
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
#
########################################################################


lowercase__							:							Dict													=     1_6
lowercase__							:							int													=     3_2




def 		A_	(							snake_case							:				Union[str, Any]	) ->      Any:



						'''simple docstring'''

						return int(x / 2**20	)






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __enter__(					self     )->				Optional[int]:





													'''simple docstring'''






													gc.collect()
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
													__UpperCamelCase       					=     torch.cuda.memory_allocated()
													__UpperCamelCase       					=     psutil.Process()

													__UpperCamelCase       					=     self.cpu_mem_used()
													__UpperCamelCase       					=     True
													__UpperCamelCase       					=     threading.Thread(target=self.peak_monitor_func     )
													__UpperCamelCase       					=     True
													peak_monitor_thread.start()
													return self



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													return self.process.memory_info().rss



							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     -1

													while True:
																			__UpperCamelCase       					=     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       ,		*SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     False

													gc.collect()
													torch.cuda.empty_cache()
													__UpperCamelCase       					=     torch.cuda.memory_allocated()
													__UpperCamelCase       					=     torch.cuda.max_memory_allocated()
													__UpperCamelCase       					=     bamb(self.end - self.begin     )
													__UpperCamelCase       					=     bamb(self.peak - self.begin     )

													__UpperCamelCase       					=     self.cpu_mem_used()
													__UpperCamelCase       					=     bamb(self.cpu_end - self.cpu_begin     )
													__UpperCamelCase       					=     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

				lowercase__							:							Union[str, Any]													=     mocked_dataloaders  # noqa: F811




def 		A_	(							snake_case							:				Dict	, snake_case							:				List[str]	) ->      Any:



						'''simple docstring'''

						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	, snake_case	) == "1":
												__UpperCamelCase       					=     2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						__UpperCamelCase       					=     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:
												__UpperCamelCase       					=     Accelerator(
												    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with='''wandb'''	, project_dir=args.logging_dir	, fsdp_plugin=snake_case	, )
						else:
												__UpperCamelCase       					=     Accelerator(fsdp_plugin=snake_case	)
						accelerator.print(accelerator.distributed_type	)

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

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												__UpperCamelCase       					=     vars(snake_case	)
												accelerator.init_trackers('''fsdp_glue_no_trainer'''	, snake_case	)

						__UpperCamelCase       					=     AutoTokenizer.from_pretrained(args.model_name_or_path	)
						__UpperCamelCase       					=     load_dataset('''glue'''	, '''mrpc'''	)
						__UpperCamelCase       					=     evaluate.load('''glue'''	, '''mrpc'''	)

						def tokenize_function(snake_case							:				Optional[int]	):
												# max_length=None => use the model max length (it's actually the default)
												__UpperCamelCase       					=     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():
												__UpperCamelCase       					=     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
						__UpperCamelCase       					=     tokenized_datasets.rename_column('''label'''	, '''labels'''	)

						# If the batch size is too big we use gradient accumulation
						__UpperCamelCase       					=     1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__UpperCamelCase       					=     batch_size // MAX_GPU_BATCH_SIZE
												__UpperCamelCase       					=     MAX_GPU_BATCH_SIZE

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

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

						# Instantiate dataloaders.
						__UpperCamelCase       					=     DataLoader(
						    tokenized_datasets['''train''']	, shuffle=snake_case	, collate_fn=snake_case	, batch_size=snake_case	)
						__UpperCamelCase       					=     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)
						__UpperCamelCase       					=     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
						__UpperCamelCase       					=     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
						__UpperCamelCase       					=     torch.optim.AdamW(params=model.parameters()	, lr=snake_case	, weight_decay=2e-4	)

						# Instantiate scheduler
						__UpperCamelCase       					=     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.
						__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase       					=     accelerator.prepare(
						    snake_case	, snake_case	, snake_case	, snake_case	)

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

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('''epoch_'''	, ''''''	)	)
																		__UpperCamelCase       					=     None
												else:
																		__UpperCamelCase       					=     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
																		__UpperCamelCase       					=     (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:
																								__UpperCamelCase       					=     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	)
																								__UpperCamelCase       					=     model(**snake_case	)
																								__UpperCamelCase       					=     outputs.loss
																								__UpperCamelCase       					=     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	):
																														__UpperCamelCase       					=     f"step_{overall_step}"
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										__UpperCamelCase       					=     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():
																														__UpperCamelCase       					=     model(**snake_case	)
																								__UpperCamelCase       					=     outputs.logits.argmax(dim=-1	)
																								__UpperCamelCase					,							__UpperCamelCase       					=     accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
																								metric.add_batch(
																								    predictions=snake_case	, references=snake_case	, )

