





import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

UpperCAmelCase__  :	List[str]     =logging.get_logger(__name__)

UpperCAmelCase__  :	Any     ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

UpperCAmelCase__  :	Dict     ={
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

UpperCAmelCase__  :	Optional[int]     ={
    '''microsoft/deberta-base''': 5_12,
    '''microsoft/deberta-large''': 5_12,
    '''microsoft/deberta-xlarge''': 5_12,
    '''microsoft/deberta-base-mnli''': 5_12,
    '''microsoft/deberta-large-mnli''': 5_12,
    '''microsoft/deberta-xlarge-mnli''': 5_12,
}

UpperCAmelCase__  :	List[Any]     ={
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}





def    _lowercase    (  )  ->     Optional[Any]:
				lowerCamelCase		       =(
				    list(range(ord("""!""" )	,					ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )	,					ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )	,					ord("""ÿ""" ) + 1 ) )
				)
				lowerCamelCase		       =bs[:]
				lowerCamelCase		       =0
				for b in range(2**8 ):
								if b not in bs:
												bs.append(_UpperCAmelCase )
												cs.append(2**8 + n )
												n += 1
				lowerCamelCase		       =[chr(_UpperCAmelCase ) for n in cs]
				return dict(zip(_UpperCAmelCase	,					_UpperCAmelCase ) )





def    _lowercase    (  _UpperCAmelCase )  ->     Union[str, Any]:
				lowerCamelCase		       =set()
				lowerCamelCase		       =word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								lowerCamelCase		       =char
				return pairs




class 					__A   (  a			):
				__A            = VOCAB_FILES_NAMES
				__A            = PRETRAINED_VOCAB_FILES_MAP
				__A            = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A            = ["""input_ids""", """attention_mask""", """token_type_ids"""]


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_="replace"      ,       UpperCAmelCase_="[CLS]"      ,       UpperCAmelCase_="[SEP]"      ,       UpperCAmelCase_="[SEP]"      ,       UpperCAmelCase_="[CLS]"      ,       UpperCAmelCase_="[UNK]"      ,       UpperCAmelCase_="[PAD]"      ,       UpperCAmelCase_="[MASK]"      ,       UpperCAmelCase_=False      ,       UpperCAmelCase_=False      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else bos_token
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else eos_token
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else sep_token
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else cls_token
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else unk_token
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else mask_token

								super().__init__(
								    errors=UpperCAmelCase_      ,       bos_token=UpperCAmelCase_      ,       eos_token=UpperCAmelCase_      ,       unk_token=UpperCAmelCase_      ,       sep_token=UpperCAmelCase_      ,       cls_token=UpperCAmelCase_      ,       pad_token=UpperCAmelCase_      ,       mask_token=UpperCAmelCase_      ,       add_prefix_space=UpperCAmelCase_      ,       add_bos_token=UpperCAmelCase_      ,       **UpperCAmelCase_      ,       )
								lowerCamelCase		       =add_bos_token

								with open(UpperCAmelCase_      ,       encoding="""utf-8"""							) as vocab_handle:
												lowerCamelCase		       =json.load(UpperCAmelCase_							)
								lowerCamelCase		       ={v: k for k, v in self.encoder.items()}
								lowerCamelCase		       =errors  # how to handle errors in decoding
								lowerCamelCase		       =bytes_to_unicode()
								lowerCamelCase		       ={v: k for k, v in self.byte_encoder.items()}
								with open(UpperCAmelCase_      ,       encoding="""utf-8"""							) as merges_handle:
												lowerCamelCase		       =merges_handle.read().split("""\n"""							)[1:-1]
								lowerCamelCase		       =[tuple(merge.split()							) for merge in bpe_merges]
								lowerCamelCase		       =dict(zip(UpperCAmelCase_      ,       range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase		       ={}
								lowerCamelCase		       =add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								lowerCamelCase		       =re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


				@property
				# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
				def _snake_case				(						self							):
								return len(self.encoder							)


				def _snake_case				(						self							):
								return dict(self.encoder      ,       **self.added_tokens_encoder							)


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								if token in self.cache:
												return self.cache[token]
								lowerCamelCase		       =tuple(UpperCAmelCase_							)
								lowerCamelCase		       =get_pairs(UpperCAmelCase_							)

								if not pairs:
												return token

								while True:
												lowerCamelCase		       =min(UpperCAmelCase_      ,       key=lambda UpperCAmelCase_							: self.bpe_ranks.get(UpperCAmelCase_      ,       float("""inf"""							)							)							)
												if bigram not in self.bpe_ranks:
																break
												lowerCamelCase       ,     lowerCamelCase		       =bigram
												lowerCamelCase		       =[]
												lowerCamelCase		       =0
												while i < len(UpperCAmelCase_							):
																try:
																				lowerCamelCase		       =word.index(UpperCAmelCase_      ,       UpperCAmelCase_							)
																except ValueError:
																				new_word.extend(word[i:]							)
																				break
																else:
																				new_word.extend(word[i:j]							)
																				lowerCamelCase		       =j

