






'''simple docstring'''







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

__lowerCAmelCase									=   random.Random()





def 		_UpperCAmelCase (				__A				: Union[str, Any]    ,		__A				: str=1.0    ,		__A				: Optional[int]=None    ,		__A				: Any=None   ):
				if rng is None:
								a_		:	Tuple									=   global_rng

				a_		:	List[Any]									=   []
				for batch_idx in range(shape[0]   ):
								values.append([]   )
								for _ in range(shape[1]   ):
												values[-1].append(rng.random() * scale   )

				return values





@require_torch
@require_torchaudio
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def __init__(			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      int=7	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=400	,    __SCREAMING_SNAKE_CASE   :      str=2000	,    __SCREAMING_SNAKE_CASE   :      Any=24	,    __SCREAMING_SNAKE_CASE   :      int=24	,    __SCREAMING_SNAKE_CASE   :      Tuple=0.0	,    __SCREAMING_SNAKE_CASE   :      List[Any]=1_6000	,    __SCREAMING_SNAKE_CASE   :      int=True	,    __SCREAMING_SNAKE_CASE   :      Any=True	,    )      ->      int:
							a_		:	str									=   parent
							a_		:	List[Any]									=   batch_size
							a_		:	List[str]									=   min_seq_length
							a_		:	str									=   max_seq_length
							a_		:	Union[str, Any]									=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							a_		:	Optional[Any]									=   feature_size
							a_		:	Optional[int]									=   num_mel_bins
							a_		:	Dict									=   padding_value
							a_		:	List[str]									=   sampling_rate
							a_		:	Any									=   return_attention_mask
							a_		:	Optional[int]									=   do_normalize






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      List[str]:
							return {
							    "feature_size": self.feature_size,
							    "num_mel_bins": self.num_mel_bins,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}







			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      str=False	,    __SCREAMING_SNAKE_CASE   :      Dict=False       )      ->      Tuple:
							def _flatten(__SCREAMING_SNAKE_CASE   :      Any       ):
											return list(itertools.chain(*__SCREAMING_SNAKE_CASE       )       )

							if equal_length:
											a_		:	Union[str, Any]									=   [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
							else:
											# make sure that inputs increase in size
											a_		:	Optional[Any]									=   [
											    floats_list((x, self.feature_size)       )
											    for x in range(self.min_seq_length	,    self.max_seq_length	,    self.seq_length_diff       )
											]
							if numpify:
											a_		:	Any									=   [np.asarray(__SCREAMING_SNAKE_CASE       ) for x in speech_inputs]
							return speech_inputs





@require_torch
@require_torchaudio
class 						SCREAMING_SNAKE_CASE       (       snake_case__						, unittest.TestCase							):
			snake_case__	=					SpeechaTextFeatureExtractor if is_speech_available() else None






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      str:
							a_		:	Tuple									=   SpeechaTextFeatureExtractionTester(self       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      List[str]       )      ->      List[Any]:
							self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE	,    axis=0       ) < 1e-3       )       )
							self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE	,    axis=0       ) - 1       ) < 1e-3       )       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[Any]:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							a_		:	Dict									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							# create three inputs of length 800, 1000, and 1200
							a_		:	List[Any]									=   [floats_list((1, x)       )[0] for x in range(800	,    1400	,    200       )]
							a_		:	str									=   [np.asarray(__SCREAMING_SNAKE_CASE       ) for speech_input in speech_inputs]

							# Test feature size
							a_		:	Union[str, Any]									=   feature_extractor(__SCREAMING_SNAKE_CASE	,    padding=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       ).input_features
							self.assertTrue(input_features.ndim == 3       )
							self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size       )

							# Test not batched input
							a_		:	Optional[Any]									=   feature_extractor(speech_inputs[0]	,    return_tensors='''np'''       ).input_features
							a_		:	Optional[Any]									=   feature_extractor(np_speech_inputs[0]	,    return_tensors='''np'''       ).input_features
							self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    atol=1e-3       )       )

							# Test batched
							a_		:	Tuple									=   feature_extractor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       ).input_features
							a_		:	str									=   feature_extractor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       ).input_features
							for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    atol=1e-3       )       )

