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

			def tokenize_function(a_				: Optional[Any]):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase						:Optional[Any]						  =     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						:List[str]						  =     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						:str						  =     tokenized_datasets.rename_column('''label'''						,			'''labels''')

			def collate_fn(a_				: str):
						# 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.
			lowerCamelCase						:Tuple						  =     DataLoader(
			    tokenized_datasets['''train''']						,			shuffle=a_						,			collate_fn=a_						,			batch_size=a_)
			lowerCamelCase						:Any						  =     DataLoader(
			    tokenized_datasets['''validation''']						,			shuffle=a_						,			collate_fn=a_						,			batch_size=a_)

			return train_dataloader, eval_dataloader





def  _lowerCamelCase					(   a_				: Optional[int]						,			a_				: Optional[int]						,			a_				: Union[str, Any]						,			a_				: Dict):
			model.eval()
			lowerCamelCase						:Any						  =     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						:List[str]						  =     model(**a_)
						lowerCamelCase						:Optional[int]						  =     outputs.logits.argmax(dim=-1)
						# It is slightly faster to call this once, than multiple times
						lowerCamelCase  ,    lowerCamelCase						: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:
												lowerCamelCase						:List[str]						  =     predictions[: len(eval_dataloader.dataset) - samples_seen]
												lowerCamelCase						:str						  =     references[: len(eval_dataloader.dataset) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=a_						,			references=a_						,			)

			lowerCamelCase						:Union[str, Any]						  =     metric.compute()
			return eval_metric["accuracy"]





def  _lowerCamelCase					(   a_				: Tuple						,			a_				: int):
			# Initialize accelerator
			lowerCamelCase						:Union[str, Any]						  =     Accelerator()

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

			set_seed(a_)
			lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     get_dataloaders(a_						,			a_						,			a_)

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

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

			if accelerator.state.deepspeed_plugin is not None:
						lowerCamelCase						:str						  =     accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						lowerCamelCase						:Tuple						  =     1
			lowerCamelCase						: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
			):
						lowerCamelCase						:List[str]						  =     get_linear_schedule_with_warmup(
						    optimizer=a_						,			num_warmup_steps=0						,			num_training_steps=a_						,			)
			else:
						lowerCamelCase						:List[str]						  =     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						:str						  =     accelerator.prepare(
			    a_						,			a_						,			a_						,			a_						,			a_)

			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase						:List[Any]						  =     0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase						:int						  =     0
			lowerCamelCase						:List[Any]						  =     evaluate.load('''glue'''						,			'''mrpc''')
			lowerCamelCase						:Any						  =     num_epochs

			if args.partial_train_epoch is not None:
						lowerCamelCase						:int						  =     args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint)
						lowerCamelCase						:Tuple						  =     args.resume_from_checkpoint.split('''epoch_''')[1]
						lowerCamelCase						:str						  =     ''''''
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						lowerCamelCase						:Optional[Any]						  =     int(a_) + 1
						lowerCamelCase						:Optional[int]						  =     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						:Any						  =     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						:Optional[int]						  =     {}
			for epoch in range(a_						,			a_):
						model.train()
						for step, batch in enumerate(a_):
									lowerCamelCase						:List[str]						  =     model(**a_)
									lowerCamelCase						:int						  =     outputs.loss
									lowerCamelCase						:int						  =     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						:List[Any]						  =     F"epoch_{epoch}"
						lowerCamelCase						:Tuple						  =     os.path.join(args.output_dir						,			a_)
						accelerator.save_state(a_)
						lowerCamelCase						:Tuple						  =     evaluation_loop(a_						,			a_						,			a_						,			a_)
						lowerCamelCase						:int						  =     accuracy
						lowerCamelCase						:Union[str, Any]						  =     lr_scheduler.get_lr()[0]
						lowerCamelCase						:Any						  =     optimizer.param_groups[0]['''lr''']
						lowerCamelCase						:List[Any]						  =     epoch
						lowerCamelCase						:Optional[Any]						  =     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					(   ):
			lowerCamelCase						:List[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(
			    '''--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						:str						  =     parser.parse_args()
			lowerCamelCase						:Tuple						  =     {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

			training_function(a_						,			a_)


if __name__ == "__main__":
			main()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__           =							logging.get_logger(__name__)

A__           =							{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'roberta-prelayernorm'







							def __init__(				self     :    str	,					__snake_case     :    List[str]=50265	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    int=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Optional[int]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=512	,					__snake_case     :    Dict=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    Any=1e-1_2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Dict=0	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any="absolute"	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

										lowerCamelCase						:Optional[int]						  =     vocab_size
										lowerCamelCase						:Dict						  =     hidden_size
										lowerCamelCase						:Tuple						  =     num_hidden_layers
										lowerCamelCase						:Optional[int]						  =     num_attention_heads
										lowerCamelCase						:Any						  =     hidden_act
										lowerCamelCase						:List[Any]						  =     intermediate_size
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:str						  =     attention_probs_dropout_prob
										lowerCamelCase						:Tuple						  =     max_position_embeddings
										lowerCamelCase						:int						  =     type_vocab_size
										lowerCamelCase						:Optional[Any]						  =     initializer_range
										lowerCamelCase						:Union[str, Any]						  =     layer_norm_eps
										lowerCamelCase						:Dict						  =     position_embedding_type
										lowerCamelCase						:List[Any]						  =     use_cache
										lowerCamelCase						:Optional[int]						  =     classifier_dropout


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Any		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Optional[Any]						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:List[str]						  =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]		)



