



"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import 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 (
							    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFXLMForMultipleChoice,
							    TFXLMForQuestionAnsweringSimple,
							    TFXLMForSequenceClassification,
							    TFXLMForTokenClassification,
							    TFXLMModel,
							    TFXLMWithLMHeadModel,
							    XLMConfig,
							)






class   UpperCAmelCase__		:



		def __init__(			self	:     Optional[int]   ,				snake_case	:     Tuple   ,				)      ->    Tuple:






									'''simple docstring'''

									A    =       parent
									A    =       13
									A    =       7
									A    =       True
									A    =       True
									A    =       True
									A    =       True
									A    =       True
									A    =       False
									A    =       False
									A    =       False
									A    =       2
									A    =       99
									A    =       0
									A    =       32
									A    =       2
									A    =       4
									A    =       0.1
									A    =       0.1
									A    =       512
									A    =       16
									A    =       2
									A    =       0.02
									A    =       3
									A    =       4
									A    =       'last'
									A    =       True
									A    =       None
									A    =       0



		def        A_		(			self	:     int					)      ->    str:






									'''simple docstring'''

									A    =       ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size					)
									A    =       random_attention_mask([self.batch_size, self.seq_length]   ,				dtype=tf.floataa					)

									A    =       None
									if self.use_input_lengths:
																A    =       (
																    ids_tensor([self.batch_size]   ,				vocab_size=2					) + self.seq_length - 2
																)  # small variation of seq_length

									A    =       None
									if self.use_token_type_ids:
																A    =       ids_tensor([self.batch_size, self.seq_length]   ,				self.n_langs					)

									A    =       None
									A    =       None
									A    =       None
									if self.use_labels:
																A    =       ids_tensor([self.batch_size]   ,				self.type_sequence_label_size					)
																A    =       ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels					)
																A    =       ids_tensor([self.batch_size]   ,				2   ,				dtype=tf.floataa					)
																A    =       ids_tensor([self.batch_size]   ,				self.num_choices					)

									A    =       XLMConfig(
									    vocab_size=self.vocab_size   ,				n_special=self.n_special   ,				emb_dim=self.hidden_size   ,				n_layers=self.num_hidden_layers   ,				n_heads=self.num_attention_heads   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				gelu_activation=self.gelu_activation   ,				sinusoidal_embeddings=self.sinusoidal_embeddings   ,				asm=self.asm   ,				causal=self.causal   ,				n_langs=self.n_langs   ,				max_position_embeddings=self.max_position_embeddings   ,				initializer_range=self.initializer_range   ,				summary_type=self.summary_type   ,				use_proj=self.use_proj   ,				bos_token_id=self.bos_token_id   ,				)

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)



		def        A_		(			self	:     str   ,				snake_case	:     int   ,				snake_case	:     Any   ,				snake_case	:     Optional[int]   ,				snake_case	:     Optional[Any]   ,				snake_case	:     int   ,				snake_case	:     List[Any]   ,				snake_case	:     int   ,				snake_case	:     List[str]   ,				snake_case	:     List[Any]   ,				)      ->    List[str]:






									'''simple docstring'''

									A    =       TFXLMModel(config=snake_case					)
									A    =       {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
									A    =       model(snake_case					)

									A    =       [input_ids, input_mask]
									A    =       model(snake_case					)
									self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)					)



		def        A_		(			self	:     Optional[int]   ,				snake_case	:     Optional[Any]   ,				snake_case	:     int   ,				snake_case	:     List[str]   ,				snake_case	:     Optional[Any]   ,				snake_case	:     Tuple   ,				snake_case	:     List[str]   ,				snake_case	:     List[str]   ,				snake_case	:     List[str]   ,				snake_case	:     Optional[Any]   ,				)      ->    Dict:






									'''simple docstring'''

									A    =       TFXLMWithLMHeadModel(snake_case					)

									A    =       {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
									A    =       model(snake_case					)

									A    =       outputs

									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)					)



		def        A_		(			self	:     Optional[int]   ,				snake_case	:     List[Any]   ,				snake_case	:     Optional[Any]   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Optional[Any]   ,				snake_case	:     Dict   ,				snake_case	:     Tuple   ,				snake_case	:     Dict   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Optional[Any]   ,				)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       TFXLMForQuestionAnsweringSimple(snake_case					)

									A    =       {'input_ids': input_ids, 'lengths': input_lengths}

									A    =       model(snake_case					)

									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        A_		(			self	:     List[Any]   ,				snake_case	:     Dict   ,				snake_case	:     Any   ,				snake_case	:     Any   ,				snake_case	:     int   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[int]   ,				snake_case	:     Dict   ,				snake_case	:     str   ,				snake_case	:     Union[str, Any]   ,				)      ->    int:






									'''simple docstring'''

									A    =       TFXLMForSequenceClassification(snake_case					)

									A    =       {'input_ids': input_ids, 'lengths': input_lengths}

									A    =       model(snake_case					)

