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[Any]     			=						16
_lowerCamelCase   :				Any     			=						32





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					return int(x / 2**2_0					)


class   lowercase      :




							def __enter__(  self				:   Tuple )   ->			int:



												'''simple docstring'''

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





							def __exit__(  self				:   Optional[Any]		,							*_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Accelerator ,	UpperCAmelCase__       :							int = 1_6 ,	UpperCAmelCase__       :							str = "bert-base-cased" ,	UpperCAmelCase__       :							int = 3_2_0 ,	UpperCAmelCase__       :							int = 1_6_0 ,	):
					SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    load_dataset(
					    "glue" ,	"mrpc" ,	split={"train": F"train[:{n_train}]", "validation": F"validation[:{n_val}]"}					)

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

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

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

					def collate_fn(UpperCAmelCase__       :							Optional[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(UpperCAmelCase__ ,	padding="max_length" ,	max_length=1_2_8 ,	return_tensors="pt"					)
										return tokenizer.pad(UpperCAmelCase__ ,	padding="longest" ,	return_tensors="pt"					)

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

					return train_dataloader, eval_dataloader





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							int					):
					# Initialize accelerator
					SCREAMING_SNAKE_CASE           =    Accelerator()

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

					set_seed(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    get_dataloaders(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	args.n_train ,	args.n_val					)

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

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

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

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

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

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





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


if __name__ == "__main__":
							main()





import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileNetVaForImageClassification, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor


class   lowercase      (       a       ):




							def 		__snake_case(  self				:   Optional[int] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.config_class(**self.inputs_dict )
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"tf_padding" ) )
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"depth_multiplier" ) )



class   lowercase      :




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Any=13		,							_UpperCamelCase				:   Any=3		,							_UpperCamelCase				:   Union[str, Any]=32		,							_UpperCamelCase				:   Optional[Any]=0.2_5		,							_UpperCamelCase				:   int=8		,							_UpperCamelCase				:   str=True		,							_UpperCamelCase				:   Any=1_024		,							_UpperCamelCase				:   Tuple=32		,							_UpperCamelCase				:   List[str]="relu6"		,							_UpperCamelCase				:   Tuple=0.1		,							_UpperCamelCase				:   List[str]=0.0_2		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Optional[Any]=10		,							_UpperCamelCase				:   List[str]=None		,							)   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    depth_multiplier
												SCREAMING_SNAKE_CASE           =    min_depth
												SCREAMING_SNAKE_CASE           =    tf_padding
												SCREAMING_SNAKE_CASE           =    int(last_hidden_size * depth_multiplier )
												SCREAMING_SNAKE_CASE           =    output_stride
												SCREAMING_SNAKE_CASE           =    hidden_act
												SCREAMING_SNAKE_CASE           =    classifier_dropout_prob
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    num_labels
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    scope




							def 		__snake_case(  self				:   Dict )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size]		,							self.num_labels )
																	SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.image_size, self.image_size]		,							self.num_labels )

												SCREAMING_SNAKE_CASE           =    self.get_config()

												return config, pixel_values, labels, pixel_labels




							def 		__snake_case(  self				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												return MobileNetVaConfig(
												    num_channels=self.num_channels		,							image_size=self.image_size		,							depth_multiplier=self.depth_multiplier		,							min_depth=self.min_depth		,							tf_padding=self.tf_padding		,							hidden_act=self.hidden_act		,							classifier_dropout_prob=self.classifier_dropout_prob		,							initializer_range=self.initializer_range		,							)




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   int )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,							(
												        self.batch_size,
												        self.last_hidden_size,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )		,							)




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.num_labels
												SCREAMING_SNAKE_CASE           =    MobileNetVaForImageClassification(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							labels=_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels) )





							def 		__snake_case(  self				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    config_and_inputs
												SCREAMING_SNAKE_CASE           =    {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    Dict									=							(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							lowercase__  :    Tuple									=							(
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    Tuple									=							False
							lowercase__  :    List[str]									=							False




							def 		__snake_case(  self				:   List[str] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaModelTester(self )
												SCREAMING_SNAKE_CASE           =    MobileNetVaConfigTester(self		,							config_class=_UpperCamelCase		,							has_text_modality=_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any] )   ->			List[Any]:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
							def 		__snake_case(  self				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not output attentions" )
							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    inspect.signature(model.forward )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	SCREAMING_SNAKE_CASE           =    [*signature.parameters.keys()]

																	SCREAMING_SNAKE_CASE           =    ["pixel_values"]
																	self.assertListEqual(arg_names[:1]		,							_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase )




							def 		__snake_case(  self				:   List[str] )   ->			Optional[int]:



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Tuple ):
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()

																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE           =    model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )

																	SCREAMING_SNAKE_CASE           =    outputs.hidden_states

																	SCREAMING_SNAKE_CASE           =    26
																	self.assertEqual(len(_UpperCamelCase )		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    True
																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	SCREAMING_SNAKE_CASE           =    True

																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Any )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase )





							@slow
							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE           =    MobileNetVaModel.from_pretrained(_UpperCamelCase )
																	self.assertIsNotNone(_UpperCamelCase )





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


@require_torch
@require_vision
class   lowercase      (       unittest.TestCase       ):




							@cached_property
							def 		__snake_case(  self				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												return (
												    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
												)





							@slow
							def 		__snake_case(  self				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.default_image_processor
												SCREAMING_SNAKE_CASE           =    prepare_img()
												SCREAMING_SNAKE_CASE           =    image_processor(images=_UpperCamelCase		,							return_tensors="pt" ).to(_UpperCamelCase )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )

												# verify the logits
												SCREAMING_SNAKE_CASE           =    torch.Size((1, 1_001) )
												self.assertEqual(outputs.logits.shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] ).to(_UpperCamelCase )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]		,							_UpperCamelCase		,							atol=1e-4 ) )




