






"""simple docstring"""
from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					str 			=					"""efficientformer"""





	def __init__(self  ,  lowerCAmelCase_ = [3, 2, 6, 4]  ,  lowerCAmelCase_ = [48, 96, 224, 448]  ,  lowerCAmelCase_ = [True, True, True, True]  ,  lowerCAmelCase_ = 448  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 4  ,  lowerCAmelCase_ = 7  ,  lowerCAmelCase_ = 5  ,  lowerCAmelCase_ = 8  ,  lowerCAmelCase_ = 4  ,  lowerCAmelCase_ = 0.0  ,  lowerCAmelCase_ = 16  ,  lowerCAmelCase_ = 3  ,  lowerCAmelCase_ = 3  ,  lowerCAmelCase_ = 3  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = 0.0  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = 1e-5  ,  lowerCAmelCase_ = "gelu"  ,  lowerCAmelCase_ = 0.02  ,  lowerCAmelCase_ = 1e-1_2  ,  lowerCAmelCase_ = 224  ,  lowerCAmelCase_ = 1e-0_5  ,  **lowerCAmelCase_  ,  ):
		super().__init__(**lowerCAmelCase_				)

		A_		:    str						     = hidden_act
		A_		:    Optional[Any]						     = hidden_dropout_prob
		A_		:    Optional[int]						     = hidden_sizes
		A_		:    List[Any]						     = num_hidden_layers
		A_		:    str						     = num_attention_heads
		A_		:    Tuple						     = initializer_range
		A_		:    Any						     = layer_norm_eps
		A_		:    Union[str, Any]						     = patch_size
		A_		:    List[Any]						     = num_channels
		A_		:    Tuple						     = depths
		A_		:    str						     = mlp_expansion_ratio
		A_		:    Optional[Any]						     = downsamples
		A_		:    List[Any]						     = dim
		A_		:    Optional[int]						     = key_dim
		A_		:    List[str]						     = attention_ratio
		A_		:    Any						     = resolution
		A_		:    Any						     = pool_size
		A_		:    int						     = downsample_patch_size
		A_		:    Optional[int]						     = downsample_stride
		A_		:    str						     = downsample_pad
		A_		:    Optional[Any]						     = drop_path_rate
		A_		:    List[Any]						     = num_metaad_blocks
		A_		:    Union[str, Any]						     = distillation
		A_		:    Dict						     = use_layer_scale
		A_		:    Dict						     = layer_scale_init_value
		A_		:    Dict						     = image_size
		A_		:    Optional[int]						     = batch_norm_eps









"""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
#
########################################################################


_lowerCAmelCase       =				16
_lowerCAmelCase       =				32






def       __UpperCamelCase						(		snake_case__ , snake_case__ = 16       ):
	A_		:    List[str]						     = AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	A_		:    Optional[Any]						     = load_dataset("""glue""" , """mrpc"""       )

	def tokenize_function(snake_case__       ):
		# max_length=None => use the model max length (it's actually the default)
		A_		:    Tuple						     = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=snake_case__ , max_length=snake_case__       )
		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():
		A_		:    str						     = datasets.map(
		    snake_case__ , batched=snake_case__ , 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
	A_		:    int						     = tokenized_datasets.rename_column("""label""" , """labels"""       )

	def collate_fn(snake_case__       ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A_		:    Optional[Any]						     = 128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A_		:    Tuple						     = 16
		elif accelerator.mixed_precision != "no":
			A_		:    List[Any]						     = 8
		else:
			A_		:    List[Any]						     = None

		return tokenizer.pad(
		    snake_case__ , padding="""longest""" , max_length=snake_case__ , pad_to_multiple_of=snake_case__ , return_tensors="""pt""" , )

	# Instantiate dataloaders.
	A_		:    Optional[Any]						     = DataLoader(
	    tokenized_datasets["""train"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )
	A_		:    List[str]						     = DataLoader(
	    tokenized_datasets["""validation"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )

	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

							_lowerCAmelCase       =				mocked_dataloaders  # noqa: F811






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , snake_case__       ) == "1":
		A_		:    List[Any]						     = 2
	# New Code #
	A_		:    Tuple						     = int(args.gradient_accumulation_steps       )
	# Initialize accelerator
	A_		:    Any						     = Accelerator(
	    cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=snake_case__       )
	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
	A_		:    Union[str, Any]						     = config["""lr"""]
	A_		:    Tuple						     = int(config["""num_epochs"""]       )
	A_		:    List[Any]						     = int(config["""seed"""]       )
	A_		:    Dict						     = int(config["""batch_size"""]       )

	A_		:    Tuple						     = evaluate.load("""glue""" , """mrpc"""       )

	set_seed(snake_case__       )
	A_     ,  A_		:    List[Any]						     = get_dataloaders(snake_case__ , snake_case__       )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_		:    Dict						     = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=snake_case__       )

	# 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).
	A_		:    Union[str, Any]						     = model.to(accelerator.device       )

	# Instantiate optimizer
	A_		:    Optional[int]						     = AdamW(params=model.parameters() , lr=snake_case__       )

	# Instantiate scheduler
	A_		:    Union[str, Any]						     = get_linear_schedule_with_warmup(
	    optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__       ) * 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.
	A_     ,  A_     ,  A_     ,  A_     ,  A_		:    Optional[int]						     = accelerator.prepare(
	    snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )

	# Now we train the model
	for epoch in range(snake_case__       ):
		model.train()
		for step, batch in enumerate(snake_case__       ):
			# 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(snake_case__       ):
				A_		:    Optional[int]						     = model(**snake_case__       )
				A_		:    Tuple						     = output.loss
				accelerator.backward(snake_case__       )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(snake_case__       ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device       )
			with torch.no_grad():
				A_		:    List[str]						     = model(**snake_case__       )
			A_		:    List[str]						     = outputs.logits.argmax(dim=-1       )
			A_     ,  A_		:    List[Any]						     = accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
			metric.add_batch(
			    predictions=snake_case__ , references=snake_case__ , )

		A_		:    str						     = metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:""" , snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = argparse.ArgumentParser(description="""Simple example of training script."""       )
	parser.add_argument(
	    """--mixed_precision""" , type=snake_case__ , default=snake_case__ , 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=snake_case__ , 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."""       )
	A_		:    int						     = parser.parse_args()
	A_		:    Optional[Any]						     = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(snake_case__ , snake_case__       )


if __name__ == "__main__":
							main()

