





# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     Optional[Any]:
				# Format the message.
				if name is None:
								lowerCamelCase		       =None
				else:
								lowerCamelCase		       =""".""" * max(0	,					spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
								lowerCamelCase		       =fmt.format(_UpperCAmelCase )

				# Print and recurse (if needed).
				if isinstance(_UpperCAmelCase	,					_UpperCAmelCase ):
								if msg is not None:
												print(_UpperCAmelCase )
								for k in val.keys():
												recursive_print(_UpperCAmelCase	,					val[k]	,					spaces + 2 )
				elif isinstance(_UpperCAmelCase	,					torch.Tensor ):
								print(_UpperCAmelCase	,					""":"""	,					val.size() )
				else:
								print(_UpperCAmelCase	,					""":"""	,					_UpperCAmelCase )





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Tuple:
				# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
				# for compatibility with later versions of NVIDIA Megatron-LM.
				# The inverse operation is performed inside Megatron-LM to read checkpoints:
				# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
				# If param is the weight tensor of the self-attention block, the returned tensor
				# will have to be transposed one more time to be read by HuggingFace BERT.
				lowerCamelCase		       =param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								lowerCamelCase		       =(num_heads, hidden_size, num_splits) + input_shape[1:]
								lowerCamelCase		       =param.view(*_UpperCAmelCase )
								lowerCamelCase		       =param.transpose(0	,					2 )
								lowerCamelCase		       =param.transpose(1	,					2 ).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								lowerCamelCase		       =(num_heads, num_splits, hidden_size) + input_shape[1:]
								lowerCamelCase		       =param.view(*_UpperCAmelCase )
								lowerCamelCase		       =param.transpose(0	,					1 ).contiguous()
				lowerCamelCase		       =param.view(*_UpperCAmelCase )
				return param





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     List[Any]:
				# The converted output model.
				lowerCamelCase		       ={}

				# old versions did not store training args
				lowerCamelCase		       =input_state_dict.get("""args"""	,					_UpperCAmelCase )
				if ds_args is not None:
								# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
								# from pprint import pprint
								# pprint(vars(ds_args))

								lowerCamelCase		       =ds_args.tokenizer_type
								lowerCamelCase		       =ds_args.padded_vocab_size
								lowerCamelCase		       =ds_args.max_position_embeddings
								lowerCamelCase		       =ds_args.hidden_size
								lowerCamelCase		       =ds_args.num_layers
								lowerCamelCase		       =ds_args.num_attention_heads
								lowerCamelCase		       =ds_args.ffn_hidden_size if """ffn_hidden_size""" in ds_args else 4 * ds_args.hidden_size
								# pprint(config)

				# The number of heads.
				lowerCamelCase		       =config.num_attention_heads
				# The hidden_size per head.
				lowerCamelCase		       =config.hidden_size // heads
				# Megatron-LM checkpoint version
				if "checkpoint_version" in input_state_dict.keys():
								lowerCamelCase		       =input_state_dict["""checkpoint_version"""]
				else:
								lowerCamelCase		       =0.0

				# The model.
				lowerCamelCase		       =input_state_dict["""model"""]
				# The language model.
				lowerCamelCase		       =model["""language_model"""]
				# The embeddings.
				lowerCamelCase		       =lm["""embedding"""]

				# The word embeddings.
				lowerCamelCase		       =embeddings["""word_embeddings"""]["""weight"""]
				# Truncate the embedding table to vocab_size rows.
				lowerCamelCase		       =word_embeddings[: config.vocab_size, :]
				# Store the word embeddings.
				lowerCamelCase		       =word_embeddings

				# The position embeddings.
				lowerCamelCase		       =embeddings["""position_embeddings"""]["""weight"""]
				assert pos_embeddings.size(0 ) == config.max_position_embeddings and pos_embeddings.size(1 ) == config.hidden_size
				# Store the position embeddings.
				lowerCamelCase		       =pos_embeddings

				# The token-type embeddings.
				lowerCamelCase		       =embeddings["""tokentype_embeddings"""]["""weight"""]
				# Store the position embeddings.
				lowerCamelCase		       =tokentype_embeddings

				# The transformer.
				lowerCamelCase		       =lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

				# The regex to extract layer names.
				lowerCamelCase		       =re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )

				# The simple map of names for "automated" rules.
				lowerCamelCase		       ={
				    """attention.dense""": """.attention.output.dense.""",
				    """self_attention.dense""": """.attention.output.dense.""",
				    """mlp.dense_h_to_4h""": """.intermediate.dense.""",
				    """mlp.dense_4h_to_h""": """.output.dense.""",
				}

				# Keep track of the attention/query/value tensor.
				lowerCamelCase		       =None

				# Extract the layers.
				for key, val in transformer.items():
								# Match the name.
								lowerCamelCase		       =layer_re.match(_UpperCAmelCase )