									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     str   ,				snake_case	:     Any   ,				snake_case	:     Any   ,				snake_case	:     Tuple   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Dict   ,				snake_case	:     Optional[int]   ,				snake_case	:     List[Any]   ,				snake_case	:     str   ,				)      ->    List[Any]:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       TFXLMForTokenClassification(config=snake_case					)
									A    =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
									A    =       model(snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)					)



		def        A_		(			self	:     Dict   ,				snake_case	:     Dict   ,				snake_case	:     List[Any]   ,				snake_case	:     List[str]   ,				snake_case	:     Optional[int]   ,				snake_case	:     str   ,				snake_case	:     Any   ,				snake_case	:     str   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     str   ,				)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       self.num_choices
									A    =       TFXLMForMultipleChoice(config=snake_case					)
									A    =       tf.tile(tf.expand_dims(snake_case   ,				1					)   ,				(1, self.num_choices, 1)					)
									A    =       tf.tile(tf.expand_dims(snake_case   ,				1					)   ,				(1, self.num_choices, 1)					)
									A    =       tf.tile(tf.expand_dims(snake_case   ,				1					)   ,				(1, self.num_choices, 1)					)
									A    =       {
									    'input_ids': multiple_choice_inputs_ids,
									    'attention_mask': multiple_choice_input_mask,
									    'token_type_ids': multiple_choice_token_type_ids,
									}
									A    =       model(snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)					)



		def        A_		(			self	:     Union[str, Any]					)      ->    int:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									(
									    (
									    A
									)   ,	(
									    A
									)   ,	(
									    A
									)   ,	(
									    A
									)   ,	(
									    A
									)   ,	(
									    A
									)   ,	(
									    A
									)   ,	(
									    A
									)   ,	(
									    A
									)   ,	
									)    =       config_and_inputs
									A    =       {
									    'input_ids': input_ids,
									    'token_type_ids': token_type_ids,
									    'langs': token_type_ids,
									    'lengths': input_lengths,
									}
									return config, inputs_dict






@require_tf
class   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						Dict							=  (
		    (
		        TFXLMModel,
		        TFXLMWithLMHeadModel,
		        TFXLMForSequenceClassification,
		        TFXLMForQuestionAnsweringSimple,
		        TFXLMForTokenClassification,
		        TFXLMForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		lowerCAmelCase_							:						Union[str, Any]							=  (
		    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
		)  # TODO (PVP): Check other models whether language generation is also applicable
		lowerCAmelCase_							:						Tuple							=  (
		    {
		        """feature-extraction""": TFXLMModel,
		        """fill-mask""": TFXLMWithLMHeadModel,
		        """question-answering""": TFXLMForQuestionAnsweringSimple,
		        """text-classification""": TFXLMForSequenceClassification,
		        """text-generation""": TFXLMWithLMHeadModel,
		        """token-classification""": TFXLMForTokenClassification,
		        """zero-shot""": TFXLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCAmelCase_							:						List[str]							=  False
		lowerCAmelCase_							:						Optional[int]							=  False



		def        A_		(			self	:     Tuple   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Tuple   ,				snake_case	:     List[str]   ,				snake_case	:     Tuple   ,				snake_case	:     Union[str, Any]					)      ->    List[str]:






									'''simple docstring'''

									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith('Fast'					)
									):
																# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																return True

									return False



		def        A_		(			self	:     Union[str, Any]					)      ->    int:






									'''simple docstring'''

									A    =       TFXLMModelTester(self					)
									A    =       ConfigTester(self   ,				config_class=snake_case   ,				emb_dim=37					)



		def        A_		(			self	:     Dict					)      ->    str:






									'''simple docstring'''

									self.config_tester.run_common_tests()



		def        A_		(			self	:     int					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_model(*snake_case					)



		def        A_		(			self	:     List[str]					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_lm_head(*snake_case					)



		def        A_		(			self	:     Tuple					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_qa(*snake_case					)



		def        A_		(			self	:     List[Any]					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_sequence_classif(*snake_case					)



		def        A_		(			self	:     Dict					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_token_classification(*snake_case					)



		def        A_		(			self	:     Optional[Any]					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case					)



		@slow
		def        A_		(			self	:     List[Any]					)      ->    str:






									'''simple docstring'''

									for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																A    =       TFXLMModel.from_pretrained(snake_case					)
																self.assertIsNotNone(snake_case					)






@require_tf
class   UpperCAmelCase__		(   unittest.TestCase  ):



		@slow
		def        A_		(			self	:     Optional[Any]					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'					)
									A    =       tf.convert_to_tensor([[14, 447]]   ,				dtype=tf.intaa					)  # the president
									A    =       [
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									]  # the president the president the president the president the president the president the president the president the president the president
									# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
									A    =       model.generate(snake_case   ,				do_sample=snake_case					)
									self.assertListEqual(output_ids[0].numpy().tolist()   ,				snake_case					)








