




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


__A        =    16
__A        =    32







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any      )     ->       List[str]:





				"""simple docstring"""


				return int(x / 2**20      )







class     __lowerCAmelCase :

			"""simple docstring"""







			def __enter__(      self							)    ->      int:



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






			def __exit__(      self			,						*lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							gc.collect()
							torch.cuda.empty_cache()
							__lowerCamelCase		       =    torch.cuda.memory_allocated()
							__lowerCamelCase		       =    torch.cuda.max_memory_allocated()
							__lowerCamelCase		       =    bamb(self.end - self.begin							)
							__lowerCamelCase		       =    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 ,     )     ->       List[Any]:





				"""simple docstring"""


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

				def tokenize_function(UpperCamelCase__				:      Optional[int]      ):
								# max_length=None => use the model max length (it's actually the default)
								__lowerCamelCase		       =    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
				__lowerCamelCase		       =    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
				__lowerCamelCase		       =    tokenized_datasets.rename_column('label' ,     'labels'      )

				def collate_fn(UpperCamelCase__				:      Dict      ):
								# 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=128 ,     return_tensors='pt'      )
								return tokenizer.pad(UpperCamelCase__ ,     padding='longest' ,     return_tensors='pt'      )

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

				return train_dataloader, eval_dataloader







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    Accelerator()

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

				set_seed(UpperCamelCase__      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    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)
				__lowerCamelCase		       =    AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__ ,     return_dict=UpperCamelCase__      )

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

				if accelerator.state.deepspeed_plugin is not None:
								__lowerCamelCase		       =    accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								__lowerCamelCase		       =    1
				__lowerCamelCase		       =    (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
				):
								__lowerCamelCase		       =    get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase__ ,     num_warmup_steps=0 ,     num_training_steps=UpperCamelCase__ ,     )
				else:
								__lowerCamelCase		       =    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.
				__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    accelerator.prepare(
				    UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

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

				# Now we train the model
				__lowerCamelCase		       =    {}
				for epoch in range(UpperCamelCase__ ,     UpperCamelCase__      ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(UpperCamelCase__      ):
																__lowerCamelCase		       =    model(**UpperCamelCase__      )
																__lowerCamelCase		       =    outputs.loss
																__lowerCamelCase		       =    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      )      )      )
								__lowerCamelCase		       =    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_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    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=320 ,     help='Number of training examples to use.' ,     )
				parser.add_argument(
				    '--n_val' ,     type=UpperCamelCase__ ,     default=160 ,     help='Number of validation examples to use.' ,     )
				parser.add_argument(
				    '--num_epochs' ,     type=UpperCamelCase__ ,     default=1 ,     help='Number of train epochs.' ,     )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
				training_function(UpperCamelCase__ ,     UpperCamelCase__      )


if __name__ == "__main__":
					main()




# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


SCREAMING_SNAKE_CASE							:List[str]							 =				'pytorch_model.bin'
SCREAMING_SNAKE_CASE							:str							 =				'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'adapter_config.json'
SCREAMING_SNAKE_CASE							:Dict							 =				'adapter_model.bin'
SCREAMING_SNAKE_CASE							:Dict							 =				'adapter_model.safetensors'
SCREAMING_SNAKE_CASE							:str							 =				'tf_model.h5'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE							:str							 =				'model.ckpt'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'flax_model.msgpack'
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE							:Tuple							 =				'model.safetensors'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'model.safetensors.index.json'
SCREAMING_SNAKE_CASE							:str							 =				'config.json'
SCREAMING_SNAKE_CASE							:int							 =				'preprocessor_config.json'
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'generation_config.json'
SCREAMING_SNAKE_CASE							:List[str]							 =				'modelcard.json'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				'▁'
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

SCREAMING_SNAKE_CASE							:str							 =				[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE							:List[Any]							 =				[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def 						UpperCAmelCase			(	a_						)		->	Dict:







				"""simple docstring"""



				if version.parse(a_						) < version.parse(a_						):
								if "dev" in min_version:
												__A   			= (
												    "This example requires a source install from HuggingFace Transformers (see "
												    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
												)
								else:
												__A   			= F'''This example requires a minimum version of {min_version},'''
								error_message += F''' but the version found is {__version__}.\n'''
								raise ImportError(
								    error_message
								    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
								    "versions of HuggingFace Transformers."						)
