






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


lowerCamelCase : Optional[int]						   =       16
lowerCamelCase : Union[str, Any]						   =       32
def       snake_case_		(     lowerCAmelCase_       :						Any      ):
							return int(x / 2**20      )

class 		lowerCAmelCase     :

					'''simple docstring'''

					def __enter__(  self				:	Any							)			->  Tuple:


												"""simple docstring"""

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



					def __exit__(  self				:	List[str]		,     *__a				:	Any							)			->  Union[str, Any]:


												"""simple docstring"""

												gc.collect()
												torch.cuda.empty_cache()
												__lowercase      :							List[str]				=     torch.cuda.memory_allocated()
												__lowercase      :							str				=     torch.cuda.max_memory_allocated()
												__lowercase      :							Any				=     bamb(self.end - self.begin							)
												__lowercase      :							Dict				=     bamb(self.peak - self.begin							)
												# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def       snake_case_		(     lowerCAmelCase_       :						Accelerator       ,       lowerCAmelCase_       :						int = 16       ,       lowerCAmelCase_       :						str = "bert-base-cased"       ,       lowerCAmelCase_       :						int = 320       ,       lowerCAmelCase_       :						int = 160       ,       ):
							__lowercase      :							int				=     AutoTokenizer.from_pretrained(lowerCAmelCase_      )
							__lowercase      :							Any				=     load_dataset(
							    """glue"""       ,       """mrpc"""       ,       split={"""train""": F"train[:{n_train}]", """validation""": F"validation[:{n_val}]"}      )

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

							# Apply the method we just defined to all the examples in all the splits of the dataset
							__lowercase      :							int				=     datasets.map(
							    lowerCAmelCase_       ,       batched=lowerCAmelCase_       ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,       load_from_cache_file=lowerCAmelCase_      )

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

							def collate_fn(lowerCAmelCase_       :						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(lowerCAmelCase_       ,       padding="""max_length"""       ,       max_length=128       ,       return_tensors="""pt"""      )
														return tokenizer.pad(lowerCAmelCase_       ,       padding="""longest"""       ,       return_tensors="""pt"""      )

							# Instantiate dataloaders.
							__lowercase      :							List[str]				=     DataLoader(
							    tokenized_datasets["""train"""]       ,       shuffle=lowerCAmelCase_       ,       collate_fn=lowerCAmelCase_       ,       batch_size=lowerCAmelCase_      )
							__lowercase      :							Dict				=     DataLoader(
							    tokenized_datasets["""validation"""]       ,       shuffle=lowerCAmelCase_       ,       collate_fn=lowerCAmelCase_       ,       batch_size=lowerCAmelCase_      )

							return train_dataloader, eval_dataloader
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						Optional[Any]      ):
							# Initialize accelerator
							__lowercase      :							Union[str, Any]				=     Accelerator()

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

							set_seed(lowerCAmelCase_      )
							__lowercase			,			__lowercase      :							Any				=     get_dataloaders(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_       ,       args.n_train       ,       args.n_val      )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowercase      :							List[Any]				=     AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_       ,       return_dict=lowerCAmelCase_      )

							# Instantiate optimizer
							__lowercase      :							int				=     (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							__lowercase      :							Tuple				=     optimizer_cls(params=model.parameters()       ,       lr=lowerCAmelCase_      )

							if accelerator.state.deepspeed_plugin is not None:
														__lowercase      :							Tuple				=     accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														__lowercase      :							Union[str, Any]				=     1
							__lowercase      :							Optional[Any]				=     (len(lowerCAmelCase_      ) * 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      :							Union[str, Any]				=     get_linear_schedule_with_warmup(
														    optimizer=lowerCAmelCase_       ,       num_warmup_steps=0       ,       num_training_steps=lowerCAmelCase_       ,       )
							else:
														__lowercase      :							Dict				=     DummyScheduler(lowerCAmelCase_       ,       total_num_steps=lowerCAmelCase_       ,       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			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Optional[Any]				=     accelerator.prepare(
							    lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

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

							# Now we train the model
							__lowercase      :							List[Any]				=     {}
							for epoch in range(lowerCAmelCase_       ,       lowerCAmelCase_      ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(lowerCAmelCase_      ):
																												__lowercase      :							str				=     model(**lowerCAmelCase_      )
																												__lowercase      :							Optional[int]				=     outputs.loss
																												__lowercase      :							Tuple				=     loss / gradient_accumulation_steps
																												accelerator.backward(lowerCAmelCase_      )
																												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      :							int				=     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(lowerCAmelCase_       ,       lowerCAmelCase_      )
def       snake_case_		(     ):
							__lowercase      :							Dict				=     argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""      )
							parser.add_argument(
							    """--model_name_or_path"""       ,       type=lowerCAmelCase_       ,       default="""bert-base-cased"""       ,       help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,       required=lowerCAmelCase_       ,       )
							parser.add_argument(
							    """--output_dir"""       ,       type=lowerCAmelCase_       ,       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=lowerCAmelCase_       ,       default=lowerCAmelCase_       ,       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=lowerCAmelCase_       ,       default=320       ,       help="""Number of training examples to use."""       ,       )
							parser.add_argument(
							    """--n_val"""       ,       type=lowerCAmelCase_       ,       default=160       ,       help="""Number of validation examples to use."""       ,       )
							parser.add_argument(
							    """--num_epochs"""       ,       type=lowerCAmelCase_       ,       default=1       ,       help="""Number of train epochs."""       ,       )
							__lowercase      :							List[Any]				=     parser.parse_args()
							__lowercase      :							List[str]				=     {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(lowerCAmelCase_       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	main()







