


'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BlipImageProcessor





class 					lowercase__						(		unittest.TestCase      ):





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case=7					,      __snake_case=3					,      __snake_case=18					,      __snake_case=30					,      __snake_case=400					,      __snake_case=True					,      __snake_case=None					,      __snake_case=True					,      __snake_case=False					,      __snake_case=[0.48145466, 0.4578275, 0.40821073]					,      __snake_case=[0.26862954, 0.26130258, 0.27577711]					,      __snake_case=True					,      ):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   size if size is not None else {"""height""": 20, """width""": 20}
											_SCREAMING_SNAKE_CASE				: Any  	=   parent
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   batch_size
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   num_channels
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   image_size
											_SCREAMING_SNAKE_CASE				: List[str]  	=   min_resolution
											_SCREAMING_SNAKE_CASE				: int  	=   max_resolution
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   do_resize
											_SCREAMING_SNAKE_CASE				: Tuple  	=   size
											_SCREAMING_SNAKE_CASE				: List[str]  	=   do_normalize
											_SCREAMING_SNAKE_CASE				: Tuple  	=   image_mean
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   image_std
											_SCREAMING_SNAKE_CASE				: int  	=   do_pad
											_SCREAMING_SNAKE_CASE				: str  	=   do_convert_rgb






							def UpperCAmelCase_			(					self						):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_convert_rgb": self.do_convert_rgb,
											    "do_pad": self.do_pad,
											}






							def UpperCAmelCase_			(					self					,      __snake_case=False					,      __snake_case=False					,      __snake_case=False						):
											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   []
															for i in range(self.batch_size						):
																			image_inputs.append(
																			    np.random.randint(
																			        255					,      size=(self.num_channels, self.max_resolution, self.max_resolution)					,      dtype=np.uinta						)						)
											else:
															_SCREAMING_SNAKE_CASE				: Any  	=   []
															for i in range(self.batch_size						):
																			_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[Any]  	=   np.random.choice(np.arange(self.min_resolution					,      self.max_resolution						)					,      2						)
																			image_inputs.append(np.random.randint(255					,      size=(self.num_channels, width, height)					,      dtype=np.uinta						)						)

											if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															_SCREAMING_SNAKE_CASE				: str  	=   [Image.fromarray(np.moveaxis(__snake_case					,      0					,      -1						)						) for x in image_inputs]

											if torchify:
															_SCREAMING_SNAKE_CASE				: str  	=   [torch.from_numpy(__snake_case						) for x in image_inputs]

											return image_inputs





@require_torch
@require_vision
class 					lowercase__						(		_snake_case  ,  unittest.TestCase      ):





							'''simple docstring'''


							A_							:   Tuple   			=						BlipImageProcessor if is_vision_available() else None






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   BlipImageProcessingTester(self						)






							@property
							def UpperCAmelCase_			(					self						):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: str  	=   self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(__snake_case					,      """do_resize"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """size"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """do_normalize"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """image_mean"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """image_std"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """do_convert_rgb"""						)						)






							def UpperCAmelCase_			(					self						):
											pass






							def UpperCAmelCase_			(					self						):
											# Initialize image_processor
											_SCREAMING_SNAKE_CASE				: List[str]  	=   self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											_SCREAMING_SNAKE_CASE				: int  	=   self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case						)
											for image in image_inputs:
															self.assertIsInstance(__snake_case					,      Image.Image						)

											# Test not batched input
											_SCREAMING_SNAKE_CASE				: str  	=   image_processor(image_inputs[0]					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )

											# Test batched
											_SCREAMING_SNAKE_CASE				: int  	=   image_processor(__snake_case					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )






							def UpperCAmelCase_			(					self						):
											# Initialize image_processor
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case					,      numpify=__snake_case						)
											for image in image_inputs:
															self.assertIsInstance(__snake_case					,      np.ndarray						)

											# Test not batched input
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   image_processor(image_inputs[0]					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )

											# Test batched
											_SCREAMING_SNAKE_CASE				: Tuple  	=   image_processor(__snake_case					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )






							def UpperCAmelCase_			(					self						):
											# Initialize image_processor
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case					,      torchify=__snake_case						)
											for image in image_inputs:
															self.assertIsInstance(__snake_case					,      torch.Tensor						)

											# Test not batched input
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   image_processor(image_inputs[0]					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )

											# Test batched
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   image_processor(__snake_case					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )





@require_torch
@require_vision
class 					lowercase__						(		_snake_case  ,  unittest.TestCase      ):





							'''simple docstring'''


							A_							:   Optional[Any]   			=						BlipImageProcessor if is_vision_available() else None






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: int  	=   BlipImageProcessingTester(self					,      num_channels=4						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   3






