
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


SCREAMING_SNAKE_CASE				:Optional[int]									=							16
SCREAMING_SNAKE_CASE				:str									=							32


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







				"""simple docstring"""



				return int(x / 2**2_0		)





class 				UpperCAmelCase :




	'''simple docstring'''





	def __enter__(			self   :					Optional[Any]					):
					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					__A       =					torch.cuda.memory_allocated()
					return self







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





def 		UpperCAmelCase     (      a_						,  a_ = 1_6						,  a_ = "bert-base-cased"						,  a_ = 3_2_0						,  a_ = 1_6_0						,  )						->      Tuple:







				"""simple docstring"""



				__A       =					AutoTokenizer.from_pretrained(__A		)
				__A       =					load_dataset(
				    "glue"						,  "mrpc"						,  split={"train": F'''train[:{n_train}]''', "validation": F'''validation[:{n_val}]'''}		)

				def tokenize_function(a_		):
								# max_length=None => use the model max length (it's actually the default)
								__A       =					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
				__A       =					datasets.map(
				    __A						,  batched=__A						,  remove_columns=["idx", "sentence1", "sentence2"]						,  load_from_cache_file=__A		)

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

				def collate_fn(a_		):
								# 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(__A						,  padding="max_length"						,  max_length=1_2_8						,  return_tensors="pt"		)
								return tokenizer.pad(__A						,  padding="longest"						,  return_tensors="pt"		)

				# Instantiate dataloaders.
				__A       =					DataLoader(
				    tokenized_datasets["train"]						,  shuffle=__A						,  collate_fn=__A						,  batch_size=__A		)
				__A       =					DataLoader(
				    tokenized_datasets["validation"]						,  shuffle=__A						,  collate_fn=__A						,  batch_size=__A		)

				return train_dataloader, eval_dataloader


def 		UpperCAmelCase     (      a_						,  a_		)						->      List[str]:







				"""simple docstring"""



				__A       =					Accelerator()

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

				set_seed(__A		)
				__A       =					get_dataloaders(__A						,  __A						,  __A						,  args.n_train						,  args.n_val		)

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

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

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

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

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

				# Now we train the model
				__A       =					{}
				for epoch in range(__A						,  __A		):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(__A		):
																__A       =					model(**__A		)
																__A       =					outputs.loss
																__A       =					loss / gradient_accumulation_steps
																accelerator.backward(__A		)
																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		)		)		)
								__A       =					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(__A						,  __A		)


def 		UpperCAmelCase     (      )						->      str:







				"""simple docstring"""



				__A       =					argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."		)
				parser.add_argument(
				    "--model_name_or_path"						,  type=__A						,  default="bert-base-cased"						,  help="Path to pretrained model or model identifier from huggingface.co/models."						,  required=__A						,  )
				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(
				    "--peak_memory_upper_bound"						,  type=__A						,  default=__A						,  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=__A						,  default=3_2_0						,  help="Number of training examples to use."						,  )
				parser.add_argument(
				    "--n_val"						,  type=__A						,  default=1_6_0						,  help="Number of validation examples to use."						,  )
				parser.add_argument(
				    "--num_epochs"						,  type=__A						,  default=1						,  help="Number of train epochs."						,  )
				__A       =					parser.parse_args()
				__A       =					{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
				training_function(__A						,  __A		)


if __name__ == "__main__":
					main()








import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)




class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						def __init__(self:					Optional[int] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=13 ,					__UpperCAmelCase:					Any=7 ,					__UpperCAmelCase:					List[str]=True ,					__UpperCAmelCase:					Optional[int]=True ,					__UpperCAmelCase:					Dict=True ,					__UpperCAmelCase:					Optional[Any]=True ,					__UpperCAmelCase:					Optional[int]=99 ,					__UpperCAmelCase:					Optional[Any]=32 ,					__UpperCAmelCase:					int=5 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Optional[int]=37 ,					__UpperCAmelCase:					int="gelu" ,					__UpperCAmelCase:					Tuple=0.1 ,					__UpperCAmelCase:					Any=0.1 ,					__UpperCAmelCase:					Union[str, Any]=512 ,					__UpperCAmelCase:					Optional[Any]=16 ,					__UpperCAmelCase:					List[Any]=2 ,					__UpperCAmelCase:					str=0.02 ,					__UpperCAmelCase:					int=4 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Tuple      =					parent
											__a					:						int      =					batch_size
											__a					:						Optional[int]      =					seq_length
											__a					:						List[Any]      =					is_training
											__a					:						Tuple      =					use_attention_mask
											__a					:						Optional[int]      =					use_token_type_ids
											__a					:						Tuple      =					use_labels
											__a					:						str      =					vocab_size
											__a					:						Union[str, Any]      =					hidden_size
											__a					:						List[str]      =					num_hidden_layers
											__a					:						Optional[int]      =					num_attention_heads
											__a					:						Any      =					intermediate_size
											__a					:						Any      =					hidden_act
											__a					:						List[str]      =					hidden_dropout_prob
											__a					:						Dict      =					attention_probs_dropout_prob
											__a					:						Tuple      =					max_position_embeddings
											__a					:						Optional[int]      =					type_vocab_size
											__a					:						Tuple      =					type_sequence_label_size
											__a					:						List[Any]      =					initializer_range
											__a					:						int      =					num_choices



						def        UpperCAmelCase__					(self:					Tuple   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

											__a					:						Dict      =					None
											if self.use_attention_mask:
																__a					:						List[Any]      =					random_attention_mask([self.batch_size, self.seq_length]   )

											__a					:						Optional[int]      =					None
											if self.use_token_type_ids:
																__a					:						Dict      =					ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size   )

											__a					:						Dict      =					RobertaConfig(
											    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=__UpperCAmelCase ,					initializer_range=self.initializer_range ,					)

											return config, input_ids, token_type_ids, attention_mask



						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Dict      =					config_and_inputs
											__a					:						str      =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
											return config, inputs_dict



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											__a					:						Optional[Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					config_and_inputs

											__a					:						int      =					True
											__a					:						List[str]      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					vocab_size=2   )

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




@require_flax
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      True

						snake_case__        =      (
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)



						def        UpperCAmelCase__					(self:					Dict   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Tuple      =					FlaxRobertaModelTester(self   )



						@slow
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     List[Any]:




											'''simple docstring'''



											for model_class_name in self.all_model_classes:
																__a					:						int      =					model_class_name.from_pretrained("roberta-base" ,					from_pt=__UpperCAmelCase   )
																__a					:						Tuple      =					model(np.ones((1, 1)   )   )
																self.assertIsNotNone(__UpperCAmelCase   )





