothesis"]				, padding=_lowerCamelCase				, max_length=data_args.max_seq_length				, truncation=_lowerCamelCase				, )

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					_lowerCAmelCase     :							Any    =    min(len(_lowerCamelCase  )				, data_args.max_train_samples  )
																					_lowerCAmelCase     :							Optional[Any]    =    train_dataset.select(range(_lowerCamelCase  )  )
														with training_args.main_process_first(desc="train dataset map pre-processing"  ):
																					_lowerCAmelCase     :							Dict    =    train_dataset.map(
																					    _lowerCamelCase				, batched=_lowerCamelCase				, load_from_cache_file=not data_args.overwrite_cache				, desc="Running tokenizer on train dataset"				, )
														# Log a few random samples from the training set:
														for index in random.sample(range(len(_lowerCamelCase  )  )				, 3  ):
																					logger.info(F"Sample {index} of the training set: {train_dataset[index]}."  )

							if training_args.do_eval:
														if data_args.max_eval_samples is not None:
																					_lowerCAmelCase     :							Union[str, Any]    =    min(len(_lowerCamelCase  )				, data_args.max_eval_samples  )
																					_lowerCAmelCase     :							Union[str, Any]    =    eval_dataset.select(range(_lowerCamelCase  )  )
														with training_args.main_process_first(desc="validation dataset map pre-processing"  ):
																					_lowerCAmelCase     :							Tuple    =    eval_dataset.map(
																					    _lowerCamelCase				, batched=_lowerCamelCase				, load_from_cache_file=not data_args.overwrite_cache				, desc="Running tokenizer on validation dataset"				, )

							if training_args.do_predict:
														if data_args.max_predict_samples is not None:
																					_lowerCAmelCase     :							List[Any]    =    min(len(_lowerCamelCase  )				, data_args.max_predict_samples  )
																					_lowerCAmelCase     :							List[str]    =    predict_dataset.select(range(_lowerCamelCase  )  )
														with training_args.main_process_first(desc="prediction dataset map pre-processing"  ):
																					_lowerCAmelCase     :							Optional[int]    =    predict_dataset.map(
																					    _lowerCamelCase				, batched=_lowerCamelCase				, load_from_cache_file=not data_args.overwrite_cache				, desc="Running tokenizer on prediction dataset"				, )

    # Get the metric function
							_lowerCAmelCase     :							List[str]    =    evaluate.load("xnli"  )

							# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(_lowerCamelCase  ):
														_lowerCAmelCase     :							Optional[Any]    =    p.predictions[0] if isinstance(p.predictions				, _lowerCamelCase  ) else p.predictions
														_lowerCAmelCase     :							Dict    =    np.argmax(_lowerCamelCase				, axis=1  )
														return metric.compute(predictions=_lowerCamelCase				, references=p.label_ids  )

							# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
							if data_args.pad_to_max_length:
														_lowerCAmelCase     :							int    =    default_data_collator
							elif training_args.fpaa:
														_lowerCAmelCase     :							List[str]    =    DataCollatorWithPadding(_lowerCamelCase				, pad_to_multiple_of=8  )
							else:
														_lowerCAmelCase     :							Union[str, Any]    =    None

							# Initialize our Trainer
							_lowerCAmelCase     :							Dict    =    Trainer(
							    model=_lowerCamelCase				, args=_lowerCamelCase				, train_dataset=train_dataset if training_args.do_train else None				, eval_dataset=eval_dataset if training_args.do_eval else None				, compute_metrics=_lowerCamelCase				, tokenizer=_lowerCamelCase				, data_collator=_lowerCamelCase				, )

