'''simple docstring'''




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


_lowercase						: Dict						=						16
_lowercase						: List[Any]						=						32




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      Any:
							return int(x / 2**20 )





class     __magic_name__						:







			def __enter__(	self				:  Union[str, Any]    ):
										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()
										return self







			def __exit__(	self				:  List[Any]				,			*lowercase_				:  int    ):
										gc.collect()
										torch.cuda.empty_cache()
										lowercase_					:			Dict										=       torch.cuda.memory_allocated()
										lowercase_					:			List[Any]										=       torch.cuda.max_memory_allocated()
										lowercase_					:			Tuple										=       bamb(self.end - self.begin    )
										lowercase_					:			Tuple										=       bamb(self.peak - self.begin    )
										# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int = 16      ,      UpperCAmelCase__       :					str = "bert-base-cased"      ,      UpperCAmelCase__       :					int = 320      ,      UpperCAmelCase__       :					int = 160      ,      )			->      Union[str, Any]:
							lowercase_					:			int										=       AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
							lowercase_					:			List[Any]										=       load_dataset(
							    """glue"""      ,      """mrpc"""      ,      split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''} )

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

							def collate_fn(UpperCAmelCase__       :					Tuple ):
														# 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.
							lowercase_					:			int										=       DataLoader(
							    tokenized_datasets["""train"""]      ,      shuffle=_SCREAMING_SNAKE_CASE      ,      collate_fn=_SCREAMING_SNAKE_CASE      ,      batch_size=_SCREAMING_SNAKE_CASE )
							lowercase_					:			Tuple										=       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	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      Dict:
							# Initialize accelerator
							lowercase_					:			str										=       Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowercase_					:			Dict										=       config['lr']
							lowercase_					:			str										=       int(config["""num_epochs"""] )
							lowercase_					:			Optional[int]										=       int(config["""seed"""] )
							lowercase_					:			Any										=       int(config["""batch_size"""] )
							lowercase_					:			List[str]										=       args.model_name_or_path

							set_seed(_SCREAMING_SNAKE_CASE )
							lowercase_					:			int										=       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)
							lowercase_					:			Optional[int]										=       AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE      ,      return_dict=_SCREAMING_SNAKE_CASE )

							# Instantiate optimizer
							lowercase_					:			Optional[Any]										=       (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							lowercase_					:			Optional[Any]										=       optimizer_cls(params=model.parameters()      ,      lr=_SCREAMING_SNAKE_CASE )

							if accelerator.state.deepspeed_plugin is not None:
														lowercase_					:			int										=       accelerator.state.deepspeed_plugin.deepspeed_config[
														    'gradient_accumulation_steps'
														]
							else:
														lowercase_					:			Union[str, Any]										=       1
							lowercase_					:			Tuple										=       (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
							):
														lowercase_					:			str										=       get_linear_schedule_with_warmup(
														    optimizer=_SCREAMING_SNAKE_CASE      ,      num_warmup_steps=0      ,      num_training_steps=_SCREAMING_SNAKE_CASE      ,      )
							else:
														lowercase_					:			List[Any]										=       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.
							lowercase_					:			Optional[Any]										=       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
							lowercase_					:			Union[str, Any]										=       0
							# We also need to keep track of the stating epoch so files are named properly
							lowercase_					:			Dict										=       0

							# Now we train the model
							lowercase_					:			str										=       {}
							for epoch in range(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
																												lowercase_					:			List[Any]										=       model(**_SCREAMING_SNAKE_CASE )
																												lowercase_					:			str										=       outputs.loss
																												lowercase_					:			str										=       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 ) ) )
														lowercase_					:			List[Any]										=       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	(      )			->      List[Any]:
							lowercase_					:			int										=       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."""      ,      )
							lowercase_					:			List[str]										=       parser.parse_args()
							lowercase_					:			List[Any]										=       {'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()









'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase	:						Union[str, Any]       =	{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Union[str, Any]       =	[
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Dict       =	[
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						str       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



