



'''simple docstring'''

import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a							   =				16
a							   =				32







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase = 16					)				->					List[Any]:





			"""simple docstring"""







			snake_case:    List[str]      							=AutoTokenizer.from_pretrained('bert-base-cased'					)
			snake_case:    List[Any]      							=load_dataset('glue'				,							'mrpc'					)

			def tokenize_function(__UpperCAmelCase					):
						# max_length=None => use the model max length (it's actually the default)
						snake_case:    str      							=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
			# starting with the main process first:
			with accelerator.main_process_first():
						snake_case:    Dict      							=datasets.map(
						    __lowerCamelCase				,							batched=__lowerCamelCase				,							remove_columns=['idx', 'sentence1', 'sentence2']				,							)

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

			def collate_fn(__UpperCAmelCase					):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						snake_case:    Optional[Any]      							=1_28 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									snake_case:    Optional[Any]      							=16
						elif accelerator.mixed_precision != "no":
									snake_case:    Optional[Any]      							=8
						else:
									snake_case:    List[Any]      							=None

						return tokenizer.pad(
						    __lowerCamelCase				,							padding='longest'				,							max_length=__lowerCamelCase				,							pad_to_multiple_of=__lowerCamelCase				,							return_tensors='pt'				,							)

			# Instantiate dataloaders.
			snake_case:    int      							=DataLoader(
			    tokenized_datasets['train']				,							shuffle=__lowerCamelCase				,							collate_fn=__lowerCamelCase				,							batch_size=__lowerCamelCase					)
			snake_case:    List[str]      							=DataLoader(
			    tokenized_datasets['validation']				,							shuffle=__lowerCamelCase				,							collate_fn=__lowerCamelCase				,							batch_size=__lowerCamelCase					)

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	a							   =				mocked_dataloaders  # noqa: F811







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			if os.environ.get('TESTING_MOCKED_DATALOADERS'				,							__lowerCamelCase					) == "1":
						snake_case:    List[str]      							=2
			# New Code #
			snake_case:    Dict      							=int(args.gradient_accumulation_steps					)
			# Initialize accelerator
			snake_case:    int      							=Accelerator(
			    cpu=args.cpu				,							mixed_precision=args.mixed_precision				,							gradient_accumulation_steps=__lowerCamelCase					)
			if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
						raise NotImplementedError(
						    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'					)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			snake_case:    Optional[Any]      							=config['lr']
			snake_case:    List[Any]      							=int(config['num_epochs']					)
			snake_case:    List[str]      							=int(config['seed']					)
			snake_case:    str      							=int(config['batch_size']					)

			snake_case:    Tuple      							=evaluate.load('glue'				,							'mrpc'					)

			set_seed(__lowerCamelCase					)
			snake_case							,  snake_case:    Union[str, Any]      							=get_dataloaders(__lowerCamelCase				,							__lowerCamelCase					)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			snake_case:    int      							=AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,							return_dict=__lowerCamelCase					)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			snake_case:    Union[str, Any]      							=model.to(accelerator.device					)

			# Instantiate optimizer
			snake_case:    int      							=AdamW(params=model.parameters()				,							lr=__lowerCamelCase					)

			# Instantiate scheduler
			snake_case:    List[str]      							=get_linear_schedule_with_warmup(
			    optimizer=__lowerCamelCase				,							num_warmup_steps=1_00				,							num_training_steps=(len(__lowerCamelCase					) * num_epochs)				,							)

			# 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.
			snake_case							,  snake_case							,  snake_case							,  snake_case							,  snake_case:    Dict      							=accelerator.prepare(
			    __lowerCamelCase				,							__lowerCamelCase				,							__lowerCamelCase				,							__lowerCamelCase				,							__lowerCamelCase					)

			# Now we train the model
			for epoch in range(__lowerCamelCase					):
						model.train()
						for step, batch in enumerate(__lowerCamelCase					):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device					)
									# New code #
									# We use the new `accumulate` context manager to perform gradient accumulation
									# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
									with accelerator.accumulate(__lowerCamelCase					):
												snake_case:    str      							=model(**__lowerCamelCase					)
												snake_case:    List[str]      							=output.loss
												accelerator.backward(__lowerCamelCase					)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						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():
												snake_case:    Union[str, Any]      							=model(**__lowerCamelCase					)
									snake_case:    Optional[int]      							=outputs.logits.argmax(dim=-1					)
									snake_case							,  snake_case:    Optional[int]      							=accelerator.gather_for_metrics((predictions, batch['labels'])					)
									metric.add_batch(
									    predictions=__lowerCamelCase				,							references=__lowerCamelCase				,							)

						snake_case:    int      							=metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''				,							__lowerCamelCase					)







def a_     (    )				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=argparse.ArgumentParser(description='Simple example of training script.'					)
			parser.add_argument(
			    '--mixed_precision'				,							type=__lowerCamelCase				,							default=__lowerCamelCase				,							choices=['no', 'fp16', 'bf16', 'fp8']				,							help='Whether to use mixed precision. Choose'
			    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
			    'and an Nvidia Ampere GPU.'				,							)
			# New Code #
			parser.add_argument(
			    '--gradient_accumulation_steps'				,							type=__lowerCamelCase				,							default=1				,							help='The number of minibatches to be ran before gradients are accumulated.'				,							)
			parser.add_argument('--cpu'				,							action='store_true'				,							help='If passed, will train on the CPU.'					)
			snake_case:    int      							=parser.parse_args()
			snake_case:    str      							={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(__lowerCamelCase				,							__lowerCamelCase					)


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


lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"distilbert"
	SCREAMING_SNAKE_CASE  	=							{
	    "hidden_size": "dim",
	    "num_attention_heads": "n_heads",
	    "num_hidden_layers": "n_layers",
	}



	def __init__(    self				:     Optional[Any]					,						snake_case_				:     Tuple=3_05_22					,						snake_case_				:     Any=5_12					,						snake_case_				:     Dict=False					,						snake_case_				:     Tuple=6					,						snake_case_				:     Union[str, Any]=12					,						snake_case_				:     List[str]=7_68					,						snake_case_				:     Optional[Any]=4 * 7_68					,						snake_case_				:     Optional[Any]=0.1					,						snake_case_				:     Dict=0.1					,						snake_case_				:     Union[str, Any]="gelu"					,						snake_case_				:     Any=0.0_2					,						snake_case_				:     Optional[Any]=0.1					,						snake_case_				:     List[Any]=0.2					,						snake_case_				:     Optional[Any]=0					,						**snake_case_				:     List[Any]					,						)->				Tuple:
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =sinusoidal_pos_embds
						__lowerCAmelCase						 =n_layers
						__lowerCAmelCase						 =n_heads
						__lowerCAmelCase						 =dim
						__lowerCAmelCase						 =hidden_dim
						__lowerCAmelCase						 =dropout
						__lowerCAmelCase						 =attention_dropout
						__lowerCAmelCase						 =activation
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =qa_dropout
						__lowerCAmelCase						 =seq_classif_dropout
						super().__init__(**snake_case_					,						pad_token_id=snake_case_)






class     __a			(  SCREAMING_SNAKE_CASE					):



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__lowerCAmelCase						 ={0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
											__lowerCAmelCase						 ={0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						    ])