							# Training
							if training_args.do_train:
														_lowerCAmelCase     :							Union[str, Any]    =    None
														if training_args.resume_from_checkpoint is not None:
																					_lowerCAmelCase     :							List[Any]    =    training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					_lowerCAmelCase     :							int    =    last_checkpoint
														_lowerCAmelCase     :							Any    =    trainer.train(resume_from_checkpoint=_lowerCamelCase  )
														_lowerCAmelCase     :							List[str]    =    train_result.metrics
														_lowerCAmelCase     :							Dict    =    (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowerCamelCase  )
														)
														_lowerCAmelCase     :							Tuple    =    min(_lowerCamelCase				, len(_lowerCamelCase  )  )

														trainer.save_model()  # Saves the tokenizer too for easy upload

														trainer.log_metrics("train"				, _lowerCamelCase  )
														trainer.save_metrics("train"				, _lowerCamelCase  )
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														logger.info("*** Evaluate ***"  )
														_lowerCAmelCase     :							Optional[int]    =    trainer.evaluate(eval_dataset=_lowerCamelCase  )

														_lowerCAmelCase     :							Dict    =    data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowerCamelCase  )
														_lowerCAmelCase     :							Union[str, Any]    =    min(_lowerCamelCase				, len(_lowerCamelCase  )  )

														trainer.log_metrics("eval"				, _lowerCamelCase  )
														trainer.save_metrics("eval"				, _lowerCamelCase  )

							# Prediction
							if training_args.do_predict:
														logger.info("*** Predict ***"  )
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    trainer.predict(_lowerCamelCase				, metric_key_prefix="predict"  )

														_lowerCAmelCase     :							str    =    (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_lowerCamelCase  )
														)
														_lowerCAmelCase     :							int    =    min(_lowerCamelCase				, len(_lowerCamelCase  )  )

														trainer.log_metrics("predict"				, _lowerCamelCase  )
														trainer.save_metrics("predict"				, _lowerCamelCase  )

														_lowerCAmelCase     :							str    =    np.argmax(_lowerCamelCase				, axis=1  )
														_lowerCAmelCase     :							Any    =    os.path.join(training_args.output_dir				, "predictions.txt"  )
														if trainer.is_world_process_zero():
																					with open(_lowerCamelCase				, "w"  ) as writer:
																												writer.write("index\tprediction\n"  )
																												for index, item in enumerate(_lowerCamelCase  ):
																																			_lowerCAmelCase     :							str    =    label_list[item]
																																			writer.write(F"{index}\t{item}\n"  )


if __name__ == "__main__":
						main()






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 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					int = 16  ,			A__       :					str = "bert-base-cased"	):





       '''simple docstring'''



       __lowerCamelCase   =						AutoTokenizer.from_pretrained(A__	)
       __lowerCamelCase   =						load_dataset("""glue"""  ,			"""mrpc"""	)

       def tokenize_function(A__       :					int	):
              # max_length=None => use the model max length (it's actually the default)
              __lowerCamelCase   =						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
       __lowerCamelCase   =						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
       __lowerCamelCase   =						tokenized_datasets.rename_column("""label"""  ,			"""labels"""	)

       def collate_fn(A__       :					Optional[int]	):
              # 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=128  ,			return_tensors="""pt"""	)
              return tokenizer.pad(A__  ,			padding="""longest"""  ,			return_tensors="""pt"""	)

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

       return train_dataloader, eval_dataloader




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					Union[str, Any]  ,			A__       :					Tuple  ,			A__       :					Optional[Any]	):





       '''simple docstring'''



       model.eval()
       __lowerCamelCase   =						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():
                     __lowerCamelCase   =						model(**A__	)
              __lowerCamelCase   =						outputs.logits.argmax(dim=-1	)
              # It is slightly faster to call this once, than multiple times
              __lowerCamelCase,   __lowerCamelCase   =						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:
                            __lowerCamelCase   =						predictions[: len(eval_dataloader.dataset	) - samples_seen]
                            __lowerCamelCase   =						references[: len(eval_dataloader.dataset	) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=A__  ,			references=A__  ,			)

       __lowerCamelCase   =						metric.compute()
       return eval_metric["accuracy"]




def 					lowerCamelCase__       (						A__       :					Optional[Any]  ,			A__       :					Optional[int]	):





