
"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDebertaVaForMaskedLM,
     TFDebertaVaForQuestionAnswering,
     TFDebertaVaForSequenceClassification,
     TFDebertaVaForTokenClassification,
     TFDebertaVaModel,
 )





class 				a__					:



     def __init__(							self :Optional[Any]      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :Union[str, Any]=13      ,  _lowerCamelCase :Optional[int]=7      ,  _lowerCamelCase :List[Any]=True      ,  _lowerCamelCase :Union[str, Any]=True      ,  _lowerCamelCase :Dict=True      ,  _lowerCamelCase :List[str]=True      ,  _lowerCamelCase :Union[str, Any]=99      ,  _lowerCamelCase :List[str]=32      ,  _lowerCamelCase :int=2      ,  _lowerCamelCase :int=4      ,  _lowerCamelCase :Any=37      ,  _lowerCamelCase :Union[str, Any]="gelu"      ,  _lowerCamelCase :List[Any]=0.1      ,  _lowerCamelCase :Any=0.1      ,  _lowerCamelCase :str=512      ,  _lowerCamelCase :Tuple=16      ,  _lowerCamelCase :Union[str, Any]=2      ,  _lowerCamelCase :Any=0.02      ,  _lowerCamelCase :Optional[int]=False      ,  _lowerCamelCase :Union[str, Any]=True      ,  _lowerCamelCase :Optional[int]="None"      ,  _lowerCamelCase :Optional[Any]=3      ,  _lowerCamelCase :Dict=4      ,  _lowerCamelCase :int=None      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=parent
      UpperCamelCase_     :					str   	=batch_size
      UpperCamelCase_     :					Tuple   	=seq_length
      UpperCamelCase_     :					Tuple   	=is_training
      UpperCamelCase_     :					Optional[Any]   	=use_input_mask
      UpperCamelCase_     :					Any   	=use_token_type_ids
      UpperCamelCase_     :					Optional[Any]   	=use_labels
      UpperCamelCase_     :					int   	=vocab_size
      UpperCamelCase_     :					Tuple   	=hidden_size
      UpperCamelCase_     :					List[Any]   	=num_hidden_layers
      UpperCamelCase_     :					List[str]   	=num_attention_heads
      UpperCamelCase_     :					Any   	=intermediate_size
      UpperCamelCase_     :					Dict   	=hidden_act
      UpperCamelCase_     :					str   	=hidden_dropout_prob
      UpperCamelCase_     :					Optional[Any]   	=attention_probs_dropout_prob
      UpperCamelCase_     :					int   	=max_position_embeddings
      UpperCamelCase_     :					Tuple   	=type_vocab_size
      UpperCamelCase_     :					Any   	=type_sequence_label_size
      UpperCamelCase_     :					str   	=initializer_range
      UpperCamelCase_     :					Union[str, Any]   	=num_labels
      UpperCamelCase_     :					str   	=num_choices
      UpperCamelCase_     :					List[str]   	=relative_attention
      UpperCamelCase_     :					Dict   	=position_biased_input
      UpperCamelCase_     :					List[Any]   	=pos_att_type
      UpperCamelCase_     :					Tuple   	=scope



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size   )

      UpperCamelCase_     :					Dict   	=None
      if self.use_input_mask:
       UpperCamelCase_     :					Union[str, Any]   	=random_attention_mask([self.batch_size, self.seq_length]   )

      UpperCamelCase_     :					List[Any]   	=None
      if self.use_token_type_ids:
       UpperCamelCase_     :					str   	=ids_tensor([self.batch_size, self.seq_length]      ,  self.type_vocab_size   )

      UpperCamelCase_     :					Dict   	=None
      UpperCamelCase_     :					int   	=None
      UpperCamelCase_     :					List[str]   	=None
      if self.use_labels:
       UpperCamelCase_     :					int   	=ids_tensor([self.batch_size]      ,  self.type_sequence_label_size   )
       UpperCamelCase_     :					Optional[Any]   	=ids_tensor([self.batch_size, self.seq_length]      ,  self.num_labels   )

      UpperCamelCase_     :					List[Any]   	=DebertaVaConfig(
          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      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,  max_position_embeddings=self.max_position_embeddings      ,  type_vocab_size=self.type_vocab_size      ,  relative_attention=self.relative_attention      ,  position_biased_input=self.position_biased_input      ,  initializer_range=self.initializer_range      ,  return_dict=lowercase__      ,  )

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



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :Optional[int]      ,  _lowerCamelCase :Optional[int]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=TFDebertaVaModel(config=lowercase__   )
      UpperCamelCase_     :					Optional[Any]   	={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

      UpperCamelCase_     :					Any   	=[input_ids, input_mask]
      UpperCamelCase_     :					Optional[int]   	=model(lowercase__   )

