



"""simple docstring"""


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import (
				    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SqueezeBertForMaskedLM,
				    SqueezeBertForMultipleChoice,
				    SqueezeBertForQuestionAnswering,
				    SqueezeBertForSequenceClassification,
				    SqueezeBertForTokenClassification,
				    SqueezeBertModel,
				)







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def __init__(					self :Optional[Any]   ,  lowercase_ :int   ,  lowercase_ :Tuple=13   ,  lowercase_ :Any=7   ,  lowercase_ :List[str]=True   ,  lowercase_ :Optional[int]=True   ,  lowercase_ :str=False   ,  lowercase_ :Optional[int]=True   ,  lowercase_ :Union[str, Any]=99   ,  lowercase_ :Optional[int]=32   ,  lowercase_ :int=5   ,  lowercase_ :int=4   ,  lowercase_ :Optional[int]=64   ,  lowercase_ :Union[str, Any]="gelu"   ,  lowercase_ :Union[str, Any]=0.1   ,  lowercase_ :List[Any]=0.1   ,  lowercase_ :Union[str, Any]=5_12   ,  lowercase_ :Tuple=16   ,  lowercase_ :Union[str, Any]=2   ,  lowercase_ :Optional[Any]=0.02   ,  lowercase_ :Union[str, Any]=3   ,  lowercase_ :int=4   ,  lowercase_ :Any=None   ,  lowercase_ :Any=2   ,  lowercase_ :List[Any]=2   ,  lowercase_ :Optional[Any]=2   ,  lowercase_ :Any=2   ,  lowercase_ :int=4   ,  lowercase_ :Optional[int]=1   ,  )				->			int:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = seq_length
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_input_mask
												UpperCAmelCase             = use_token_type_ids
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												UpperCAmelCase             = intermediate_size
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob
												UpperCAmelCase             = max_position_embeddings
												UpperCAmelCase             = type_vocab_size
												UpperCAmelCase             = type_sequence_label_size
												UpperCAmelCase             = initializer_range
												UpperCAmelCase             = num_labels
												UpperCAmelCase             = num_choices
												UpperCAmelCase             = scope
												UpperCAmelCase             = q_groups
												UpperCAmelCase             = k_groups
												UpperCAmelCase             = v_groups
												UpperCAmelCase             = post_attention_groups
												UpperCAmelCase             = intermediate_groups
												UpperCAmelCase             = output_groups






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Union[str, Any]:
												UpperCAmelCase             = ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size					)

												UpperCAmelCase             = None
												if self.use_input_mask:
																		UpperCAmelCase             = random_attention_mask([self.batch_size, self.seq_length]					)

												UpperCAmelCase             = None
												UpperCAmelCase             = None
												UpperCAmelCase             = None
												if self.use_labels:
																		UpperCAmelCase             = ids_tensor([self.batch_size]   ,  self.type_sequence_label_size					)
																		UpperCAmelCase             = ids_tensor([self.batch_size, self.seq_length]   ,  self.num_labels					)
																		UpperCAmelCase             = ids_tensor([self.batch_size]   ,  self.num_choices					)

												UpperCAmelCase             = self.get_config()

												return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Optional[Any]:
												return SqueezeBertConfig(
												    embedding_size=self.hidden_size   ,  vocab_size=self.vocab_size   ,  hidden_size=self.hidden_size   ,  num_hidden_layers=self.num_hidden_layers   ,  num_attention_heads=self.num_attention_heads   ,  intermediate_size=self.intermediate_size   ,  hidden_act=self.hidden_act   ,  attention_probs_dropout_prob=self.hidden_dropout_prob   ,  attention_dropout=self.attention_probs_dropout_prob   ,  max_position_embeddings=self.max_position_embeddings   ,  initializer_range=self.initializer_range   ,  q_groups=self.q_groups   ,  k_groups=self.k_groups   ,  v_groups=self.v_groups   ,  post_attention_groups=self.post_attention_groups   ,  intermediate_groups=self.intermediate_groups   ,  output_groups=self.output_groups   ,  )






						def 	UpperCAmelCase__     (					self :Optional[Any]   ,  lowercase_ :List[str]   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Tuple   ,  lowercase_ :Any   ,  lowercase_ :Dict   ,  lowercase_ :Tuple					)				->			str:
												UpperCAmelCase             = SqueezeBertModel(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  lowercase_					)
												UpperCAmelCase             = model(lowercase_					)
												self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)					)






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :str   ,  lowercase_ :Tuple   ,  lowercase_ :List[Any]   ,  lowercase_ :Dict   ,  lowercase_ :Any					)				->			int:
												UpperCAmelCase             = SqueezeBertForMaskedLM(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)					)






						def 	UpperCAmelCase__     (					self :List[str]   ,  lowercase_ :List[Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Dict   ,  lowercase_ :List[str]					)				->			Dict:
												UpperCAmelCase             = SqueezeBertForQuestionAnswering(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  start_positions=lowercase_   ,  end_positions=lowercase_					)
												self.parent.assertEqual(result.start_logits.shape   ,  (self.batch_size, self.seq_length)					)
												self.parent.assertEqual(result.end_logits.shape   ,  (self.batch_size, self.seq_length)					)