																if word[i] == first and i < len(UpperCAmelCase_							) - 1 and word[i + 1] == second:
																				new_word.append(first + second							)
																				i += 2
																else:
																				new_word.append(word[i]							)
																				i += 1
												lowerCamelCase		       =tuple(UpperCAmelCase_							)
												lowerCamelCase		       =new_word
												if len(UpperCAmelCase_							) == 1:
																break
												else:
																lowerCamelCase		       =get_pairs(UpperCAmelCase_							)
								lowerCamelCase		       =""" """.join(UpperCAmelCase_							)
								lowerCamelCase		       =word
								return word


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase		       =[self.cls_token_id]
								lowerCamelCase		       =[self.sep_token_id]
								return cls + token_ids_a + sep + token_ids_a + sep


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = False							):
								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=UpperCAmelCase_      ,       token_ids_a=UpperCAmelCase_      ,       already_has_special_tokens=UpperCAmelCase_							)

								if token_ids_a is None:
												return [1] + ([0] * len(UpperCAmelCase_							)) + [1]
								return [1] + ([0] * len(UpperCAmelCase_							)) + [1] + ([0] * len(UpperCAmelCase_							)) + [1]


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								lowerCamelCase		       =[self.sep_token_id]
								lowerCamelCase		       =[self.cls_token_id]

								if token_ids_a is None:
												return len(cls + token_ids_a + sep							) * [0]
								return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =[]
								for token in re.findall(self.pat      ,       UpperCAmelCase_							):
												lowerCamelCase		       ="""""".join(
												    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase_							).split(""" """							)							)
								return bpe_tokens


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								return self.encoder.get(UpperCAmelCase_      ,       self.encoder.get(self.unk_token							)							)


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								return self.decoder.get(UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       ="""""".join(UpperCAmelCase_							)
								lowerCamelCase		       =bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""      ,       errors=self.errors							)
								return text


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								if not os.path.isdir(UpperCAmelCase_							):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
												return
								lowerCamelCase		       =os.path.join(
								    UpperCAmelCase_      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase		       =os.path.join(
								    UpperCAmelCase_      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

								with open(UpperCAmelCase_      ,       """w"""      ,       encoding="""utf-8"""							) as f:
												f.write(json.dumps(self.encoder      ,       indent=2      ,       sort_keys=UpperCAmelCase_      ,       ensure_ascii=UpperCAmelCase_							) + """\n"""							)

								lowerCamelCase		       =0
								with open(UpperCAmelCase_      ,       """w"""      ,       encoding="""utf-8"""							) as writer:
												writer.write("""#version: 0.2\n"""							)
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,       key=lambda UpperCAmelCase_							: kv[1]							):
																if index != token_index:
																				logger.warning(
																				    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																				    """ Please check that the tokenizer is not corrupted!"""							)
																				lowerCamelCase		       =token_index
																writer.write(""" """.join(UpperCAmelCase_							) + """\n"""							)
																index += 1

								return vocab_file, merge_file


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_=False      ,       **UpperCAmelCase_							):
								lowerCamelCase		       =kwargs.pop("""add_prefix_space"""      ,       self.add_prefix_space							)
								if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase_							) > 0 and not text[0].isspace()):
												lowerCamelCase		       =""" """ + text
								return (text, kwargs)


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =[]
								for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(UpperCAmelCase_      ,       add_special_tokens=UpperCAmelCase_							) + [self.eos_token_id]							)
								if len(UpperCAmelCase_							) > self.model_max_length:
												lowerCamelCase		       =input_ids[-self.model_max_length :]
								return input_ids












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
#
########################################################################

UpperCAmelCase__  :	Union[str, Any]     =16
UpperCAmelCase__  :	List[Any]     =32





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase = 16 )  ->     Dict:
				lowerCamelCase		       =AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCamelCase		       =load_dataset("""glue"""	,					"""mrpc""" )

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCamelCase		       =datasets.map(
								    _UpperCAmelCase	,					batched=_UpperCAmelCase	,					remove_columns=["""idx""", """sentence1""", """sentence2"""]	,					)

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

				def collate_fn(_UpperCAmelCase ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCamelCase		       =1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCamelCase		       =16
								elif accelerator.mixed_precision != "no":
												lowerCamelCase		       =8
								else:
												lowerCamelCase		       =None

								return tokenizer.pad(
								    _UpperCAmelCase	,					padding="""longest"""	,					max_length=_UpperCAmelCase	,					pad_to_multiple_of=_UpperCAmelCase	,					return_tensors="""pt"""	,					)

