



"""simple docstring"""







import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# 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)
#   - FSDP
#
# 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:   Optional[int]       			=							16
UpperCAmelCase:   Any       			=							32


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase	):
		return int(x / 2**20	)





class    UpperCamelCase     :




	"""simple docstring"""






	def __enter__(			self					):
			gc.collect()
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
			_lowercase							:      Optional[int]			=					torch.cuda.memory_allocated()
			_lowercase							:      List[Any]			=					psutil.Process()

			_lowercase							:      Union[str, Any]			=					self.cpu_mem_used()
			_lowercase							:      Dict			=					True
			_lowercase							:      List[str]			=					threading.Thread(target=self.peak_monitor_func					)
			_lowercase							:      Tuple			=					True
			peak_monitor_thread.start()
			return self






	def 					lowerCamelCase__ (			self					):
			return self.process.memory_info().rss






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					-1

			while True:
					_lowercase							:      Any			=					max(self.cpu_mem_used()							,self.cpu_peak					)

					# can't sleep or will not catch the peak right (this comment is here on purpose)
					# time.sleep(0.001) # 1msec

					if not self.peak_monitoring:
							break






	def __exit__(			self							,*UpperCAmelCase_					):
			_lowercase							:      Optional[Any]			=					False

			gc.collect()
			torch.cuda.empty_cache()
			_lowercase							:      List[str]			=					torch.cuda.memory_allocated()
			_lowercase							:      str			=					torch.cuda.max_memory_allocated()
			_lowercase							:      Optional[Any]			=					bamb(self.end - self.begin					)
			_lowercase							:      Optional[int]			=					bamb(self.peak - self.begin					)

			_lowercase							:      Optional[int]			=					self.cpu_mem_used()
			_lowercase							:      str			=					bamb(self.cpu_end - self.cpu_begin					)
			_lowercase							:      Union[str, Any]			=					bamb(self.cpu_peak - self.cpu_begin					)
			# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

		UpperCAmelCase:   str       			=							mocked_dataloaders  # noqa: F811


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase	):






		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,							_snake_case	) == "1":
				_lowercase							:      int			=					2

		# New Code #
		# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
		_lowercase							:      Any			=					FullyShardedDataParallelPlugin(
		    state_dict_config=FullStateDictConfig(offload_to_cpu=_snake_case					,							ranka_only=_snake_case	)					,							optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_snake_case					,							ranka_only=_snake_case	)					,							)

		# Initialize accelerator
		if args.with_tracking:
				_lowercase							:      Tuple			=					Accelerator(
				    cpu=args.cpu					,							mixed_precision=args.mixed_precision					,							log_with="""wandb"""					,							project_dir=args.logging_dir					,							fsdp_plugin=_snake_case					,							)
		else:
				_lowercase							:      Optional[int]			=					Accelerator(fsdp_plugin=_snake_case	)
		accelerator.print(accelerator.distributed_type	)

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

		# We need to initialize the trackers we use, and also store our configuration
		if args.with_tracking:
				_lowercase							:      List[Any]			=					vars(_snake_case	)
				accelerator.init_trackers("""fsdp_glue_no_trainer"""					,							_snake_case	)

		_lowercase							:      Tuple			=					AutoTokenizer.from_pretrained(args.model_name_or_path	)
		_lowercase							:      Optional[int]			=					load_dataset("""glue"""					,							"""mrpc"""	)
		_lowercase							:      Optional[int]			=					evaluate.load("""glue"""					,							"""mrpc"""	)

		def tokenize_function(__UpperCAmelCase	):
				# max_length=None => use the model max length (it's actually the default)
				_lowercase							:      List[Any]			=					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():
				_lowercase							:      List[Any]			=					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
		_lowercase							:      int			=					tokenized_datasets.rename_column("""label"""					,							"""labels"""	)