							@property
							def UpperCAmelCase_			(					self						):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(__snake_case					,      """do_resize"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """size"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """do_normalize"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """image_mean"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """image_std"""						)						)
											self.assertTrue(hasattr(__snake_case					,      """do_convert_rgb"""						)						)






							def UpperCAmelCase_			(					self						):
											pass






							def UpperCAmelCase_			(					self						):
											# Initialize image_processor
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case						)
											for image in image_inputs:
															self.assertIsInstance(__snake_case					,      Image.Image						)

											# Test not batched input
											_SCREAMING_SNAKE_CASE				: Tuple  	=   image_processor(image_inputs[0]					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        1,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )

											# Test batched
											_SCREAMING_SNAKE_CASE				: Tuple  	=   image_processor(__snake_case					,      return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape					,      (
											        self.image_processor_tester.batch_size,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )					,      )








'''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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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_	:     Any							     =	16
UpperCAmelCase_	:     Dict							     =	32







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				if args.with_tracking:
								_SCREAMING_SNAKE_CASE				: List[str]  	=   Accelerator(
								    cpu=args.cpu							,   mixed_precision=args.mixed_precision							,   log_with="""all"""							,   project_dir=args.project_dir  )
				else:
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   Accelerator(cpu=args.cpu							,   mixed_precision=args.mixed_precision  )

				if hasattr(args.checkpointing_steps							,   """isdigit"""  ):
								if args.checkpointing_steps == "epoch":
												_SCREAMING_SNAKE_CASE				: str  	=   args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												_SCREAMING_SNAKE_CASE				: Dict  	=   int(args.checkpointing_steps  )
								else:
												raise ValueError(
												    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""  )
				else:
								_SCREAMING_SNAKE_CASE				: int  	=   None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_SCREAMING_SNAKE_CASE				: List[str]  	=   config["""lr"""]
				_SCREAMING_SNAKE_CASE				: Tuple  	=   int(config["""num_epochs"""]  )
				_SCREAMING_SNAKE_CASE				: Dict  	=   int(config["""seed"""]  )
				_SCREAMING_SNAKE_CASE				: Tuple  	=   int(config["""batch_size"""]  )

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								_SCREAMING_SNAKE_CASE				: List[str]  	=   os.path.split(SCREAMING_SNAKE_CASE__  )[-1].split("""."""  )[0]
								accelerator.init_trackers(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: Tuple  	=   AutoTokenizer.from_pretrained("""bert-base-cased"""  )
				_SCREAMING_SNAKE_CASE				: Dict  	=   load_dataset("""glue"""							,   """mrpc"""  )
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   evaluate.load("""glue"""							,   """mrpc"""  )

				def tokenize_function(SCREAMING_SNAKE_CASE__  ):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   tokenizer(examples["""sentence1"""]							,   examples["""sentence2"""]							,   truncation=SCREAMING_SNAKE_CASE__							,   max_length=SCREAMING_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():
								_SCREAMING_SNAKE_CASE				: Any  	=   datasets.map(
								    SCREAMING_SNAKE_CASE__							,   batched=SCREAMING_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
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   tokenized_datasets.rename_column("""label"""							,   """labels"""  )

				# If the batch size is too big we use gradient accumulation
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								_SCREAMING_SNAKE_CASE				: Any  	=   batch_size // MAX_GPU_BATCH_SIZE
								_SCREAMING_SNAKE_CASE				: str  	=   MAX_GPU_BATCH_SIZE

				def collate_fn(SCREAMING_SNAKE_CASE__  ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_SCREAMING_SNAKE_CASE				: Union[str, 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":
												_SCREAMING_SNAKE_CASE				: Dict  	=   16
								elif accelerator.mixed_precision != "no":
												_SCREAMING_SNAKE_CASE				: int  	=   8
								else:
												_SCREAMING_SNAKE_CASE				: str  	=   None

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

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE				: Dict  	=   DataLoader(
				    tokenized_datasets["""train"""]							,   shuffle=SCREAMING_SNAKE_CASE__							,   collate_fn=SCREAMING_SNAKE_CASE__							,   batch_size=SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   DataLoader(
				    tokenized_datasets["""validation"""]							,   shuffle=SCREAMING_SNAKE_CASE__							,   collate_fn=SCREAMING_SNAKE_CASE__							,   batch_size=SCREAMING_SNAKE_CASE__  )

				set_seed(SCREAMING_SNAKE_CASE__  )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_SCREAMING_SNAKE_CASE				: List[str]  	=   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,   return_dict=SCREAMING_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).
				_SCREAMING_SNAKE_CASE				: Any  	=   model.to(accelerator.device  )

				# Instantiate optimizer
				_SCREAMING_SNAKE_CASE				: List[str]  	=   AdamW(params=model.parameters()							,   lr=SCREAMING_SNAKE_CASE__  )

