



import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCamelCase_      			=  "\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

UpperCamelCase_      			=  "\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

UpperCamelCase_      			=  "\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - 'id': id of the question-answer pair (see above),\n            - 'answers': a list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION							)
class 				a_    (						datasets.Metric							):

						def       __a			(       self      :Dict)						->   Optional[int]:
												return datasets.MetricInfo(
												    description=_DESCRIPTION  ,		citation=_CITATION  ,		inputs_description=_KWARGS_DESCRIPTION  ,		features=datasets.Features(
												        {
												            '''predictions''': {
												                '''id''': datasets.Value('''string'''),
												                '''prediction_text''': datasets.Value('''string'''),
												                '''no_answer_probability''': datasets.Value('''float32'''),
												            },
												            '''references''': {
												                '''id''': datasets.Value('''string'''),
												                '''answers''': datasets.features.Sequence(
												                    {'''text''': datasets.Value('''string'''), '''answer_start''': datasets.Value('''int32''')}),
												            },
												        })  ,		codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']  ,		reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']  ,		)




						def       __a			(       self      :str  ,		_lowercase      :Dict  ,		_lowercase      :List[Any]  ,		_lowercase      :int=1.0)						->   Optional[int]:
												UpperCAmelCase_					=	{p['''id''']: p['''no_answer_probability'''] for p in predictions}
												UpperCAmelCase_					=	[{'''paragraphs''': [{'''qas''': references}]}]
												UpperCAmelCase_					=	{p['''id''']: p['''prediction_text'''] for p in predictions}

												UpperCAmelCase_					=	make_qid_to_has_ans(_lowercase)  # maps qid to True/False
												UpperCAmelCase_					=	[k for k, v in qid_to_has_ans.items() if v]
												UpperCAmelCase_					=	[k for k, v in qid_to_has_ans.items() if not v]

												UpperCAmelCase_				, UpperCAmelCase_					=	get_raw_scores(_lowercase  ,		_lowercase)
												UpperCAmelCase_					=	apply_no_ans_threshold(_lowercase  ,		_lowercase  ,		_lowercase  ,		_lowercase)
												UpperCAmelCase_					=	apply_no_ans_threshold(_lowercase  ,		_lowercase  ,		_lowercase  ,		_lowercase)
												UpperCAmelCase_					=	make_eval_dict(_lowercase  ,		_lowercase)

												if has_ans_qids:
																		UpperCAmelCase_					=	make_eval_dict(_lowercase  ,		_lowercase  ,		qid_list=_lowercase)
																		merge_eval(_lowercase  ,		_lowercase  ,		'''HasAns''')
												if no_ans_qids:
																		UpperCAmelCase_					=	make_eval_dict(_lowercase  ,		_lowercase  ,		qid_list=_lowercase)
																		merge_eval(_lowercase  ,		_lowercase  ,		'''NoAns''')
												find_all_best_thresh(_lowercase  ,		_lowercase  ,		_lowercase  ,		_lowercase  ,		_lowercase  ,		_lowercase)
												return dict(_lowercase)










import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


UpperCamelCase_      			=  16
UpperCamelCase_      			=  32







def 							A     (						__UpperCAmelCase      )			->			Optional[int]:



						'''simple docstring'''






						return int(x / 2**20      )







class 				a_    :

						def __enter__(       self      :Any)						->   Optional[Any]:
												gc.collect()
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
												UpperCAmelCase_					=	torch.cuda.memory_allocated()
												UpperCAmelCase_					=	psutil.Process()

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	threading.Thread(target=self.peak_monitor_func)
												UpperCAmelCase_					=	True
												peak_monitor_thread.start()
												return self

						def       __a			(       self      :Tuple)						->   int:
												return self.process.memory_info().rss

						def       __a			(       self      :str)						->   Dict:
												UpperCAmelCase_					=	-1

												while True:
																		UpperCAmelCase_					=	max(self.cpu_mem_used()  ,		self.cpu_peak)

																		# can't sleep or will not catch the peak right (this comment is here on purpose)
																		# time.sleep(0.001) # 1msec

																		if not self.peak_monitoring:
																								break




						def __exit__(       self      :Optional[Any]  ,		*_lowercase      :Optional[Any])						->   List[str]:
												UpperCAmelCase_					=	False

												gc.collect()
												torch.cuda.empty_cache()
												UpperCAmelCase_					=	torch.cuda.memory_allocated()
												UpperCAmelCase_					=	torch.cuda.max_memory_allocated()
												UpperCAmelCase_					=	bamb(self.end - self.begin)
												UpperCAmelCase_					=	bamb(self.peak - self.begin)

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	bamb(self.cpu_end - self.cpu_begin)
												UpperCAmelCase_					=	bamb(self.cpu_peak - self.cpu_begin)
												# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

							UpperCamelCase_      			=  mocked_dataloaders  # noqa: F811







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,   __UpperCAmelCase      ) == "1":
												UpperCAmelCase_					=	2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						UpperCAmelCase_					=	FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   )