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

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

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

		# Instantiate dataloaders.
		_lowercase							:      Dict			=					DataLoader(
		    tokenized_datasets["""train"""]					,							shuffle=_snake_case					,							collate_fn=_snake_case					,							batch_size=_snake_case	)
		_lowercase							:      List[str]			=					DataLoader(
		    tokenized_datasets["""validation"""]					,							shuffle=_snake_case					,							collate_fn=_snake_case					,							batch_size=_snake_case	)

		set_seed(_snake_case	)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_lowercase							:      str			=					AutoModelForSequenceClassification.from_pretrained(
		    args.model_name_or_path					,							return_dict=_snake_case					,							low_cpu_mem_usage=_snake_case	)

		# New Code #
		# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
		_lowercase							:      Optional[int]			=					accelerator.prepare(_snake_case	)
		accelerator.print(_snake_case	)

		# Instantiate optimizer
		# New Code #
		# For FSDP feature, at present it doesn't support multiple parameter groups,
		# so we need to create a single parameter group for the whole model
		_lowercase							:      Dict			=					torch.optim.AdamW(params=model.parameters()					,							lr=_snake_case					,							weight_decay=2E-4	)

		# Instantiate scheduler
		_lowercase							:      Optional[Any]			=					get_linear_schedule_with_warmup(
		    optimizer=_snake_case					,							num_warmup_steps=10					,							num_training_steps=(len(_snake_case	) * num_epochs) // gradient_accumulation_steps					,							)

		# New Code #
		# For FSDP feature, prepare everything except the model as we have already prepared the model
		# before creating the optimizer
		# 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.
		_lowercase			,   _lowercase			,   _lowercase			,   _lowercase							:      Optional[int]			=					accelerator.prepare(
		    _snake_case					,							_snake_case					,							_snake_case					,							_snake_case	)

		_lowercase							:      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	)
						_lowercase							:      Optional[int]			=					os.path.basename(args.resume_from_checkpoint	)
				else:
						# Get the most recent checkpoint
						_lowercase							:      Dict			=					[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
						dirs.sort(key=os.path.getctime	)
						_lowercase							:      Optional[Any]			=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				_lowercase							:      Any			=					os.path.splitext(_snake_case	)[0]

				if "epoch" in training_difference:
						num_epochs -= int(training_difference.replace("""epoch_"""					,							""""""	)	)
						_lowercase							:      str			=					None
				else:
						_lowercase							:      Optional[Any]			=					int(training_difference.replace("""step_"""					,							""""""	)	)
						num_epochs -= resume_step // len(_snake_case	)
						# If resuming by step, we also need to know exactly how far into the DataLoader we went
						_lowercase							:      Optional[int]			=					(num_epochs * len(_snake_case	)) - resume_step

    # Now we train the model
		for epoch in range(_snake_case	):
				# New Code #
				# context manager to track the peak memory usage during the training epoch
				with TorchTracemalloc() as tracemalloc:
						model.train()
						if args.with_tracking:
								_lowercase							:      List[Any]			=					0
						for step, batch in enumerate(_snake_case	):
								# We need to skip steps until we reach the resumed step
								if args.resume_from_checkpoint and epoch == 0:
										if resume_step is not None and step < resume_step:
												pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								_lowercase							:      Any			=					model(**_snake_case	)
								_lowercase							:      Dict			=					outputs.loss
								_lowercase							:      int			=					loss / gradient_accumulation_steps
								# We keep track of the loss at each epoch
								if args.with_tracking:
										total_loss += loss.detach().float()
								accelerator.backward(_snake_case	)
								if step % gradient_accumulation_steps == 0:
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# accelerator.print(lr_scheduler.get_lr())

								overall_step += 1

								if isinstance(_snake_case					,							_snake_case	):
										_lowercase							:      str			=					F"""step_{overall_step}"""
										if overall_step % checkpointing_steps == 0:
												if args.output_dir is not None:
														_lowercase							:      int			=					os.path.join(args.output_dir					,							_snake_case	)
												accelerator.save_state(_snake_case	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
				accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin	)	)	)
				accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used	)	)
				accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked	)	)
				accelerator.print(
				    """Total Peak Memory consumed during the train (max): {}""".format(
				        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
				# Logging the peak memory usage of the GPU to the tracker
				if args.with_tracking:
						accelerator.log(
						    {
						        """train_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin	),
						    }					,							step=_snake_case					,							)