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :str   ,  lowercase_ :List[str]   ,  lowercase_ :int   ,  lowercase_ :str   ,  lowercase_ :Optional[int]   ,  lowercase_ :Union[str, Any]					)				->			int:
												UpperCAmelCase             = self.num_labels
												UpperCAmelCase             = SqueezeBertForSequenceClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_labels)					)






						def 	UpperCAmelCase__     (					self :List[str]   ,  lowercase_ :List[str]   ,  lowercase_ :Any   ,  lowercase_ :Any   ,  lowercase_ :Dict   ,  lowercase_ :Optional[int]   ,  lowercase_ :Tuple					)				->			Any:
												UpperCAmelCase             = self.num_labels
												UpperCAmelCase             = SqueezeBertForTokenClassification(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()

												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.num_labels)					)






						def 	UpperCAmelCase__     (					self :int   ,  lowercase_ :Dict   ,  lowercase_ :Any   ,  lowercase_ :Tuple   ,  lowercase_ :Optional[Any]   ,  lowercase_ :int   ,  lowercase_ :str					)				->			int:
												UpperCAmelCase             = self.num_choices
												UpperCAmelCase             = SqueezeBertForMultipleChoice(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = input_ids.unsqueeze(1					).expand(-1   ,  self.num_choices   ,  -1					).contiguous()
												UpperCAmelCase             = input_mask.unsqueeze(1					).expand(-1   ,  self.num_choices   ,  -1					).contiguous()
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_   ,  )
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_choices)					)






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Tuple:
												UpperCAmelCase             = self.prepare_config_and_inputs()
												((UpperCAmelCase)      ,    (UpperCAmelCase)      ,    (UpperCAmelCase)      ,    (UpperCAmelCase)      ,    (UpperCAmelCase)      ,    (UpperCAmelCase))             = config_and_inputs
												UpperCAmelCase             = {'input_ids': input_ids, 'attention_mask': input_mask}
												return config, inputs_dict







@require_torch
class       A_			(     SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(
						    (
						        SqueezeBertModel,
						        SqueezeBertForMaskedLM,
						        SqueezeBertForMultipleChoice,
						        SqueezeBertForQuestionAnswering,
						        SqueezeBertForSequenceClassification,
						        SqueezeBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						__UpperCamelCase						=							(
						    {
						        """feature-extraction""": SqueezeBertModel,
						        """fill-mask""": SqueezeBertForMaskedLM,
						        """question-answering""": SqueezeBertForQuestionAnswering,
						        """text-classification""": SqueezeBertForSequenceClassification,
						        """token-classification""": SqueezeBertForTokenClassification,
						        """zero-shot""": SqueezeBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCamelCase						=							False
						__UpperCamelCase						=							True
						__UpperCamelCase						=							False






						def 	UpperCAmelCase__     (					self :str					)				->			Dict:
												UpperCAmelCase             = SqueezeBertModelTester(self					)
												UpperCAmelCase             = ConfigTester(self   ,  config_class=lowercase_   ,  dim=37					)






						def 	UpperCAmelCase__     (					self :int					)				->			Dict:
												self.config_tester.run_common_tests()






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Tuple:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_model(*lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			List[str]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase_					)






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Dict:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase_					)






						def 	UpperCAmelCase__     (					self :str					)				->			Union[str, Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase_					)






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Optional[Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase_					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Any:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase_					)






						@slow
						def 	UpperCAmelCase__     (					self :Dict					)				->			List[str]:
												for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase             = SqueezeBertModel.from_pretrained(lowercase_					)
																		self.assertIsNotNone(lowercase_					)







@require_sentencepiece
@require_tokenizers
@require_torch
class       A_			(     unittest.TestCase				):



						"""simple docstring"""






						@slow
						def 	UpperCAmelCase__     (					self :Tuple					)				->			int:
												UpperCAmelCase             = SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'					)

												UpperCAmelCase             = torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]					)
												UpperCAmelCase             = model(lowercase_					)[0]
												UpperCAmelCase             = torch.Size((1, 3)					)
												self.assertEqual(output.shape   ,  lowercase_					)
												UpperCAmelCase             = torch.tensor([[0.6401, -0.0349, -0.6041]]					)
												self.assertTrue(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-4					)					)









'''simple docstring'''