				# Instantiate dataloaders.
				lowerCamelCase		       =DataLoader(
				    tokenized_datasets["""train"""]	,					shuffle=_UpperCAmelCase	,					collate_fn=_UpperCAmelCase	,					batch_size=_UpperCAmelCase )
				lowerCamelCase		       =DataLoader(
				    tokenized_datasets["""validation"""]	,					shuffle=_UpperCAmelCase	,					collate_fn=_UpperCAmelCase	,					batch_size=_UpperCAmelCase )

				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

							UpperCAmelCase__  :	Union[str, Any]     =mocked_dataloaders  # noqa: F811





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     List[Any]:
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,					_UpperCAmelCase ) == "1":
								lowerCamelCase		       =2
				# Initialize accelerator
				lowerCamelCase		       =Accelerator(cpu=args.cpu	,					mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase		       =config["""lr"""]
				lowerCamelCase		       =int(config["""num_epochs"""] )
				lowerCamelCase		       =int(config["""seed"""] )
				lowerCamelCase		       =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":
												lowerCamelCase		       =args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												lowerCamelCase		       =int(args.checkpointing_steps )
								else:
												raise ValueError(
												    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
				else:
								lowerCamelCase		       =None

				set_seed(_UpperCAmelCase )

				lowerCamelCase       ,     lowerCamelCase		       =get_dataloaders(_UpperCAmelCase	,					_UpperCAmelCase )
				lowerCamelCase		       =evaluate.load("""glue"""	,					"""mrpc""" )

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

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

				# 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).
				lowerCamelCase		       =model.to(accelerator.device )

				# Instantiate optimizer
				lowerCamelCase		       =AdamW(params=model.parameters()	,					lr=_UpperCAmelCase )

				# Instantiate scheduler
				lowerCamelCase		       =get_linear_schedule_with_warmup(
				    optimizer=_UpperCAmelCase	,					num_warmup_steps=1_00	,					num_training_steps=(len(_UpperCAmelCase ) * 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.
				lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase		       =accelerator.prepare(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )

				# New Code #
				# We need to keep track of how many total steps we have iterated over
				lowerCamelCase		       =0
				# We also need to keep track of the stating epoch so files are named properly
				lowerCamelCase		       =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 )
												lowerCamelCase		       =os.path.basename(args.resume_from_checkpoint )
								else:
												# Get the most recent checkpoint
												lowerCamelCase		       =[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
												dirs.sort(key=os.path.getctime )
												lowerCamelCase		       =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								lowerCamelCase		       =os.path.splitext(_UpperCAmelCase )[0]

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

    # Now we train the model
				for epoch in range(_UpperCAmelCase	,					_UpperCAmelCase ):
								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
												lowerCamelCase		       =accelerator.skip_first_batches(_UpperCAmelCase	,					_UpperCAmelCase )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												lowerCamelCase		       =train_dataloader
								for step, batch in enumerate(_UpperCAmelCase ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												lowerCamelCase		       =model(**_UpperCAmelCase )
												lowerCamelCase		       =outputs.loss
												lowerCamelCase		       =loss / gradient_accumulation_steps
												accelerator.backward(_UpperCAmelCase )
												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(_UpperCAmelCase	,					_UpperCAmelCase ):
																lowerCamelCase		       =F"""step_{overall_step}"""
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								lowerCamelCase		       =os.path.join(args.output_dir	,					_UpperCAmelCase )
																				accelerator.save_state(_UpperCAmelCase )

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

								lowerCamelCase		       =metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""	,					_UpperCAmelCase )

								# 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":
												lowerCamelCase		       =F"""epoch_{epoch}"""
												if args.output_dir is not None:
																lowerCamelCase		       =os.path.join(args.output_dir	,					_UpperCAmelCase )
												accelerator.save_state(_UpperCAmelCase )





def    _lowercase    (  )  ->     str:
				lowerCamelCase		       =argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""	,					type=_UpperCAmelCase	,					default=_UpperCAmelCase	,					choices=["""no""", """fp16""", """bf16""", """fp8"""]	,					help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""	,					)
				parser.add_argument("""--cpu"""	,					action="""store_true"""	,					help="""If passed, will train on the CPU.""" )
				parser.add_argument(
				    """--checkpointing_steps"""	,					type=_UpperCAmelCase	,					default=_UpperCAmelCase	,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""	,					)
				parser.add_argument(
				    """--output_dir"""	,					type=_UpperCAmelCase	,					default="""."""	,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,					)
				parser.add_argument(
				    """--resume_from_checkpoint"""	,					type=_UpperCAmelCase	,					default=_UpperCAmelCase	,					help="""If the training should continue from a checkpoint folder."""	,					)
				lowerCamelCase		       =parser.parse_args()
				lowerCamelCase		       ={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_UpperCAmelCase	,					_UpperCAmelCase )


if __name__ == "__main__":
							main()