							# Test 2-D numpy arrays are batched.
							a_		:	Union[str, Any]									=   [floats_list((1, x)       )[0] for x in (800, 800, 800)]
							a_		:	int									=   np.asarray(__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   feature_extractor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       ).input_features
							a_		:	Any									=   feature_extractor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       ).input_features
							for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    atol=1e-3       )       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      str:
							a_		:	List[Any]									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							a_		:	str									=   [floats_list((1, x)       )[0] for x in range(800	,    1400	,    200       )]

							a_		:	List[Any]									=   ['longest', 'max_length', 'do_not_pad']
							a_		:	Optional[Any]									=   [None, 16, None]
							for max_length, padding in zip(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											a_		:	str									=   feature_extractor(
											    __SCREAMING_SNAKE_CASE	,    padding=__SCREAMING_SNAKE_CASE	,    max_length=__SCREAMING_SNAKE_CASE	,    return_attention_mask=__SCREAMING_SNAKE_CASE       )
											a_		:	Tuple									=   inputs.input_features
											a_		:	Tuple									=   inputs.attention_mask
											a_		:	Optional[Any]									=   [np.sum(__SCREAMING_SNAKE_CASE       ) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Dict:
							a_		:	Optional[int]									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							a_		:	str									=   [floats_list((1, x)       )[0] for x in range(800	,    1400	,    200       )]

							a_		:	Optional[int]									=   ['longest', 'max_length', 'do_not_pad']
							a_		:	List[Any]									=   [None, 16, None]
							for max_length, padding in zip(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											a_		:	List[str]									=   feature_extractor(
											    __SCREAMING_SNAKE_CASE	,    max_length=__SCREAMING_SNAKE_CASE	,    padding=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''	,    return_attention_mask=__SCREAMING_SNAKE_CASE       )
											a_		:	List[str]									=   inputs.input_features
											a_		:	int									=   inputs.attention_mask
											a_		:	Optional[Any]									=   [np.sum(__SCREAMING_SNAKE_CASE       ) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6       )
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6       )
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      str:
							a_		:	List[str]									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							a_		:	Tuple									=   [floats_list((1, x)       )[0] for x in range(800	,    1400	,    200       )]
							a_		:	List[str]									=   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,    padding='''max_length'''	,    max_length=4	,    truncation=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''	,    return_attention_mask=__SCREAMING_SNAKE_CASE	,    )
							a_		:	str									=   inputs.input_features
							a_		:	List[str]									=   inputs.attention_mask
							a_		:	Union[str, Any]									=   np.sum(attention_mask == 1	,    axis=1       )

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
							self._check_zero_mean_unit_variance(input_features[1]       )
							self._check_zero_mean_unit_variance(input_features[2]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Dict:
							a_		:	Union[str, Any]									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							a_		:	Union[str, Any]									=   [floats_list((1, x)       )[0] for x in range(800	,    1400	,    200       )]
							a_		:	Optional[int]									=   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,    padding='''longest'''	,    max_length=4	,    truncation=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''	,    return_attention_mask=__SCREAMING_SNAKE_CASE	,    )
							a_		:	int									=   inputs.input_features
							a_		:	Optional[Any]									=   inputs.attention_mask
							a_		:	Optional[int]									=   np.sum(attention_mask == 1	,    axis=1       )

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
							self._check_zero_mean_unit_variance(input_features[2]       )

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape	,    (3, 4, 24)       )

							a_		:	Optional[int]									=   [floats_list((1, x)       )[0] for x in range(800	,    1400	,    200       )]
							a_		:	List[Any]									=   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,    padding='''longest'''	,    max_length=16	,    truncation=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''	,    return_attention_mask=__SCREAMING_SNAKE_CASE	,    )
							a_		:	Dict									=   inputs.input_features
							a_		:	Any									=   inputs.attention_mask
							a_		:	Any									=   np.sum(attention_mask == 1	,    axis=1       )

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
							self._check_zero_mean_unit_variance(input_features[2]       )

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape	,    (3, 6, 24)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[Any]:
							import torch

							a_		:	Optional[int]									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							a_		:	Union[str, Any]									=   np.random.rand(100	,    32       ).astype(np.floataa       )
							a_		:	Optional[Any]									=   np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											a_		:	int									=   feature_extractor.pad([{'''input_features''': inputs}]	,    return_tensors='''np'''       )
											self.assertTrue(np_processed.input_features.dtype == np.floataa       )
											a_		:	Dict									=   feature_extractor.pad([{'''input_features''': inputs}]	,    return_tensors='''pt'''       )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      Optional[Any]:
							from datasets import load_dataset

							a_		:	List[str]									=   load_dataset('''hf-internal-testing/librispeech_asr_dummy'''	,    '''clean'''	,    split='''validation'''       )
							# automatic decoding with librispeech
							a_		:	List[str]									=   ds.sort('''id'''       ).select(range(__SCREAMING_SNAKE_CASE       )       )[:num_samples]['audio']

							return [x["array"] for x in speech_samples]







			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      int:
							# fmt: off
							a_		:	Tuple									=   np.array([
							    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
							    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
							    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
							]       )
							# fmt: on

							a_		:	Optional[Any]									=   self._load_datasamples(1       )
							a_		:	List[str]									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							a_		:	str									=   feature_extractor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features
							self.assertEquals(input_features.shape	,    (1, 584, 24)       )
							self.assertTrue(np.allclose(input_features[0, 0, :30]	,    __SCREAMING_SNAKE_CASE	,    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


