




'''simple docstring'''

import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    level=os.environ.get('LOGLEVEL', 'INFO').upper(),
    stream=sys.stdout,
)

a    :							Optional[int]										=    logging.getLogger(__name__)

a    :							Tuple										=    {'facebook/bart-base': BartForConditionalGeneration}
a    :							Any										=    {'facebook/bart-base': BartTokenizer}


def 						__magic_name__       (      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''      )
					parser.add_argument(
					    '''--validation_file''', type=__UpperCAmelCase, default=__UpperCAmelCase, help='''A csv or a json file containing the validation data.'''      )
					parser.add_argument(
					    '''--max_length''', type=__UpperCAmelCase, default=5, help='''The maximum total input sequence length after tokenization.''', )
					parser.add_argument(
					    '''--num_beams''', type=__UpperCAmelCase, default=__UpperCAmelCase, help=(
					        '''Number of beams to use for evaluation. This argument will be '''
					        '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
					    ), )
					parser.add_argument(
					    '''--model_name_or_path''', type=__UpperCAmelCase, help='''Path to pretrained model or model identifier from huggingface.co/models.''', required=__UpperCAmelCase, )
					parser.add_argument(
					    '''--config_name''', type=__UpperCAmelCase, default=__UpperCAmelCase, help='''Pretrained config name or path if not the same as model_name''', )
					parser.add_argument(
					    '''--device''', type=__UpperCAmelCase, default='''cpu''', help='''Device where the model will be run''', )
					parser.add_argument('''--output_file_path''', type=__UpperCAmelCase, default=__UpperCAmelCase, help='''Where to store the final ONNX file.'''      )

					snake_case_		     =							parser.parse_args()

					return args


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase="cpu"      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							model_dict[model_name].from_pretrained(__UpperCAmelCase      ).to(__UpperCAmelCase      )
					snake_case_		     =							tokenizer_dict[model_name].from_pretrained(__UpperCAmelCase      )

					if model_name in ["facebook/bart-base"]:
										snake_case_		     =							0
										snake_case_		     =							None
										snake_case_		     =							0

					return huggingface_model, tokenizer


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					model.eval()

					snake_case_		     =							None
					snake_case_		     =							torch.jit.script(BARTBeamSearchGenerator(__UpperCAmelCase      )      )

					with torch.no_grad():
										snake_case_		     =							'''My friends are cool but they eat too many carbs.'''
										snake_case_		     =							tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='''pt'''      ).to(model.device      )

										snake_case_		     =							model.generate(
										    inputs['''input_ids'''], attention_mask=inputs['''attention_mask'''], num_beams=__UpperCAmelCase, max_length=__UpperCAmelCase, early_stopping=__UpperCAmelCase, decoder_start_token_id=model.config.decoder_start_token_id, )

										torch.onnx.export(
										    __UpperCAmelCase, (
										        inputs['''input_ids'''],
										        inputs['''attention_mask'''],
										        num_beams,
										        max_length,
										        model.config.decoder_start_token_id,
										    ), __UpperCAmelCase, opset_version=14, input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id'''], output_names=['''output_ids'''], dynamic_axes={
										        '''input_ids''': {0: '''batch''', 1: '''seq'''},
										        '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
										    }, example_outputs=__UpperCAmelCase, )

										logger.info('''Model exported to {}'''.format(__UpperCAmelCase      )      )

										snake_case_		     =							remove_dup_initializers(os.path.abspath(__UpperCAmelCase      )      )

										logger.info('''Deduplicated and optimized model written to {}'''.format(__UpperCAmelCase      )      )

										snake_case_		     =							onnxruntime.InferenceSession(__UpperCAmelCase      )
										snake_case_		     =							ort_sess.run(
										    __UpperCAmelCase, {
										        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
										        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
										        '''num_beams''': np.array(__UpperCAmelCase      ),
										        '''max_length''': np.array(__UpperCAmelCase      ),
										        '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id      ),
										    }, )

										np.testing.assert_allclose(summary_ids.cpu().numpy(), ort_out[0], rtol=1e-3, atol=1e-3      )

										logger.info('''Model outputs from torch and ONNX Runtime are similar.'''      )
										logger.info('''Success.'''      )


def 						__magic_name__       (      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							parse_args()
					snake_case_		     =							5
					snake_case_		     =							4

					# Make one log on every process with the configuration for debugging.
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO, )

					logger.setLevel(logging.INFO      )
					transformers.utils.logging.set_verbosity_error()

					snake_case_		     =							torch.device(args.device      )

					snake_case_     ,snake_case_		     =							load_model_tokenizer(args.model_name_or_path, __UpperCAmelCase      )

					if model.config.decoder_start_token_id is None:
										raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''      )

					model.to(__UpperCAmelCase      )

					if args.max_length:
										snake_case_		     =							args.max_length

					if args.num_beams:
										snake_case_		     =							args.num_beams

					if args.output_file_path:
										snake_case_		     =							args.output_file_path
					else:
										snake_case_		     =							'''BART.onnx'''

					logger.info('''Exporting model to ONNX'''      )
					export_and_validate_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


if __name__ == "__main__":
							main()