"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

A     =      1_6
A     =      3_2

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__ = 16      )			->				List[Any]:
							A    =       AutoTokenizer.from_pretrained('bert-base-cased'      )
							A    =       load_dataset('glue'					,   'mrpc'      )

							def tokenize_function(lowerCamelCase__      ):
														# max_length=None => use the model max length (it's actually the default)
														A    =       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
							# starting with the main process first:
							with accelerator.main_process_first():
														A    =       datasets.map(
														    lowerCamelCase__					,   batched=lowerCamelCase__					,   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
							A    =       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.
														A    =       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":
																					A    =       16
														elif accelerator.mixed_precision != "no":
																					A    =       8
														else:
																					A    =       None

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

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

							return train_dataloader, eval_dataloader


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

							A     =      mocked_dataloaders  # noqa: F811

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				List[str]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'					,   lowerCamelCase__      ) == "1":
														A    =       2
							# Initialize accelerator
							A    =       Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision      )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							A    =       config['lr']
							A    =       int(config['num_epochs']      )
							A    =       int(config['seed']      )
							A    =       int(config['batch_size']      )

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps					,   'isdigit'      ):
														if args.checkpointing_steps == "epoch":
																					A    =       args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A    =       int(args.checkpointing_steps      )
														else:
																					raise ValueError(
																					    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
							else:
														A    =       None

							set_seed(lowerCamelCase__      )

							A   ,	A    =       get_dataloaders(lowerCamelCase__					,   lowerCamelCase__      )
							A    =       evaluate.load('glue'					,   'mrpc'      )

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

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A    =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,   return_dict=lowerCamelCase__      )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							A    =       model.to(accelerator.device      )

							# Instantiate optimizer
							A    =       AdamW(params=model.parameters()					,   lr=lowerCamelCase__      )

							# Instantiate scheduler
							A    =       get_linear_schedule_with_warmup(
							    optimizer=lowerCamelCase__					,   num_warmup_steps=100					,   num_training_steps=(len(lowerCamelCase__      ) * num_epochs) // gradient_accumulation_steps					,   )

							# 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    =       accelerator.prepare(
							    lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							A    =       0
							# We also need to keep track of the stating epoch so files are named properly
							A    =       0

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							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      )
																					A    =       os.path.basename(args.resume_from_checkpoint      )
														else:
																					# Get the most recent checkpoint
																					A    =       [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime      )
																					A    =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A    =       os.path.splitext(lowerCamelCase__      )[0]

														if "epoch" in training_difference:
																					A    =       int(training_difference.replace('epoch_'					,   ''      )      ) + 1
																					A    =       None
														else:
																					A    =       int(training_difference.replace('step_'					,   ''      )      )
																					A    =       resume_step // len(lowerCamelCase__      )
																					resume_step -= starting_epoch * len(lowerCamelCase__      )

    # Now we train the model
							for epoch in range(lowerCamelCase__					,   lowerCamelCase__      ):
														model.train()
														# New Code #
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					A    =       accelerator.skip_first_batches(lowerCamelCase__					,   lowerCamelCase__      )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A    =       train_dataloader
														for step, batch in enumerate(lowerCamelCase__      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device      )
																					A    =       model(**lowerCamelCase__      )
																					A    =       outputs.loss
																					A    =       loss / gradient_accumulation_steps
																					accelerator.backward(lowerCamelCase__      )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(lowerCamelCase__					,   lowerCamelCase__      ):
																												A    =       f"""step_{overall_step}"""
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A    =       os.path.join(args.output_dir					,   lowerCamelCase__      )
																																			accelerator.save_state(lowerCamelCase__      )

														model.eval()
														for step, batch in enumerate(lowerCamelCase__      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																					batch.to(accelerator.device      )
																					with torch.no_grad():
																												A    =       model(**lowerCamelCase__      )
																					A    =       outputs.logits.argmax(dim=-1      )
																					A   ,	A    =       accelerator.gather_for_metrics((predictions, batch['labels'])      )
																					metric.add_batch(
																					    predictions=lowerCamelCase__					,   references=lowerCamelCase__					,   )

														A    =       metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""					,   lowerCamelCase__      )

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					A    =       f"""epoch_{epoch}"""
																					if args.output_dir is not None:
																												A    =       os.path.join(args.output_dir					,   lowerCamelCase__      )
																					accelerator.save_state(lowerCamelCase__      )

def 	lowerCAmelCase__      (  )			->				Optional[int]:
							A    =       argparse.ArgumentParser(description='Simple example of training script.'      )
							parser.add_argument(
							    '--mixed_precision'					,   type=lowerCamelCase__					,   default=lowerCamelCase__					,   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=lowerCamelCase__					,   default=lowerCamelCase__					,   help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,   )
							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.'					,   )
							A    =       parser.parse_args()
							A    =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(lowerCamelCase__					,   lowerCamelCase__      )


if __name__ == "__main__":
							main()



