

"""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
#
########################################################################


a__			:						str							=			16
a__			:						List[str]							=			32





def 					__UpperCamelCase (     a__:			Tuple      )					->      str:




					"""simple docstring"""



					return int(x / 2**20      )


class       _snake_case  :







						def __enter__(self				)      ->    Any:
											gc.collect()
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
											__lowercase        =      torch.cuda.memory_allocated()
											__lowercase        =      psutil.Process()

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







						def 							_UpperCamelCase     (self				)      ->    Any:
											return self.process.memory_info().rss







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      -1

											while True:
																__lowercase        =      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_				)      ->    Optional[Any]:
											__lowercase        =      False

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

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

							a__			:						Optional[int]							=			mocked_dataloaders  # noqa: F811





def 					__UpperCamelCase (     a__:			List[str]			,						a__:			List[Any]      )					->      Tuple:




					"""simple docstring"""



					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,						a__      ) == "1":
										__lowercase        =      2

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

					# Initialize accelerator
					if args.with_tracking:
										__lowercase        =      Accelerator(
										    cpu=args.cpu			,						mixed_precision=args.mixed_precision			,						log_with='''wandb'''			,						project_dir=args.logging_dir			,						fsdp_plugin=a__			,						)
					else:
										__lowercase        =      Accelerator(fsdp_plugin=a__      )
					accelerator.print(accelerator.distributed_type      )

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

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

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

					def tokenize_function(a__:			List[Any]      ):
										# max_length=None => use the model max length (it's actually the default)
										__lowercase        =      tokenizer(examples['''sentence1''']			,						examples['''sentence2''']			,						truncation=a__			,						max_length=a__      )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										__lowercase        =      datasets.map(
										    a__			,						batched=a__			,						remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,						)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__lowercase        =      tokenized_datasets.rename_column('''label'''			,						'''labels'''      )

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

					def collate_fn(a__:			Tuple      ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__lowercase        =      1_28 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        =      16
										elif accelerator.mixed_precision != "no":
															__lowercase        =      8
										else:
															__lowercase        =      None

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

					# Instantiate dataloaders.
					__lowercase        =      DataLoader(
					    tokenized_datasets['''train''']			,						shuffle=a__			,						collate_fn=a__			,						batch_size=a__      )
					__lowercase        =      DataLoader(
					    tokenized_datasets['''validation''']			,						shuffle=a__			,						collate_fn=a__			,						batch_size=a__      )

					set_seed(a__      )

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

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

					# 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        =      torch.optim.AdamW(params=model.parameters()			,						lr=a__			,						weight_decay=2e-4      )

					# Instantiate scheduler
					__lowercase        =      get_linear_schedule_with_warmup(
					    optimizer=a__			,						num_warmup_steps=10			,						num_training_steps=(len(a__      ) * 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        =      accelerator.prepare(
					    a__			,						a__			,						a__			,						a__      )

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

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

    # Now we train the model
					for epoch in range(a__      ):
										# 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        =      0
															for step, batch in enumerate(a__      ):
																				# 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        =      model(**a__      )
																				__lowercase        =      outputs.loss
																				__lowercase        =      loss / gradient_accumulation_steps
																				# We keep track of the loss at each epoch
																				if args.with_tracking:
																									total_loss += loss.detach().float()
																				accelerator.backward(a__      )
																				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(a__			,						a__      ):
																									__lowercase        =      f'''step_{overall_step}'''
																									if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																			__lowercase        =      os.path.join(args.output_dir			,						a__      )
																														accelerator.save_state(a__      )
        # 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=a__			,						)

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

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

															if checkpointing_steps == "epoch":
																				__lowercase        =      f'''epoch_{epoch}'''
																				if args.output_dir is not None:
																									__lowercase        =      os.path.join(args.output_dir			,						a__      )
																				accelerator.save_state(a__      )
        # 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=a__			,						)

					if args.with_tracking:
										accelerator.end_training()





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




					"""simple docstring"""



					__lowercase        =      argparse.ArgumentParser(description='''Simple example of training script.'''      )
					parser.add_argument(
					    '''--mixed_precision'''			,						type=a__			,						default=a__			,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,						help='''Whether to use mixed precision. Choose'''
					    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
					    '''and an Nvidia Ampere GPU.'''			,						)
					parser.add_argument('''--cpu'''			,						action='''store_true'''			,						help='''If passed, will train on the CPU.'''      )
					parser.add_argument(
					    '''--checkpointing_steps'''			,						type=a__			,						default=a__			,						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=a__			,						default=a__			,						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=a__			,						default='''.'''			,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,						)
					parser.add_argument(
					    '''--logging_dir'''			,						type=a__			,						default='''logs'''			,						help='''Location on where to store experiment tracking logs`'''			,						)
					parser.add_argument(
					    '''--model_name_or_path'''			,						type=a__			,						help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,						required=a__			,						)
					__lowercase        =      parser.parse_args()
					__lowercase        =      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(a__			,						a__      )


if __name__ == "__main__":
							main()





"""simple docstring"""

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a__			:						Dict							=			logging.get_logger(__name__)

a__			:						Any							=			{
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}


class       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  'longt5'
						__snake_case			      =  ['past_key_values']
						__snake_case			      =  {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(self  ,				UpperCAmelCase_=3_2_1_2_8  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=6_4  ,				UpperCAmelCase_=2_0_4_8  ,				UpperCAmelCase_=6  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=8  ,				UpperCAmelCase_=1_2_7  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=1_2_8  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=1e-6  ,				UpperCAmelCase_=1.0  ,				UpperCAmelCase_="relu"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_="local"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=1  ,				**UpperCAmelCase_  ,				)      ->    int:
											__lowercase        =      vocab_size
											__lowercase        =      d_model
											__lowercase        =      d_kv
											__lowercase        =      d_ff
											__lowercase        =      num_layers
											# default = symmetry
											__lowercase        =      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__lowercase        =      num_heads
											__lowercase        =      local_radius
											__lowercase        =      global_block_size
											__lowercase        =      relative_attention_num_buckets
											__lowercase        =      relative_attention_max_distance
											__lowercase        =      dropout_rate
											__lowercase        =      layer_norm_epsilon
											__lowercase        =      initializer_factor
											__lowercase        =      feed_forward_proj
											__lowercase        =      encoder_attention_type
											__lowercase        =      use_cache

											__lowercase        =      self.feed_forward_proj.split('''-'''				)
											__lowercase        =      act_info[-1]
											__lowercase        =      act_info[0] == '''gated'''

											if len(UpperCAmelCase_				) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_				) > 2:
																raise ValueError(
																    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																    '''\'gated-gelu\' or \'relu\''''				)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																__lowercase        =      '''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_  ,				eos_token_id=UpperCAmelCase_  ,				is_encoder_decoder=UpperCAmelCase_  ,				**UpperCAmelCase_  ,				)



class       _snake_case  (    UpperCamelCase							):







						@property
						def 							_UpperCamelCase     (self				)      ->    Mapping[str, Mapping[int, str]]:
											__lowercase        =      {
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																__lowercase        =      '''past_encoder_sequence + sequence'''
																__lowercase        =      {0: '''batch'''}
																__lowercase        =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
																self.fill_with_past_key_values_(UpperCAmelCase_  ,				direction='''inputs'''				)

											return common_inputs







						@property
						def 							_UpperCamelCase     (self				)      ->    int:
											return 1_3