       '''simple docstring'''



       __lowerCamelCase   =						Accelerator()

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

       set_seed(A__	)
       __lowerCamelCase,   __lowerCamelCase   =						get_dataloaders(A__  ,			A__  ,			A__	)

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

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

       if accelerator.state.deepspeed_plugin is not None:
              __lowerCamelCase   =						accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              __lowerCamelCase   =						1
       __lowerCamelCase   =						(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
       ):
              __lowerCamelCase   =						get_linear_schedule_with_warmup(
                  optimizer=A__  ,			num_warmup_steps=0  ,			num_training_steps=A__  ,			)
       else:
              __lowerCamelCase   =						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.
       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						accelerator.prepare(
           A__  ,			A__  ,			A__  ,			A__  ,			A__	)

       # We need to keep track of how many total steps we have iterated over
       __lowerCamelCase   =						0
       # We also need to keep track of the stating epoch so files are named properly
       __lowerCamelCase   =						0
       __lowerCamelCase   =						evaluate.load("""glue"""  ,			"""mrpc"""	)
       __lowerCamelCase   =						num_epochs

       if args.partial_train_epoch is not None:
              __lowerCamelCase   =						args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint	)
              __lowerCamelCase   =						args.resume_from_checkpoint.split("""epoch_"""	)[1]
              __lowerCamelCase   =						""""""
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              __lowerCamelCase   =						int(A__	) + 1
              __lowerCamelCase   =						evaluation_loop(A__  ,			A__  ,			A__  ,			A__	)
              accelerator.print("""resumed checkpoint performance:"""  ,			A__	)
              accelerator.print("""resumed checkpoint's scheduler's lr:"""  ,			lr_scheduler.get_lr()[0]	)
              accelerator.print("""resumed optimizers's lr:"""  ,			optimizer.param_groups[0]["""lr"""]	)
              with open(os.path.join(args.output_dir  ,			f'state_{starting_epoch-1}.json'	)  ,			"""r"""	) as f:
                     __lowerCamelCase   =						json.load(A__	)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       __lowerCamelCase   =						{}
       for epoch in range(A__  ,			A__	):
              model.train()
              for step, batch in enumerate(A__	):
                     __lowerCamelCase   =						model(**A__	)
                     __lowerCamelCase   =						outputs.loss
                     __lowerCamelCase   =						loss / gradient_accumulation_steps
                     accelerator.backward(A__	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              __lowerCamelCase   =						f'epoch_{epoch}'
              __lowerCamelCase   =						os.path.join(args.output_dir  ,			A__	)
              accelerator.save_state(A__	)
              __lowerCamelCase   =						evaluation_loop(A__  ,			A__  ,			A__  ,			A__	)
              __lowerCamelCase   =						accuracy
              __lowerCamelCase   =						lr_scheduler.get_lr()[0]
              __lowerCamelCase   =						optimizer.param_groups[0]["""lr"""]
              __lowerCamelCase   =						epoch
              __lowerCamelCase   =						overall_step
              accelerator.print(f'epoch {epoch}:'  ,			A__	)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir  ,			f'state_{epoch}.json'	)  ,			"""w"""	) as f:
                            json.dump(A__  ,			A__	)




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						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(
           """--resume_from_checkpoint"""  ,			type=A__  ,			default=A__  ,			help="""If the training should continue from a checkpoint folder."""  ,			)
       parser.add_argument(
           """--partial_train_epoch"""  ,			type=A__  ,			default=A__  ,			help="""If passed, the training will stop after this number of epochs."""  ,			)
       parser.add_argument(
           """--num_epochs"""  ,			type=A__  ,			default=2  ,			help="""Number of train epochs."""  ,			)
       __lowerCamelCase   =						parser.parse_args()
       __lowerCamelCase   =						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

       training_function(A__  ,			A__	)


if __name__ == "__main__":
       main()