						# Initialize accelerator
						if args.with_tracking:
												UpperCAmelCase_					=	Accelerator(
												    cpu=args.cpu				,   mixed_precision=args.mixed_precision				,   log_with='''wandb'''				,   project_dir=args.logging_dir				,   fsdp_plugin=__UpperCAmelCase				,   )
						else:
												UpperCAmelCase_					=	Accelerator(fsdp_plugin=__UpperCAmelCase      )
						accelerator.print(accelerator.distributed_type      )

						if hasattr(args.checkpointing_steps				,   '''isdigit'''      ):
												if args.checkpointing_steps == "epoch":
																		UpperCAmelCase_					=	args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		UpperCAmelCase_					=	int(args.checkpointing_steps      )
												else:
																		raise ValueError(
																		    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
						else:
												UpperCAmelCase_					=	None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_					=	config['''lr''']
						UpperCAmelCase_					=	int(config['''num_epochs''']      )
						UpperCAmelCase_					=	int(config['''seed''']      )
						UpperCAmelCase_					=	int(config['''batch_size''']      )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												UpperCAmelCase_					=	vars(__UpperCAmelCase      )
												accelerator.init_trackers('''fsdp_glue_no_trainer'''				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	AutoTokenizer.from_pretrained(args.model_name_or_path      )
						UpperCAmelCase_					=	load_dataset('''glue'''				,   '''mrpc'''      )
						UpperCAmelCase_					=	evaluate.load('''glue'''				,   '''mrpc'''      )

						def tokenize_function(__UpperCAmelCase      ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_					=	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():
												UpperCAmelCase_					=	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
						UpperCAmelCase_					=	tokenized_datasets.rename_column('''label'''				,   '''labels'''      )

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase_					=	1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase_					=	batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase_					=	MAX_GPU_BATCH_SIZE

						def collate_fn(__UpperCAmelCase      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase_					=	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":
																		UpperCAmelCase_					=	16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase_					=	8
												else:
																		UpperCAmelCase_					=	None

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

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

						set_seed(__UpperCAmelCase      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_					=	AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path				,   return_dict=__UpperCAmelCase				,   low_cpu_mem_usage=__UpperCAmelCase      )

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						UpperCAmelCase_					=	accelerator.prepare(__UpperCAmelCase      )
						accelerator.print(__UpperCAmelCase      )

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						UpperCAmelCase_					=	torch.optim.AdamW(params=model.parameters()				,   lr=__UpperCAmelCase				,   weight_decay=2e-4      )

						# Instantiate scheduler
						UpperCAmelCase_					=	get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase				,   num_warmup_steps=10				,   num_training_steps=(len(__UpperCAmelCase      ) * num_epochs) // gradient_accumulation_steps				,   )

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# 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.
						UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.prepare(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"      )
																		accelerator.load_state(args.resume_from_checkpoint      )
																		UpperCAmelCase_					=	os.path.basename(args.resume_from_checkpoint      )
												else:
																		# Get the most recent checkpoint
																		UpperCAmelCase_					=	[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime      )
																		UpperCAmelCase_					=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												UpperCAmelCase_					=	os.path.splitext(__UpperCAmelCase      )[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('''epoch_'''				,   ''''''      )      )
																		UpperCAmelCase_					=	None
												else:
																		UpperCAmelCase_					=	int(training_difference.replace('''step_'''				,   ''''''      )      )
																		num_epochs -= resume_step // len(__UpperCAmelCase      )
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		UpperCAmelCase_					=	(num_epochs * len(__UpperCAmelCase      )) - resume_step

    # Now we train the model
						for epoch in range(__UpperCAmelCase      ):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								UpperCAmelCase_					=	0
																		for step, batch in enumerate(__UpperCAmelCase      ):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.loss
																								UpperCAmelCase_					=	loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(__UpperCAmelCase      )
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
																														UpperCAmelCase_					=	f"step_{overall_step}"
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																																				accelerator.save_state(__UpperCAmelCase      )
        # New Code #
        # 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      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }				,   step=__UpperCAmelCase				,   )

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		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():
																														UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.logits.argmax(dim=-1      )
																								UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
																								metric.add_batch(
																								    predictions=__UpperCAmelCase				,   references=__UpperCAmelCase				,   )

																		UpperCAmelCase_					=	metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f"epoch {epoch}:"				,   __UpperCAmelCase      )
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        '''accuracy''': eval_metric['''accuracy'''],
																								        '''f1''': eval_metric['''f1'''],
																								        '''train_loss''': total_loss.item() / len(__UpperCAmelCase      ),
																								    }				,   step=__UpperCAmelCase				,   )

																		if checkpointing_steps == "epoch":
																								UpperCAmelCase_					=	f"epoch_{epoch}"
																								if args.output_dir is not None:
																														UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																								accelerator.save_state(__UpperCAmelCase      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin      )      )      )
												accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used      )      )
												accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked      )      )
												accelerator.print(
												    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }				,   step=__UpperCAmelCase				,   )

						if args.with_tracking:
												accelerator.end_training()







def 							A     (						)			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	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.'''				,   )
						parser.add_argument('''--cpu'''				,   action='''store_true'''				,   help='''If passed, will train on the CPU.'''      )
						parser.add_argument(
						    '''--checkpointing_steps'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,   )
						parser.add_argument(
						    '''--resume_from_checkpoint'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   help='''If the training should continue from a checkpoint folder.'''				,   )
						parser.add_argument(
						    '''--with_tracking'''				,   action='''store_true'''				,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''				,   )
						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(
						    '''--logging_dir'''				,   type=__UpperCAmelCase				,   default='''logs'''				,   help='''Location on where to store experiment tracking logs`'''				,   )
						parser.add_argument(
						    '''--model_name_or_path'''				,   type=__UpperCAmelCase				,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,   required=__UpperCAmelCase				,   )
						UpperCAmelCase_					=	parser.parse_args()
						UpperCAmelCase_					=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(__UpperCAmelCase				,   __UpperCAmelCase      )


if __name__ == "__main__":
							main()