'''simple docstring'''

import argparse
import os

import evaluate
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a    :							str										=    16
a    :							Union[str, Any]										=    32


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 16      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							AutoTokenizer.from_pretrained('''bert-base-cased'''      )
					snake_case_		     =							load_dataset('''glue''', '''mrpc'''      )

					def tokenize_function(__UpperCAmelCase      ):
										# max_length=None => use the model max length (it's actually the default)
										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
					# starting with the main process first:
					with accelerator.main_process_first():
										snake_case_		     =							datasets.map(
										    __UpperCAmelCase, batched=__UpperCAmelCase, remove_columns=['''idx''', '''sentence1''', '''sentence2'''], )

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

					def collate_fn(__UpperCAmelCase      ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										snake_case_		     =							128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															snake_case_		     =							16
										elif accelerator.mixed_precision != "no":
															snake_case_		     =							8
										else:
															snake_case_		     =							None

										return tokenizer.pad(
										    __UpperCAmelCase, padding='''longest''', max_length=__UpperCAmelCase, pad_to_multiple_of=__UpperCAmelCase, return_tensors='''pt''', )

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

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							a    :							Optional[Any]										=    mocked_dataloaders  # noqa: F811


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					if os.environ.get('''TESTING_MOCKED_DATALOADERS''', __UpperCAmelCase      ) == "1":
										snake_case_		     =							2
					# New Code #
					snake_case_		     =							int(args.gradient_accumulation_steps      )
					snake_case_		     =							int(args.local_sgd_steps      )
					# Initialize accelerator
					snake_case_		     =							Accelerator(
					    cpu=args.cpu, mixed_precision=args.mixed_precision, gradient_accumulation_steps=__UpperCAmelCase      )
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''      )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_		     =							config['''lr''']
					snake_case_		     =							int(config['''num_epochs''']      )
					snake_case_		     =							int(config['''seed''']      )
					snake_case_		     =							int(config['''batch_size''']      )

					snake_case_		     =							evaluate.load('''glue''', '''mrpc'''      )

					set_seed(__UpperCAmelCase      )
					snake_case_     ,snake_case_		     =							get_dataloaders(__UpperCAmelCase, __UpperCAmelCase      )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_		     =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''', return_dict=__UpperCAmelCase      )

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					snake_case_		     =							model.to(accelerator.device      )

					# Instantiate optimizer
					snake_case_		     =							AdamW(params=model.parameters(), lr=__UpperCAmelCase      )

					# Instantiate scheduler
					snake_case_		     =							get_linear_schedule_with_warmup(
					    optimizer=__UpperCAmelCase, num_warmup_steps=100, num_training_steps=(len(__UpperCAmelCase      ) * num_epochs), )

					# 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.
					snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							accelerator.prepare(
					    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					# Now we train the model
					for epoch in range(__UpperCAmelCase      ):
										model.train()
										with LocalSGD(
										    accelerator=__UpperCAmelCase, model=__UpperCAmelCase, local_sgd_steps=__UpperCAmelCase, enabled=local_sgd_steps is not None      ) as local_sgd:
															for step, batch in enumerate(__UpperCAmelCase      ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device      )
																				# New code #
																				# We use the new `accumulate` context manager to perform gradient accumulation
																				# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																				with accelerator.accumulate(__UpperCAmelCase      ):
																									snake_case_		     =							model(**__UpperCAmelCase      )
																									snake_case_		     =							output.loss
																									accelerator.backward(__UpperCAmelCase      )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

										model.eval()
										for step, batch in enumerate(__UpperCAmelCase      ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device      )
															with torch.no_grad():
																				snake_case_		     =							model(**__UpperCAmelCase      )
															snake_case_		     =							outputs.logits.argmax(dim=-1      )
															snake_case_     ,snake_case_		     =							accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
															metric.add_batch(
															    predictions=__UpperCAmelCase, references=__UpperCAmelCase, )

										snake_case_		     =							metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:", __UpperCAmelCase      )


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							argparse.ArgumentParser(description='''Simple example of training script.'''      )
					parser.add_argument(
					    '''--mixed_precision''', type=__UpperCAmelCase, default=__UpperCAmelCase, choices=['''no''', '''fp16''', '''bf16''', '''fp8'''], help='''Whether to use mixed precision. Choose'''
					    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
					    '''and an Nvidia Ampere GPU.''', )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps''', type=__UpperCAmelCase, default=1, help='''The number of minibatches to be ran before gradients are accumulated.''', )
					parser.add_argument(
					    '''--local_sgd_steps''', type=__UpperCAmelCase, default=8, help='''Number of local SGD steps or None to disable local SGD'''      )
					parser.add_argument('''--cpu''', action='''store_true''', help='''If passed, will train on the CPU.'''      )
					snake_case_		     =							parser.parse_args()
					snake_case_		     =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__UpperCAmelCase, __UpperCAmelCase      )


if __name__ == "__main__":
							main()

