





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


snake_case_				=			16
snake_case_				=			32






def lowerCamelCase__    (							snake_case_	:  Any       )      ->				Dict:
   return int(x / 2**20       )

class 		SCREAMING_SNAKE_CASE__				:


      def __enter__(self       :      Any      ):





         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         __snake_case							       =   torch.cuda.memory_allocated()
         return self


      def __exit__(self       :      List[str]				,			*a__       :      str      ):





         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         __snake_case							       =   torch.cuda.memory_allocated()
         __snake_case							       =   torch.cuda.max_memory_allocated()
         __snake_case							       =   bamb(self.end - self.begin      )
         __snake_case							       =   bamb(self.peak - self.begin      )
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  Dict = 16       ,      snake_case_	:  List[Any] = "bert-base-cased"       ,      snake_case_	:  Any = 320       ,      snake_case_	:  int = 160       ,      )      ->				Tuple:
   __snake_case							       =   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
   __snake_case							       =   load_dataset(
       '''glue'''       ,      '''mrpc'''       ,      split={'''train''': f"""train[:{n_train}]""", '''validation''': f"""validation[:{n_val}]"""}       )

   def tokenize_function(snake_case_	:  int       ):
      # max_length=None => use the model max length (it's actually the default)
      __snake_case							       =   tokenizer(examples['''sentence1''']       ,      examples['''sentence2''']       ,      truncation=SCREAMING_SNAKE_CASE__       ,      max_length=SCREAMING_SNAKE_CASE__       )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __snake_case							       =   datasets.map(
       SCREAMING_SNAKE_CASE__       ,      batched=SCREAMING_SNAKE_CASE__       ,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,      load_from_cache_file=SCREAMING_SNAKE_CASE__       )

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

   def collate_fn(snake_case_	:  List[Any]       ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(SCREAMING_SNAKE_CASE__       ,      padding='''max_length'''       ,      max_length=128       ,      return_tensors='''pt'''       )
      return tokenizer.pad(SCREAMING_SNAKE_CASE__       ,      padding='''longest'''       ,      return_tensors='''pt'''       )

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

   return train_dataloader, eval_dataloader






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  List[Any]       )      ->				Dict:
   # Initialize accelerator
   __snake_case							       =   Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __snake_case							       =   config['''lr''']
   __snake_case							       =   int(config['''num_epochs''']       )
   __snake_case							       =   int(config['''seed''']       )
   __snake_case							       =   int(config['''batch_size''']       )
   __snake_case							       =   args.model_name_or_path

   set_seed(SCREAMING_SNAKE_CASE__       )
   __snake_case       ,	__snake_case							       =   get_dataloaders(SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       ,      args.n_train       ,      args.n_val       )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __snake_case							       =   AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__       ,      return_dict=SCREAMING_SNAKE_CASE__       )

   # Instantiate optimizer
   __snake_case							       =   (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __snake_case							       =   optimizer_cls(params=model.parameters()       ,      lr=SCREAMING_SNAKE_CASE__       )

   if accelerator.state.deepspeed_plugin is not None:
      __snake_case							       =   accelerator.state.deepspeed_plugin.deepspeed_config[
          '''gradient_accumulation_steps'''
      ]
   else:
      __snake_case							       =   1
   __snake_case							       =   (len(SCREAMING_SNAKE_CASE__       ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      __snake_case							       =   get_linear_schedule_with_warmup(
          optimizer=SCREAMING_SNAKE_CASE__       ,      num_warmup_steps=0       ,      num_training_steps=SCREAMING_SNAKE_CASE__       ,      )
   else:
      __snake_case							       =   DummyScheduler(SCREAMING_SNAKE_CASE__       ,      total_num_steps=SCREAMING_SNAKE_CASE__       ,      warmup_num_steps=0       )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   accelerator.prepare(
       SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       )

   # We need to keep track of how many total steps we have iterated over
   __snake_case							       =   0
   # We also need to keep track of the stating epoch so files are named properly
   __snake_case							       =   0

   # Now we train the model
   __snake_case							       =   {}
   for epoch in range(SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       ):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
            __snake_case							       =   model(**SCREAMING_SNAKE_CASE__       )
            __snake_case							       =   outputs.loss
            __snake_case							       =   loss / gradient_accumulation_steps
            accelerator.backward(SCREAMING_SNAKE_CASE__       )
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

            overall_step += 1

        # 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       )       )       )
      __snake_case							       =   tracemalloc.peaked + bamb(tracemalloc.begin       )
      if args.peak_memory_upper_bound is not None:
         assert (
             train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
         ), "Peak memory usage exceeded the upper bound"

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir       ,      '''peak_memory_utilization.json'''       )       ,      '''w'''       ) as f:
         json.dump(SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       )






def lowerCamelCase__    (							)      ->				Optional[Any]:
   __snake_case							       =   argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
   parser.add_argument(
       '''--model_name_or_path'''       ,      type=SCREAMING_SNAKE_CASE__       ,      default='''bert-base-cased'''       ,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,      required=SCREAMING_SNAKE_CASE__       ,      )
   parser.add_argument(
       '''--output_dir'''       ,      type=SCREAMING_SNAKE_CASE__       ,      default='''.'''       ,      help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,      )
   parser.add_argument(
       '''--peak_memory_upper_bound'''       ,      type=SCREAMING_SNAKE_CASE__       ,      default=SCREAMING_SNAKE_CASE__       ,      help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''       ,      )
   parser.add_argument(
       '''--n_train'''       ,      type=SCREAMING_SNAKE_CASE__       ,      default=320       ,      help='''Number of training examples to use.'''       ,      )
   parser.add_argument(
       '''--n_val'''       ,      type=SCREAMING_SNAKE_CASE__       ,      default=160       ,      help='''Number of validation examples to use.'''       ,      )
   parser.add_argument(
       '''--num_epochs'''       ,      type=SCREAMING_SNAKE_CASE__       ,      default=1       ,      help='''Number of train epochs.'''       ,      )
   __snake_case							       =   parser.parse_args()
   __snake_case							       =   {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
   training_function(SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
    main()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XGLMForCausalLM''',
					    '''XGLMModel''',
					    '''XGLMPreTrainedModel''',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxXGLMForCausalLM''',
					    '''FlaxXGLMModel''',
					    '''FlaxXGLMPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXGLMForCausalLM''',
					    '''TFXGLMModel''',
					    '''TFXGLMPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm import XGLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm_fast import XGLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xglm import (
										    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXGLMForCausalLM,
										    TFXGLMModel,
										    TFXGLMPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)