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         =		16
_UpperCamelCase         =		32

def    _A(		lowerCAmelCase       ):
							return int(x / 2**20       )
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __enter__( self       ):







										'''simple docstring'''


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

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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self.process.memory_info().rss





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= -1

										while True:
																	A__					:    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      ,			*snake_case_       ):







										'''simple docstring'''


										A__					:    str														= False

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

										A__					:    List[Any]														= self.cpu_mem_used()
										A__					:    Tuple														= bamb(self.cpu_end - self.cpu_begin       )
										A__					:    List[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         =		mocked_dataloaders  # noqa: F811

def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,     lowercase__       ) == "1":
														A__					:    Any														= 2

							# New Code #
							# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
							A__					:    Optional[int]														= FullyShardedDataParallelPlugin(
							    state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase__ ,     ranka_only=lowercase__       ) ,     optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase__ ,     ranka_only=lowercase__       ) ,     )

							# Initialize accelerator
							if args.with_tracking:
														A__					:    int														= Accelerator(
														    cpu=args.cpu ,     mixed_precision=args.mixed_precision ,     log_with="""wandb""" ,     project_dir=args.logging_dir ,     fsdp_plugin=lowercase__ ,     )
							else:
														A__					:    int														= Accelerator(fsdp_plugin=lowercase__       )
							accelerator.print(accelerator.distributed_type       )

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

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														A__					:    int														= vars(lowercase__       )
														accelerator.init_trackers("""fsdp_glue_no_trainer""" ,     lowercase__       )

							A__					:    Optional[Any]														= AutoTokenizer.from_pretrained(args.model_name_or_path       )
							A__					:    Union[str, Any]														= load_dataset("""glue""" ,     """mrpc"""       )
							A__					:    Dict														= evaluate.load("""glue""" ,     """mrpc"""       )

							def tokenize_function(lowerCAmelCase       ):
														# max_length=None => use the model max length (it's actually the default)
														A__					:    Optional[int]														= tokenizer(examples["""sentence1"""] ,     examples["""sentence2"""] ,     truncation=lowercase__ ,     max_length=lowercase__       )
														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__					:    Union[str, Any]														= datasets.map(
														    lowercase__ ,     batched=lowercase__ ,     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__					:    int														= tokenized_datasets.rename_column("""label""" ,     """labels"""       )

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

							def collate_fn(lowerCAmelCase       ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														A__					:    Optional[int]														= 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__					:    Optional[Any]														= 16
														elif accelerator.mixed_precision != "no":
																					A__					:    Optional[Any]														= 8
														else:
																					A__					:    Optional[int]														= None

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

							# Instantiate dataloaders.
							A__					:    Optional[int]														= DataLoader(
							    tokenized_datasets["""train"""] ,     shuffle=lowercase__ ,     collate_fn=lowercase__ ,     batch_size=lowercase__       )
							A__					:    List[str]														= DataLoader(
							    tokenized_datasets["""validation"""] ,     shuffle=lowercase__ ,     collate_fn=lowercase__ ,     batch_size=lowercase__       )

							set_seed(lowercase__       )

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

							# New Code #
							# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
							A__					:    Tuple														= accelerator.prepare(lowercase__       )
							accelerator.print(lowercase__       )

							# 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
							A__					:    List[Any]														= torch.optim.AdamW(params=model.parameters() ,     lr=lowercase__ ,     weight_decay=2E-4       )

							# Instantiate scheduler
							A__					:    Union[str, Any]														= get_linear_schedule_with_warmup(
							    optimizer=lowercase__ ,     num_warmup_steps=10 ,     num_training_steps=(len(lowercase__       ) * 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.
							A__							,       A__							,       A__							,       A__					:    Tuple														= accelerator.prepare(
							    lowercase__ ,     lowercase__ ,     lowercase__ ,     lowercase__       )

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

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

    # Now we train the model
							for epoch in range(lowercase__       ):
														# New Code #
														# context manager to track the peak memory usage during the training epoch
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					if args.with_tracking:
																												A__					:    List[str]														= 0
																					for step, batch in enumerate(lowercase__       ):
																												# 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       )
																												A__					:    Optional[Any]														= model(**lowercase__       )
																												A__					:    List[Any]														= outputs.loss
																												A__					:    Optional[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(lowercase__       )
																												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(lowercase__ ,     lowercase__       ):
																																			A__					:    int														= F'''step_{overall_step}'''
																																			if overall_step % checkpointing_steps == 0:
																																										if args.output_dir is not None:
																																																	A__					:    Any														= os.path.join(args.output_dir ,     lowercase__       )
																																										accelerator.save_state(lowercase__       )
        # 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=lowercase__ ,     )