				# Instantiate scheduler
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE__							,   num_warmup_steps=100							,   num_training_steps=(len(SCREAMING_SNAKE_CASE__  ) * num_epochs) // gradient_accumulation_steps							,   )

				# 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				: Optional[Any]  	=   accelerator.prepare(
				    SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

				# We need to keep track of how many total steps we have iterated over
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   0
				# We also need to keep track of the stating epoch so files are named properly
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   0

				# Potentially load in the weights and states from a previous save
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""  )
												accelerator.load_state(args.resume_from_checkpoint  )
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   os.path.basename(args.resume_from_checkpoint  )
								else:
												# Get the most recent checkpoint
												_SCREAMING_SNAKE_CASE				: int  	=   [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
												dirs.sort(key=os.path.getctime  )
												_SCREAMING_SNAKE_CASE				: Dict  	=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								_SCREAMING_SNAKE_CASE				: str  	=   os.path.splitext(SCREAMING_SNAKE_CASE__  )[0]

								if "epoch" in training_difference:
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   int(training_difference.replace("""epoch_"""							,   """"""  )  ) + 1
												_SCREAMING_SNAKE_CASE				: List[str]  	=   None
								else:
												_SCREAMING_SNAKE_CASE				: List[str]  	=   int(training_difference.replace("""step_"""							,   """"""  )  )
												_SCREAMING_SNAKE_CASE				: Any  	=   resume_step // len(SCREAMING_SNAKE_CASE__  )
												resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__  )

    # Now we train the model
				for epoch in range(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
								model.train()
								if args.with_tracking:
												_SCREAMING_SNAKE_CASE				: str  	=   0
								if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
												# We need to skip steps until we reach the resumed step
												_SCREAMING_SNAKE_CASE				: List[str]  	=   accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   train_dataloader
								for step, batch in enumerate(SCREAMING_SNAKE_CASE__  ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												_SCREAMING_SNAKE_CASE				: Tuple  	=   model(**SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   outputs.loss
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(SCREAMING_SNAKE_CASE__  )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

												if isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   f"""step_{overall_step}"""
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   os.path.join(args.output_dir							,   SCREAMING_SNAKE_CASE__  )
																				accelerator.save_state(SCREAMING_SNAKE_CASE__  )

								model.eval()
								for step, batch in enumerate(SCREAMING_SNAKE_CASE__  ):
												# 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				: Union[str, Any]  	=   model(**SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Any  	=   outputs.logits.argmax(dim=-1  )
												_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Tuple  	=   accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
												metric.add_batch(
												    predictions=SCREAMING_SNAKE_CASE__							,   references=SCREAMING_SNAKE_CASE__							,   )

								_SCREAMING_SNAKE_CASE				: List[str]  	=   metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:"""							,   SCREAMING_SNAKE_CASE__  )
								if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE__  ),
												        """epoch""": epoch,
												    }							,   step=SCREAMING_SNAKE_CASE__							,   )

								if checkpointing_steps == "epoch":
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   f"""epoch_{epoch}"""
												if args.output_dir is not None:
																_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   os.path.join(args.output_dir							,   SCREAMING_SNAKE_CASE__  )
												accelerator.save_state(SCREAMING_SNAKE_CASE__  )

				if args.with_tracking:
								accelerator.end_training()







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   argparse.ArgumentParser(description="""Simple example of training script."""  )
				parser.add_argument(
				    """--mixed_precision"""							,   type=SCREAMING_SNAKE_CASE__							,   default=SCREAMING_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."""							,   )
				parser.add_argument("""--cpu"""							,   action="""store_true"""							,   help="""If passed, will train on the CPU."""  )
				parser.add_argument(
				    """--checkpointing_steps"""							,   type=SCREAMING_SNAKE_CASE__							,   default=SCREAMING_SNAKE_CASE__							,   help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""							,   )
				parser.add_argument(
				    """--resume_from_checkpoint"""							,   type=SCREAMING_SNAKE_CASE__							,   default=SCREAMING_SNAKE_CASE__							,   help="""If the training should continue from a checkpoint folder."""							,   )
				parser.add_argument(
				    """--with_tracking"""							,   action="""store_true"""							,   help="""Whether to load in all available experiment trackers from the environment and use them for logging."""							,   )
				parser.add_argument(
				    """--output_dir"""							,   type=SCREAMING_SNAKE_CASE__							,   default="""."""							,   help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""							,   )
				parser.add_argument(
				    """--project_dir"""							,   type=SCREAMING_SNAKE_CASE__							,   default="""logs"""							,   help="""Location on where to store experiment tracking logs` and relevent project information"""							,   )
				_SCREAMING_SNAKE_CASE				: List[str]  	=   parser.parse_args()
				_SCREAMING_SNAKE_CASE				: str  	=   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
				main()




