



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


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					"""glpn"""


							def __init__(							self  ,  a_=3  ,  a_=4  ,  a_=[2, 2, 2, 2]  ,  a_=[8, 4, 2, 1]  ,  a_=[32, 64, 160, 256]  ,  a_=[7, 3, 3, 3]  ,  a_=[4, 2, 2, 2]  ,  a_=[1, 2, 5, 8]  ,  a_=[4, 4, 4, 4]  ,  a_="gelu"  ,  a_=0.0  ,  a_=0.0  ,  a_=0.02  ,  a_=0.1  ,  a_=1E-6  ,  a_=64  ,  a_=10  ,  a_=-1  ,  **a_  ,  ):
											super().__init__(**a_		)

											a__				      =		num_channels
											a__				      =		num_encoder_blocks
											a__				      =		depths
											a__				      =		sr_ratios
											a__				      =		hidden_sizes
											a__				      =		patch_sizes
											a__				      =		strides
											a__				      =		mlp_ratios
											a__				      =		num_attention_heads
											a__				      =		hidden_act
											a__				      =		hidden_dropout_prob
											a__				      =		attention_probs_dropout_prob
											a__				      =		initializer_range
											a__				      =		drop_path_rate
											a__				      =		layer_norm_eps
											a__				      =		decoder_hidden_size
											a__				      =		max_depth
											a__				      =		head_in_index






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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


UpperCAmelCase							 =					16
UpperCAmelCase							 =					32

def 			A_      (	__a     :				Accelerator ,		__a     :				int = 16		):

				"""simple docstring"""
				a__				      =		AutoTokenizer.from_pretrained("""bert-base-cased"""		)
				a__				      =		load_dataset("""glue""" ,		"""mrpc"""		)

				def tokenize_function(__a     :				int		):
								# max_length=None => use the model max length (it's actually the default)
								a__				      =		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
				# starting with the main process first:
				with accelerator.main_process_first():
								a__				      =		datasets.map(
								    __a ,		batched=__a ,		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__				      =		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.
								a__				      =		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__				      =		16
								elif accelerator.mixed_precision != "no":
												a__				      =		8
								else:
												a__				      =		None

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

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

				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

	UpperCAmelCase							 =					mocked_dataloaders  # noqa: F811

def 			A_      (	__a     :				List[Any] ,		__a     :				Tuple		):

				"""simple docstring"""
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,		__a		) == "1":
								a__				      =		2
				# New Code #
				a__				      =		int(args.gradient_accumulation_steps		)
				a__				      =		int(args.local_sgd_steps		)
				# Initialize accelerator
				a__				      =		Accelerator(
				    cpu=args.cpu ,		mixed_precision=args.mixed_precision ,		gradient_accumulation_steps=__a		)
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""		)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				a__				      =		config["""lr"""]
				a__				      =		int(config["""num_epochs"""]		)
				a__				      =		int(config["""seed"""]		)
				a__				      =		int(config["""batch_size"""]		)

				a__				      =		evaluate.load("""glue""" ,		"""mrpc"""		)

				set_seed(__a		)
				a__      ,			a__				      =		get_dataloaders(__a ,		__a		)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				a__				      =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,		return_dict=__a		)

				# 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__				      =		model.to(accelerator.device		)

				# Instantiate optimizer
				a__				      =		AdamW(params=model.parameters() ,		lr=__a		)

				# Instantiate scheduler
				a__				      =		get_linear_schedule_with_warmup(
				    optimizer=__a ,		num_warmup_steps=100 ,		num_training_steps=(len(__a		) * 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__				      =		accelerator.prepare(
				    __a ,		__a ,		__a ,		__a ,		__a		)

				# Now we train the model
				for epoch in range(__a		):
								model.train()
								with LocalSGD(
								    accelerator=__a ,		model=__a ,		local_sgd_steps=__a ,		enabled=local_sgd_steps is not None		) as local_sgd:
												for step, batch in enumerate(__a		):
																# 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(__a		):
																				a__				      =		model(**__a		)
																				a__				      =		output.loss
																				accelerator.backward(__a		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								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():
																a__				      =		model(**__a		)
												a__				      =		outputs.logits.argmax(dim=-1		)
												a__      ,			a__				      =		accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
												metric.add_batch(
												    predictions=__a ,		references=__a ,		)

								a__				      =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:''' ,		__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser(description="""Simple example of training script."""		)
				parser.add_argument(
				    """--mixed_precision""" ,		type=__a ,		default=__a ,		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=__a ,		default=1 ,		help="""The number of minibatches to be ran before gradients are accumulated.""" ,		)
				parser.add_argument(
				    """--local_sgd_steps""" ,		type=__a ,		default=8 ,		help="""Number of local SGD steps or None to disable local SGD"""		)
				parser.add_argument("""--cpu""" ,		action="""store_true""" ,		help="""If passed, will train on the CPU."""		)
				a__				      =		parser.parse_args()
				a__				      =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(__a ,		__a		)


if __name__ == "__main__":
	main()

