
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					   =       1_6
__lowerCAmelCase					   =       3_2


def    a      ( a		,						a = 16	)     ->Tuple:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained('''bert-base-cased'''	)
			SCREAMING_SNAKE_CASE     						= load_dataset('''glue'''		,						'''mrpc'''	)

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

			def collate_fn(a	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						SCREAMING_SNAKE_CASE     						= 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":
									SCREAMING_SNAKE_CASE     						= 16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE     						= 8
						else:
									SCREAMING_SNAKE_CASE     						= None

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

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE     						= DataLoader(
			    tokenized_datasets['''train''']		,						shuffle=a		,						collate_fn=a		,						batch_size=a	)
			SCREAMING_SNAKE_CASE     						= 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

							__lowerCAmelCase					   =       mocked_dataloaders  # noqa: F811


def    a      ( a		,						a	)     ->str:




			'''simple docstring'''


			if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,						a	) == "1":
						SCREAMING_SNAKE_CASE     						= 2
			# New Code #
			SCREAMING_SNAKE_CASE     						= int(args.gradient_accumulation_steps	)
			# Initialize accelerator
			SCREAMING_SNAKE_CASE     						= Accelerator(
			    cpu=args.cpu		,						mixed_precision=args.mixed_precision		,						gradient_accumulation_steps=a	)
			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
			SCREAMING_SNAKE_CASE     						= config['''lr''']
			SCREAMING_SNAKE_CASE     						= int(config['''num_epochs''']	)
			SCREAMING_SNAKE_CASE     						= int(config['''seed''']	)
			SCREAMING_SNAKE_CASE     						= int(config['''batch_size''']	)

			SCREAMING_SNAKE_CASE     						= evaluate.load('''glue'''		,						'''mrpc'''	)

			set_seed(a	)
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= get_dataloaders(a		,						a	)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			SCREAMING_SNAKE_CASE     						= 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).
			SCREAMING_SNAKE_CASE     						= model.to(accelerator.device	)

			# Instantiate optimizer
			SCREAMING_SNAKE_CASE     						= AdamW(params=model.parameters()		,						lr=a	)

			# Instantiate scheduler
			SCREAMING_SNAKE_CASE     						= 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.
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= accelerator.prepare(
			    a		,						a		,						a		,						a		,						a	)

			# Now we train the model
			for epoch in range(a	):
						model.train()
						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	):
												SCREAMING_SNAKE_CASE     						= model(**a	)
												SCREAMING_SNAKE_CASE     						= output.loss
												accelerator.backward(a	)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						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():
												SCREAMING_SNAKE_CASE     						= model(**a	)
									SCREAMING_SNAKE_CASE     						= outputs.logits.argmax(dim=-1	)
									SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
									metric.add_batch(
									    predictions=a		,						references=a		,						)

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


def    a      ( )     ->Optional[int]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 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('''--cpu'''		,						action='''store_true'''		,						help='''If passed, will train on the CPU.'''	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(a		,						a	)


if __name__ == "__main__":
							main()

import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					   =       logging.get_logger(__name__)





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :List[Any]   ,     *lowercase      :List[str]   ,     **lowercase      :List[Any]			)		->      None:







				"""simple docstring"""






				warnings.warn(
				    '''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use YolosImageProcessor instead.'''   ,     lowercase   ,     )
				super().__init__(*lowercase   ,     **lowercase			)