      UpperCamelCase_     :					List[str]   	=model(lowercase__   )

      self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)   )



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :Union[str, Any]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=TFDebertaVaForMaskedLM(config=lowercase__   )
      UpperCamelCase_     :					List[Any]   	={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }
      UpperCamelCase_     :					List[Any]   	=model(lowercase__   )
      self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.vocab_size)   )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :int      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :int      ,  _lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=self.num_labels
      UpperCamelCase_     :					Union[str, Any]   	=TFDebertaVaForSequenceClassification(config=lowercase__   )
      UpperCamelCase_     :					str   	={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }

      UpperCamelCase_     :					Optional[int]   	=model(lowercase__   )
      self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)   )



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self.num_labels
      UpperCamelCase_     :					str   	=TFDebertaVaForTokenClassification(config=lowercase__   )
      UpperCamelCase_     :					Dict   	={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }
      UpperCamelCase_     :					int   	=model(lowercase__   )
      self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.num_labels)   )



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=TFDebertaVaForQuestionAnswering(config=lowercase__   )
      UpperCamelCase_     :					str   	={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }

      UpperCamelCase_     :					Tuple   	=model(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  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=self.prepare_config_and_inputs()
      (
          UpperCamelCase_      
      )     :					str   	=config_and_inputs
      UpperCamelCase_     :					int   	={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
      return config, inputs_dict





@require_tf
class 				a__					(						_UpperCamelCase   ,							_UpperCamelCase   ,							unittest.TestCase       ):
     UpperCAmelCase__         =      (
         (
             TFDebertaVaModel,
             TFDebertaVaForMaskedLM,
             TFDebertaVaForQuestionAnswering,
             TFDebertaVaForSequenceClassification,
             TFDebertaVaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     UpperCAmelCase__         =      (
         {
             '''feature-extraction''': TFDebertaVaModel,
             '''fill-mask''': TFDebertaVaForMaskedLM,
             '''question-answering''': TFDebertaVaForQuestionAnswering,
             '''text-classification''': TFDebertaVaForSequenceClassification,
             '''token-classification''': TFDebertaVaForTokenClassification,
             '''zero-shot''': TFDebertaVaForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      False



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=TFDebertaVaModelTester(self   )
      UpperCamelCase_     :					Tuple   	=ConfigTester(self      ,  config_class=lowercase__      ,  hidden_size=37   )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      self.config_tester.run_common_tests()



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase__   )



     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowercase__   )



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowercase__   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowercase__   )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowercase__   )




     @slow
     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'   )
      self.assertIsNotNone(lowercase__   )





@require_tf
class 				a__					(						unittest.TestCase       ):



     @unittest.skip(reason='Model not available yet'   )
     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      pass




     @slow
     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'   )
      UpperCamelCase_     :					Union[str, Any]   	=tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   )
      UpperCamelCase_     :					Tuple   	=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
      UpperCamelCase_     :					List[str]   	=model(lowercase__      ,  attention_mask=lowercase__   )[0]

      UpperCamelCase_     :					List[Any]   	=tf.constant(
          [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]   )
      tf.debugging.assert_near(output[:, 1:4, 1:4]      ,  lowercase__      ,  atol=1E-4   )




import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase       		=    16
lowerCAmelCase       		=    32




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 16      ,       lowercase_ = "bert-base-cased"      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							List[str]	    =	AutoTokenizer.from_pretrained(lowercase_      )
						__UpperCAmelCase			:							int	    =	load_dataset('''glue'''      ,       '''mrpc'''      )

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

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

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

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

						# Instantiate dataloaders.
						__UpperCAmelCase			:							Optional[Any]	    =	DataLoader(
						    tokenized_datasets['''train''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )
						__UpperCAmelCase			:							Any	    =	DataLoader(
						    tokenized_datasets['''validation''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )

						return train_dataloader, eval_dataloader




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     str:




						'''simple docstring'''







						model.eval()
						__UpperCAmelCase			:							Optional[int]	    =	0
						for step, batch in enumerate(lowercase_      ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device      )
												with torch.no_grad():
																		__UpperCAmelCase			:							Any	    =	model(**lowercase_      )
												__UpperCAmelCase			:							Optional[int]	    =	outputs.logits.argmax(dim=-1      )
												# It is slightly faster to call this once, than multiple times
												__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	accelerator.gather(
												    (predictions, batch['''labels'''])      )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(lowercase_      ) - 1:
																								__UpperCAmelCase			:							Optional[int]	    =	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																								__UpperCAmelCase			:							List[Any]	    =	references[: len(eval_dataloader.dataset      ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=lowercase_      ,       references=lowercase_      ,       )

						__UpperCAmelCase			:							Union[str, Any]	    =	metric.compute()
						return eval_metric["accuracy"]




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	Accelerator()