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


lowerCamelCase : Optional[int]						   =       logging.get_logger(__name__)

lowerCamelCase : Tuple						   =       {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			int									=      '''layoutlmv3'''



					def __init__(  self				:	Dict		,     __a				:	List[str]=50265		,     __a				:	str=768		,     __a				:	List[Any]=12		,     __a				:	List[Any]=12		,     __a				:	List[str]=3072		,     __a				:	Optional[Any]="gelu"		,     __a				:	Optional[int]=0.1		,     __a				:	List[Any]=0.1		,     __a				:	Tuple=512		,     __a				:	int=2		,     __a				:	Any=0.02		,     __a				:	Union[str, Any]=1E-5		,     __a				:	List[str]=1		,     __a				:	List[Any]=0		,     __a				:	int=2		,     __a				:	str=1024		,     __a				:	str=128		,     __a				:	List[Any]=128		,     __a				:	Tuple=True		,     __a				:	Optional[int]=32		,     __a				:	Any=128		,     __a				:	List[Any]=64		,     __a				:	Tuple=256		,     __a				:	str=True		,     __a				:	int=True		,     __a				:	Optional[Any]=True		,     __a				:	Any=224		,     __a				:	str=3		,     __a				:	List[str]=16		,     __a				:	Union[str, Any]=None		,     **__a				:	List[Any]		,     )			->  List[str]:


												"""simple docstring"""

												super().__init__(
												    vocab_size=__a		,     hidden_size=__a		,     num_hidden_layers=__a		,     num_attention_heads=__a		,     intermediate_size=__a		,     hidden_act=__a		,     hidden_dropout_prob=__a		,     attention_probs_dropout_prob=__a		,     max_position_embeddings=__a		,     type_vocab_size=__a		,     initializer_range=__a		,     layer_norm_eps=__a		,     pad_token_id=__a		,     bos_token_id=__a		,     eos_token_id=__a		,     **__a		,     )
												__lowercase      :							int				=     max_ad_position_embeddings
												__lowercase      :							Any				=     coordinate_size
												__lowercase      :							Optional[Any]				=     shape_size
												__lowercase      :							str				=     has_relative_attention_bias
												__lowercase      :							int				=     rel_pos_bins
												__lowercase      :							Union[str, Any]				=     max_rel_pos
												__lowercase      :							str				=     has_spatial_attention_bias
												__lowercase      :							str				=     rel_ad_pos_bins
												__lowercase      :							List[Any]				=     max_rel_ad_pos
												__lowercase      :							Tuple				=     text_embed
												__lowercase      :							int				=     visual_embed
												__lowercase      :							Tuple				=     input_size
												__lowercase      :							Dict				=     num_channels
												__lowercase      :							str				=     patch_size
												__lowercase      :							Optional[int]				=     classifier_dropout







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			str									=      version.parse('''1.12'''			)



					@property
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Mapping[str, Mapping[int, str]]:


												"""simple docstring"""

												if self.task in ["question-answering", "sequence-classification"]:
																			return OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																			        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
																			    ]							)
												else:
																			return OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																			        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
																			    ]							)



					@property
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  float:


												"""simple docstring"""

												return 1E-5



					@property
					def       lowerCAmelCase				(  self				:	str							)			->  int:


												"""simple docstring"""

												return 12



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	"ProcessorMixin"		,     __a				:	int = -1		,     __a				:	int = -1		,     __a				:	bool = False		,     __a				:	Optional["TensorType"] = None		,     __a				:	int = 3		,     __a				:	int = 40		,     __a				:	int = 40		,     )			->  Mapping[str, Any]:


												"""simple docstring"""

												setattr(processor.image_processor		,     """apply_ocr"""		,     __a							)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												__lowercase      :							Tuple				=     compute_effective_axis_dimension(
												    __a		,     fixed_dimension=OnnxConfig.default_fixed_batch		,     num_token_to_add=0							)
												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												__lowercase      :							Tuple				=     processor.tokenizer.num_special_tokens_to_add(__a							)
												__lowercase      :							Tuple				=     compute_effective_axis_dimension(
												    __a		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=__a							)
												# Generate dummy inputs according to compute batch and sequence
												__lowercase      :							Union[str, Any]				=     [[""" """.join([processor.tokenizer.unk_token]							) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												__lowercase      :							Tuple				=     [[[48, 84, 73, 128]]] * batch_size

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
												__lowercase      :							Tuple				=     self._generate_dummy_images(__a		,     __a		,     __a		,     __a							)

												__lowercase      :							int				=     dict(
												    processor(
												        __a		,     text=__a		,     boxes=__a		,     return_tensors=__a		,     )							)

												return inputs