_lowercase		:  str			       =16
_lowercase		:  int			       =32


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__ = 16							,lowerCAmelCase__ = "bert-base-cased"						):
		lowerCamelCase_	:     Any 						=      AutoTokenizer.from_pretrained(lowerCAmelCase__						)
		lowerCamelCase_	:     Tuple 						=      load_dataset('glue'							,'mrpc'						)

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

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

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

		def collate_fn(lowerCAmelCase__						):
				# 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(lowerCAmelCase__							,padding='max_length'							,max_length=1_28							,return_tensors='pt'						)
				return tokenizer.pad(lowerCAmelCase__							,padding='longest'							,return_tensors='pt'						)

		# Instantiate dataloaders.
		lowerCamelCase_	:     int 						=      DataLoader(
		    tokenized_datasets['train']							,shuffle=lowerCAmelCase__							,collate_fn=lowerCAmelCase__							,batch_size=lowerCAmelCase__						)
		lowerCamelCase_	:     str 						=      DataLoader(
		    tokenized_datasets['validation']							,shuffle=lowerCAmelCase__							,collate_fn=lowerCAmelCase__							,batch_size=lowerCAmelCase__						)

		return train_dataloader, eval_dataloader


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

		lowerCamelCase_	:     Tuple 						=      metric.compute()
		return eval_metric["accuracy"]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		# Initialize accelerator
		lowerCamelCase_	:     List[Any] 						=      Accelerator()

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

		set_seed(lowerCAmelCase__						)
		lowerCamelCase_						,      lowerCamelCase_	:     List[Any] 						=      get_dataloaders(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)

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

		# Instantiate optimizer
		lowerCamelCase_	:     List[Any] 						=      (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCamelCase_	:     Union[str, Any] 						=      optimizer_cls(params=model.parameters()							,lr=lowerCAmelCase__						)

		if accelerator.state.deepspeed_plugin is not None:
				lowerCamelCase_	:     Tuple 						=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				lowerCamelCase_	:     Tuple 						=      1
		lowerCamelCase_	:     str 						=      (len(lowerCAmelCase__						) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCamelCase_	:     Tuple 						=      get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase__							,num_warmup_steps=0							,num_training_steps=lowerCAmelCase__							,)
		else:
				lowerCamelCase_	:     Optional[int] 						=      DummyScheduler(lowerCAmelCase__							,total_num_steps=lowerCAmelCase__							,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.
		lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      accelerator.prepare(
		    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)

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

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

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint						)
				lowerCamelCase_	:     List[Any] 						=      args.resume_from_checkpoint.split('epoch_'						)[1]
				lowerCamelCase_	:     str 						=      ''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowerCamelCase_	:     List[str] 						=      int(lowerCAmelCase__						) + 1
				lowerCamelCase_	:     Optional[Any] 						=      evaluation_loop(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
				accelerator.print('resumed checkpoint performance:'							,lowerCAmelCase__						)
				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:
						lowerCamelCase_	:     Union[str, Any] 						=      json.load(lowerCAmelCase__						)
						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
		lowerCamelCase_	:     str 						=      {}
		for epoch in range(lowerCAmelCase__							,lowerCAmelCase__						):
				model.train()
				for step, batch in enumerate(lowerCAmelCase__						):
						lowerCamelCase_	:     List[Any] 						=      model(**lowerCAmelCase__						)
						lowerCamelCase_	:     Tuple 						=      outputs.loss
						lowerCamelCase_	:     str 						=      loss / gradient_accumulation_steps
						accelerator.backward(lowerCAmelCase__						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

				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(lowerCAmelCase__							,lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'						)
		parser.add_argument(
		    '--model_name_or_path'							,type=lowerCAmelCase__							,default='bert-base-cased'							,help='Path to pretrained model or model identifier from huggingface.co/models.'							,required=lowerCAmelCase__							,)
		parser.add_argument(
		    '--output_dir'							,type=lowerCAmelCase__							,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=lowerCAmelCase__							,default=lowerCAmelCase__							,help='If the training should continue from a checkpoint folder.'							,)
		parser.add_argument(
		    '--partial_train_epoch'							,type=lowerCAmelCase__							,default=lowerCAmelCase__							,help='If passed, the training will stop after this number of epochs.'							,)
		parser.add_argument(
		    '--num_epochs'							,type=lowerCAmelCase__							,default=2							,help='Number of train epochs.'							,)
		lowerCamelCase_	:     Optional[Any] 						=      parser.parse_args()
		lowerCamelCase_	:     List[Any] 						=      {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(lowerCAmelCase__							,lowerCAmelCase__						)


if __name__ == "__main__":
		main()
