




'''simple docstring'''

from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


a    :							int										=    logging.get_logger(__name__)


a    :							Optional[int]										=    OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

a    :							Tuple										=    OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

a    :							List[str]										=    OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

a    :							List[str]										=    OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

a    :							str										=    OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

a    :							List[Any]										=    OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

a    :							List[Any]										=    OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

a    :							Dict										=    OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

a    :							List[Any]										=    OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

a    :							str										=    OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

a    :							Any										=    OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

a    :							Tuple										=    OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

a    :							str										=    OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

a    :							Tuple										=    OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

a    :							Optional[int]										=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a    :							Union[str, Any]										=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a    :							Any										=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a    :							List[Any]										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a    :							Any										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a    :							Dict										=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a    :							Optional[Any]										=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a    :							List[Any]										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a    :							Union[str, Any]										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a    :							int										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a    :							str										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a    :							Tuple										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a    :							Tuple										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a    :							str										=    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_MAPPING







a    :							str										=    auto_class_update(FlaxAutoModel)






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_PRETRAINING_MAPPING







a    :							List[str]										=    auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_CAUSAL_LM_MAPPING







a    :							List[str]										=    auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_MASKED_LM_MAPPING







a    :							List[Any]										=    auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







a    :							Any										=    auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







a    :							int										=    auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING







a    :							str										=    auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING







a    :							Optional[Any]										=    auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING







a    :							List[Any]										=    auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING







a    :							str										=    auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







a    :							Tuple										=    auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING







a    :							Tuple										=    auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')






class 		a							(  _BaseAutoModelClass	):
				snake_case_           =						FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING







a    :							List[str]										=    auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)







'''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()