														# New Code #
														# context manager to track the peak memory usage during the evaluation
														with TorchTracemalloc() as tracemalloc:
																					model.eval()
																					for step, batch in enumerate(lowercase__       ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device       )
																												with torch.no_grad():
																																			A__					:    Optional[Any]														= model(**lowercase__       )
																												A__					:    Tuple														= outputs.logits.argmax(dim=-1       )
																												A__							,       A__					:    Union[str, Any]														= accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
																												metric.add_batch(
																												    predictions=lowercase__ ,     references=lowercase__ ,     )

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

																					if checkpointing_steps == "epoch":
																												A__					:    int														= F'''epoch_{epoch}'''
																												if args.output_dir is not None:
																																			A__					:    Any														= os.path.join(args.output_dir ,     lowercase__       )
																												accelerator.save_state(lowercase__       )
        # 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=lowercase__ ,     )

							if args.with_tracking:
														accelerator.end_training()

def    _A(		):
							A__					:    Dict														= argparse.ArgumentParser(description="""Simple example of training script."""       )
							parser.add_argument(
							    """--mixed_precision""" ,     type=lowercase__ ,     default=lowercase__ ,     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=lowercase__ ,     default=lowercase__ ,     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=lowercase__ ,     default=lowercase__ ,     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=lowercase__ ,     default=""".""" ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,     )
							parser.add_argument(
							    """--logging_dir""" ,     type=lowercase__ ,     default="""logs""" ,     help="""Location on where to store experiment tracking logs`""" ,     )
							parser.add_argument(
							    """--model_name_or_path""" ,     type=lowercase__ ,     help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,     required=lowercase__ ,     )
							A__					:    List[str]														= parser.parse_args()
							A__					:    List[Any]														= {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(lowercase__ ,     lowercase__       )


if __name__ == "__main__":
				main()






"""simple docstring"""



from dataclasses import dataclass

_UpperCamelCase         =		"0.1"
_UpperCamelCase         =		"Lucia Harcekova"
@dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			_UpperCamelCase     :	int
			_UpperCamelCase     :	int
			_UpperCamelCase     :	str





			def __repr__( self       ):







										'''simple docstring'''


										return F'''({self.offset}, {self.length}, {self.indicator})'''





class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = 13      ,			snake_case_ = 6       ):







										'''simple docstring'''


										A__					:    str														= window_size
										A__					:    Optional[int]														= lookahead_buffer_size
										A__					:    Optional[Any]														= self.window_size - self.lookahead_buffer_size





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    str														= []
										A__					:    Any														= """"""

										# while there are still characters in text to compress
										while text:
																	# find the next encoding phrase
																	# - triplet with offset, length, indicator (the next encoding character)
																	A__					:    Optional[Any]														= self._find_encoding_token(snake_case_      ,			snake_case_       )

																	# update the search buffer:
																	# - add new characters from text into it
																	# - check if size exceed the max search buffer size, if so, drop the
																	#   oldest elements
																	search_buffer += text[: token.length + 1]
																	if len(snake_case_       ) > self.search_buffer_size:
																								A__					:    Any														= search_buffer[-self.search_buffer_size :]

																	# update the text
																	A__					:    Optional[Any]														= text[token.length + 1 :]

																	# append the token to output
																	output.append(snake_case_       )

										return output





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= """"""

										for token in tokens:
																	for _ in range(token.length       ):
																								output += output[-token.offset]
																	output += token.indicator

										return output





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not text:
																	raise ValueError("""We need some text to work with."""       )

										# Initialise result parameters to default values
										A__							,       A__					:    List[Any]														= 0, 0

										if not search_buffer:
																	return Token(snake_case_      ,			snake_case_      ,			text[length]       )

										for i, character in enumerate(snake_case_       ):
																	A__					:    Tuple														= len(snake_case_       ) - i
																	if character == text[0]:
																								A__					:    Tuple														= self._match_length_from_index(snake_case_      ,			snake_case_      ,			0      ,			snake_case_       )
																								# if the found length is bigger than the current or if it's equal,
																								# which means it's offset is smaller: update offset and length
																								if found_length >= length:
																															A__							,       A__					:    List[str]														= found_offset, found_length

										return Token(snake_case_      ,			snake_case_      ,			text[length]       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not text or text[text_index] != window[window_index]:
																	return 0
										return 1 + self._match_length_from_index(
										    snake_case_      ,			window + text[text_index]      ,			text_index + 1      ,			window_index + 1       )


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				# Initialize compressor class
				_UpperCamelCase         =		LZaaCompressor(window_size=13, lookahead_buffer_size=6)

				# Example
				_UpperCamelCase         =		"cabracadabrarrarrad"
				_UpperCamelCase         =		lzaa_compressor.compress(TEXT)
				print(lzaa_compressor.compress("ababcbababaa"))
				_UpperCamelCase         =		lzaa_compressor.decompress(compressed_text)
				assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."