																		__UpperCamelCase       					=     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":
																								__UpperCamelCase       					=     f"epoch_{epoch}"
																								if args.output_dir is not None:
																														__UpperCamelCase       					=     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 		A_	(							) ->      str:



						'''simple docstring'''

						__UpperCamelCase       					=     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	, )
						__UpperCamelCase       					=     parser.parse_args()
						__UpperCamelCase       					=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(snake_case	, snake_case	)


if __name__ == "__main__":
				main()






from typing import Any

import numpy as np




def 		A_	(							snake_case							:				np.ndarray	) ->      bool:



						'''simple docstring'''

						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[1]

						# If matrix not symmetric, exit right away.
						if np.allclose(snake_case	, matrix.T	) is False:
												return False

						# Get eigenvalues and eignevectors for a symmetric matrix.
						__UpperCamelCase					,							__UpperCamelCase       					=     np.linalg.eigh(snake_case	)

						# Check sign of all eigenvalues.
						# np.all returns a value of type np.bool_
						return bool(np.all(eigen_values > 0	)	)




def 		A_	(							snake_case							:				int	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     np.random.randn(snake_case	, snake_case	)
						__UpperCamelCase       					=     np.dot(snake_case	, random_matrix.T	)
						assert _is_matrix_spd(snake_case	)
						return spd_matrix




def 		A_	(							snake_case							:				np.ndarray	, snake_case							:				np.ndarray	, snake_case							:				int = 1000	, snake_case							:				float = 1e-8	, ) ->      Any:



						'''simple docstring'''

						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[1]
						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[0]
						assert _is_matrix_spd(snake_case	)

						# Initialize solution guess, residual, search direction.
						__UpperCamelCase       					=     np.zeros((np.shape(snake_case	)[0], 1)	)
						__UpperCamelCase       					=     np.copy(snake_case	)
						__UpperCamelCase       					=     np.copy(snake_case	)

						# Set initial errors in solution guess and residual.
						__UpperCamelCase       					=     1e9
						__UpperCamelCase       					=     1e9
						__UpperCamelCase       					=     1e9

						# Set iteration counter to threshold number of iterations.
						__UpperCamelCase       					=     0

						while error > tol:
												# Save this value so we only calculate the matrix-vector product once.
												__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

												# The main algorithm.

												# Update search direction magnitude.
												__UpperCamelCase       					=     np.dot(ra.T	, snake_case	) / np.dot(pa.T	, snake_case	)
												# Update solution guess.
												__UpperCamelCase       					=     xa + alpha * pa
												# Calculate new residual.
												__UpperCamelCase       					=     ra - alpha * w
												# Calculate new Krylov subspace scale.
												__UpperCamelCase       					=     np.dot(r.T	, snake_case	) / np.dot(ra.T	, snake_case	)
												# Calculate new A conjuage search direction.
												__UpperCamelCase       					=     r + beta * pa

												# Calculate errors.
												__UpperCamelCase       					=     np.linalg.norm(r - ra	)
												__UpperCamelCase       					=     np.linalg.norm(x - xa	)
												__UpperCamelCase       					=     np.maximum(snake_case	, snake_case	)

												# Update variables.
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)

												# Update number of iterations.
												iterations += 1
												if iterations > max_iterations:
																		break

						return x




def 		A_	(							) ->      None:



						'''simple docstring'''

						__UpperCamelCase       					=     3
						__UpperCamelCase       					=     _create_spd_matrix(snake_case	)
						__UpperCamelCase       					=     np.random.randn(snake_case	, 1	)
						__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

						# Numpy solution.
						__UpperCamelCase       					=     np.linalg.solve(snake_case	, snake_case	)

						# Our implementation.
						__UpperCamelCase       					=     conjugate_gradient(snake_case	, snake_case	)

						# Ensure both solutions are close to x_true (and therefore one another).
						assert np.linalg.norm(x_numpy - x_true	) <= 1e-6
						assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1e-6


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				test_conjugate_gradient()