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

						set_seed(lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	get_dataloaders(lowercase_      ,       lowercase_      ,       lowercase_      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCAmelCase			:							List[str]	    =	AutoModelForSequenceClassification.from_pretrained(lowercase_      ,       return_dict=lowercase_      )

						# Instantiate optimizer
						__UpperCAmelCase			:							str	    =	(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__UpperCAmelCase			:							int	    =	optimizer_cls(params=model.parameters()      ,       lr=lowercase_      )

						if accelerator.state.deepspeed_plugin is not None:
												__UpperCAmelCase			:							int	    =	accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												__UpperCAmelCase			:							List[Any]	    =	1
						__UpperCAmelCase			:							str	    =	(len(lowercase_      ) * num_epochs) // gradient_accumulation_steps

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

						# We need to keep track of how many total steps we have iterated over
						__UpperCAmelCase			:							Tuple	    =	0
						# We also need to keep track of the stating epoch so files are named properly
						__UpperCAmelCase			:							Any	    =	0
						__UpperCAmelCase			:							Tuple	    =	evaluate.load('''glue'''      ,       '''mrpc'''      )
						__UpperCAmelCase			:							Any	    =	num_epochs

						if args.partial_train_epoch is not None:
												__UpperCAmelCase			:							Any	    =	args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint      )
												__UpperCAmelCase			:							List[Any]	    =	args.resume_from_checkpoint.split('''epoch_'''      )[1]
												__UpperCAmelCase			:							Union[str, Any]	    =	''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												__UpperCAmelCase			:							Tuple	    =	int(lowercase_      ) + 1
												__UpperCAmelCase			:							Optional[int]	    =	evaluation_loop(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )
												accelerator.print('''resumed checkpoint performance:'''      ,       lowercase_      )
												accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''      ,       lr_scheduler.get_lr()[0]      )
												accelerator.print('''resumed optimizers\'s lr:'''      ,       optimizer.param_groups[0]['''lr''']      )
												with open(os.path.join(args.output_dir      ,       f"state_{starting_epoch-1}.json"      )      ,       '''r'''      ) as f:
																		__UpperCAmelCase			:							str	    =	json.load(lowercase_      )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						__UpperCAmelCase			:							Tuple	    =	{}
						for epoch in range(lowercase_      ,       lowercase_      ):
												model.train()
												for step, batch in enumerate(lowercase_      ):
																		__UpperCAmelCase			:							Dict	    =	model(**lowercase_      )
																		__UpperCAmelCase			:							Union[str, Any]	    =	outputs.loss
																		__UpperCAmelCase			:							Tuple	    =	loss / gradient_accumulation_steps
																		accelerator.backward(lowercase_      )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												__UpperCAmelCase			:							Optional[Any]	    =	f"epoch_{epoch}"
												__UpperCAmelCase			:							Optional[Any]	    =	os.path.join(args.output_dir      ,       lowercase_      )
												accelerator.save_state(lowercase_      )
												__UpperCAmelCase			:							Any	    =	evaluation_loop(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )
												__UpperCAmelCase			:							List[str]	    =	accuracy
												__UpperCAmelCase			:							Dict	    =	lr_scheduler.get_lr()[0]
												__UpperCAmelCase			:							List[str]	    =	optimizer.param_groups[0]['''lr''']
												__UpperCAmelCase			:							List[Any]	    =	epoch
												__UpperCAmelCase			:							Any	    =	overall_step
												accelerator.print(f"epoch {epoch}:"      ,       lowercase_      )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir      ,       f"state_{epoch}.json"      )      ,       '''w'''      ) as f:
																								json.dump(lowercase_      ,       lowercase_      )




def     __SCREAMING_SNAKE_CASE							(       )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Any	    =	argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
						parser.add_argument(
						    '''--model_name_or_path'''      ,       type=lowercase_      ,       default='''bert-base-cased'''      ,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''      ,       required=lowercase_      ,       )
						parser.add_argument(
						    '''--output_dir'''      ,       type=lowercase_      ,       default='''.'''      ,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''      ,       )
						parser.add_argument(
						    '''--resume_from_checkpoint'''      ,       type=lowercase_      ,       default=lowercase_      ,       help='''If the training should continue from a checkpoint folder.'''      ,       )
						parser.add_argument(
						    '''--partial_train_epoch'''      ,       type=lowercase_      ,       default=lowercase_      ,       help='''If passed, the training will stop after this number of epochs.'''      ,       )
						parser.add_argument(
						    '''--num_epochs'''      ,       type=lowercase_      ,       default=2      ,       help='''Number of train epochs.'''      ,       )
						__UpperCAmelCase			:							Optional[int]	    =	parser.parse_args()
						__UpperCAmelCase			:							str	    =	{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

						training_function(lowercase_      ,       lowercase_      )


if __name__ == "__main__":
				main()