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


__lowercase	:						Dict       =	16
__lowercase	:						List[Any]       =	32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					return int(x / 2**20   )





class 							__UpperCamelCase				:




	def __enter__(   self							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						__a			:       Optional[int]					    =  torch.cuda.memory_allocated()
						return self





	def __exit__(   self						,				*__a							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						__a			:       Dict					    =  torch.cuda.memory_allocated()
						__a			:       List[Any]					    =  torch.cuda.max_memory_allocated()
						__a			:       Tuple					    =  bamb(self.end - self.begin							)
						__a			:       Tuple					    =  bamb(self.peak - self.begin							)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Accelerator						,      _SCREAMING_SNAKE_CASE      :   int = 16						,      _SCREAMING_SNAKE_CASE      :   str = "bert-base-cased"						,      _SCREAMING_SNAKE_CASE      :   int = 320						,      _SCREAMING_SNAKE_CASE      :   int = 160						,      ):
					__a			:       int					    =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       List[Any]					    =  load_dataset(
					    'glue'						,      'mrpc'						,      split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}   )

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

					# Apply the method we just defined to all the examples in all the splits of the dataset
					__a			:       List[str]					    =  datasets.map(
					    _SCREAMING_SNAKE_CASE						,      batched=_SCREAMING_SNAKE_CASE						,      remove_columns=['idx', 'sentence1', 'sentence2']						,      load_from_cache_file=_SCREAMING_SNAKE_CASE   )

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

					def collate_fn(_SCREAMING_SNAKE_CASE      :   Tuple   ):
										# 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(_SCREAMING_SNAKE_CASE						,      padding='max_length'						,      max_length=128						,      return_tensors='pt'   )
										return tokenizer.pad(_SCREAMING_SNAKE_CASE						,      padding='longest'						,      return_tensors='pt'   )

					# Instantiate dataloaders.
					__a			:       int					    =  DataLoader(
					    tokenized_datasets['train']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  DataLoader(
					    tokenized_datasets['validation']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )

					return train_dataloader, eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					# Initialize accelerator
					__a			:       str					    =  Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a			:       Dict					    =  config['lr']
					__a			:       str					    =  int(config['num_epochs']   )
					__a			:       Optional[int]					    =  int(config['seed']   )
					__a			:       Any					    =  int(config['batch_size']   )
					__a			:       List[str]					    =  args.model_name_or_path

					set_seed(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       int					    =  get_dataloaders(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      args.n_train						,      args.n_val   )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__a			:       Optional[int]					    =  AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE						,      return_dict=_SCREAMING_SNAKE_CASE   )

					# Instantiate optimizer
					__a			:       Optional[Any]					    =  (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					__a			:       Optional[Any]					    =  optimizer_cls(params=model.parameters()						,      lr=_SCREAMING_SNAKE_CASE   )

					if accelerator.state.deepspeed_plugin is not None:
										__a			:       int					    =  accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										__a			:       Union[str, Any]					    =  1
					__a			:       Tuple					    =  (len(_SCREAMING_SNAKE_CASE   ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										__a			:       str					    =  get_linear_schedule_with_warmup(
										    optimizer=_SCREAMING_SNAKE_CASE						,      num_warmup_steps=0						,      num_training_steps=_SCREAMING_SNAKE_CASE						,      )
					else:
										__a			:       List[Any]					    =  DummyScheduler(_SCREAMING_SNAKE_CASE						,      total_num_steps=_SCREAMING_SNAKE_CASE						,      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.
					__a    ,				__a    ,				__a    ,				__a    ,				__a			:       Optional[Any]					    =  accelerator.prepare(
					    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# We need to keep track of how many total steps we have iterated over
					__a			:       Union[str, Any]					    =  0
					# We also need to keep track of the stating epoch so files are named properly
					__a			:       Dict					    =  0

					# Now we train the model
					__a			:       str					    =  {}
					for epoch in range(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
																				__a			:       List[Any]					    =  model(**_SCREAMING_SNAKE_CASE   )
																				__a			:       str					    =  outputs.loss
																				__a			:       str					    =  loss / gradient_accumulation_steps
																				accelerator.backward(_SCREAMING_SNAKE_CASE   )
																				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   )   )   )
										__a			:       List[Any]					    =  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(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							():
					__a			:       int					    =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
					parser.add_argument(
					    '--model_name_or_path'						,      type=_SCREAMING_SNAKE_CASE						,      default='bert-base-cased'						,      help='Path to pretrained model or model identifier from huggingface.co/models.'						,      required=_SCREAMING_SNAKE_CASE						,      )
					parser.add_argument(
					    '--output_dir'						,      type=_SCREAMING_SNAKE_CASE						,      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=_SCREAMING_SNAKE_CASE						,      default=_SCREAMING_SNAKE_CASE						,      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=_SCREAMING_SNAKE_CASE						,      default=320						,      help='Number of training examples to use.'						,      )
					parser.add_argument(
					    '--n_val'						,      type=_SCREAMING_SNAKE_CASE						,      default=160						,      help='Number of validation examples to use.'						,      )
					parser.add_argument(
					    '--num_epochs'						,      type=_SCREAMING_SNAKE_CASE						,      default=1						,      help='Number of train epochs.'						,      )
					__a			:       List[str]					    =  parser.parse_args()
					__a			:       List[Any]					    =  {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
					training_function(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()