				# New Code #
				# context manager to track the peak memory usage during the evaluation
				with TorchTracemalloc() as tracemalloc:
						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():
										_lowercase							:      Optional[int]			=					model(**_snake_case	)
								_lowercase							:      List[Any]			=					outputs.logits.argmax(dim=-1	)
								_lowercase			,   _lowercase							:      Optional[int]			=					accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
								metric.add_batch(
								    predictions=_snake_case					,							references=_snake_case					,							)

						_lowercase							:      Optional[Any]			=					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}:"""					,							_snake_case	)
						if args.with_tracking:
								accelerator.log(
								    {
								        """accuracy""": eval_metric["""accuracy"""],
								        """f1""": eval_metric["""f1"""],
								        """train_loss""": total_loss.item() / len(_snake_case	),
								    }					,							step=_snake_case					,							)

						if checkpointing_steps == "epoch":
								_lowercase							:      Optional[int]			=					F"""epoch_{epoch}"""
								if args.output_dir is not None:
										_lowercase							:      List[str]			=					os.path.join(args.output_dir					,							_snake_case	)
								accelerator.save_state(_snake_case	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
				accelerator.print("""Memory before entering the eval : {}""".format(bamb(tracemalloc.begin	)	)	)
				accelerator.print("""Memory consumed at the end of the eval (end-begin): {}""".format(tracemalloc.used	)	)
				accelerator.print("""Peak Memory consumed during the eval (max-begin): {}""".format(tracemalloc.peaked	)	)
				accelerator.print(
				    """Total Peak Memory consumed during the eval (max): {}""".format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
				# Logging the peak memory usage of the GPU to the tracker
				if args.with_tracking:
						accelerator.log(
						    {
						        """eval_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin	),
						    }					,							step=_snake_case					,							)

		if args.with_tracking:
				accelerator.end_training()


def       __SCREAMING_SNAKE_CASE      (   ):
		_lowercase							:      Optional[int]			=					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."""					,							)
		parser.add_argument("""--cpu"""					,							action="""store_true"""					,							help="""If passed, will train on the CPU."""	)
		parser.add_argument(
		    """--checkpointing_steps"""					,							type=_snake_case					,							default=_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=_snake_case					,							default=_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=_snake_case					,							default="""."""					,							help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""					,							)
		parser.add_argument(
		    """--logging_dir"""					,							type=_snake_case					,							default="""logs"""					,							help="""Location on where to store experiment tracking logs`"""					,							)
		parser.add_argument(
		    """--model_name_or_path"""					,							type=_snake_case					,							help="""Path to pretrained model or model identifier from huggingface.co/models."""					,							required=_snake_case					,							)
		_lowercase							:      List[Any]			=					parser.parse_args()
		_lowercase							:      str			=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(_snake_case					,							_snake_case	)


if __name__ == "__main__":
		main()











"""simple docstring"""







import cva
import numpy as np

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      try:
            return int(image[x_coordinate][y_coordinate] >= center							)
      except (IndexError, TypeError):
            return 0

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       image[x_coordinate][y_coordinate]
      UpperCAmelCase							=       [1, 2, 4, 8, 16, 32, 64, 128]

      # skip get_neighbors_pixel if center is null
      if center is None:
            return 0

      # Starting from the top right, assigning value to pixels clockwise
      UpperCAmelCase							=       [
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       y_coordinate - 1      ,       _snake_case							),
      ]

      # Converting the binary value to decimal.
      return sum(
          binary_value * power for binary_value, power in zip(_snake_case      ,       _snake_case							)							)


if __name__ == "__main__":
  # Reading the image and converting it to grayscale.
  _UpperCamelCase     			=  cva.imread(
      """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
  )

  # Create a numpy array as the same height and width of read image
  _UpperCamelCase     			=  np.zeros((image.shape[0], image.shape[1]))

  # Iterating through the image and calculating the
  # local binary pattern value for each pixel.
  for i in range(0, image.shape[0]):
    for j in range(0, image.shape[1]):
      _UpperCamelCase     			=  local_binary_value(image, i, j)

  cva.imshow("""local binary pattern""", lbp_image)
  cva.waitKey(0)
  cva.destroyAllWindows()