								# Stop if that's not a layer
								if m is None:
												break

								# The index of the layer.
								lowerCamelCase		       =int(m.group(1 ) )
								# The name of the operation.
								lowerCamelCase		       =m.group(2 )
								# Is it a weight or a bias?
								lowerCamelCase		       =m.group(3 )

								# The name of the layer.
								lowerCamelCase		       =F"""bert.encoder.layer.{layer_idx}"""

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith("""layernorm""" ):
												lowerCamelCase		       ="""attention.ln""" if op_name.startswith("""input""" ) else """ln"""
												lowerCamelCase		       =val

								# Transpose the QKV matrix.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "weight":
												# Make sure the QKV pointer is nil.
												assert attention_qkv_weight is None, ""

												lowerCamelCase		       =fix_query_key_value_ordering(_UpperCAmelCase	,					_UpperCAmelCase	,					3	,					_UpperCAmelCase	,					_UpperCAmelCase )
												# Store the tensor as we need the bias as well to interleave QKV and biases.
												lowerCamelCase		       =out_val

								# Transpose the bias.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "bias":
												# Make sure we read the weight tensor.
												assert attention_qkv_weight is not None, ""

												# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
												lowerCamelCase		       =attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
												lowerCamelCase		       =attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
												lowerCamelCase		       =attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

												lowerCamelCase		       =fix_query_key_value_ordering(_UpperCAmelCase	,					_UpperCAmelCase	,					3	,					_UpperCAmelCase	,					_UpperCAmelCase )
												# Split the bias.
												lowerCamelCase		       =out_val[0 * config.hidden_size : 1 * config.hidden_size]
												lowerCamelCase		       =out_val[1 * config.hidden_size : 2 * config.hidden_size]
												lowerCamelCase		       =out_val[2 * config.hidden_size : 3 * config.hidden_size]

												# Store.
												lowerCamelCase		       =q
												lowerCamelCase		       =q_bias
												lowerCamelCase		       =k
												lowerCamelCase		       =k_bias
												lowerCamelCase		       =v
												lowerCamelCase		       =v_bias

												# Clear the stored tensor.
												lowerCamelCase		       =None

								# Copy weights and biases as is.
								elif weight_or_bias in ["weight", "bias"]:
												lowerCamelCase		       =megatron_to_transformers[op_name]
												lowerCamelCase		       =val

    # The final layernorm.
				lowerCamelCase		       =transformer["""final_layernorm.weight"""]
				lowerCamelCase		       =transformer["""final_layernorm.bias"""]

				# The pooler.
				lowerCamelCase		       =lm["""pooler"""]

				# Store the matrix and the bias.
				lowerCamelCase		       =pooler["""dense.weight"""]
				lowerCamelCase		       =pooler["""dense.bias"""]

				# The LM head from Megatron (for RACE).
				lowerCamelCase		       =model["""lm_head"""]

				# The transform matrix.
				lowerCamelCase		       =lm_head["""dense.weight"""]
				lowerCamelCase		       =lm_head["""dense.bias"""]

				# The transform LN.
				lowerCamelCase		       =lm_head["""layernorm.weight"""]
				lowerCamelCase		       =lm_head["""layernorm.bias"""]

				# For the decoder, we replicate the weights.
				lowerCamelCase		       =word_embeddings
				lowerCamelCase		       =lm_head["""bias"""]

				# The classifier from Megatron (for MLNI).
				lowerCamelCase		       =model["""binary_head"""]

				# Store the classifier.
				lowerCamelCase		       =binary_head["""weight"""]
				lowerCamelCase		       =binary_head["""bias"""]

				# It should be done!
				return output_state_dict





def    _lowercase    (  )  ->     str:
				# Create the argument parser.
				lowerCamelCase		       =argparse.ArgumentParser()
				parser.add_argument("""--print-checkpoint-structure"""	,					action="""store_true""" )
				parser.add_argument("""path_to_checkpoint"""	,					type=_UpperCAmelCase	,					help="""Path to the ZIP file containing the checkpoint""" )
				parser.add_argument(
				    """--config_file"""	,					default=""""""	,					type=_UpperCAmelCase	,					help="""An optional config json file describing the pre-trained model."""	,					)
				lowerCamelCase		       =parser.parse_args()

				# Extract the basename.
				lowerCamelCase		       =os.path.dirname(args.path_to_checkpoint )

				# Load the model.
				# the .zip is very optional, let's keep it for backward compatibility
				print(F"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\"""" )
				if args.path_to_checkpoint.endswith(""".zip""" ):
								with zipfile.ZipFile(args.path_to_checkpoint	,					"""r""" ) as checkpoint:
												with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
																lowerCamelCase		       =torch.load(_UpperCAmelCase	,					map_location="""cpu""" )
				else:
								lowerCamelCase		       =torch.load(args.path_to_checkpoint	,					map_location="""cpu""" )

				if args.config_file == "":
								# Default config of megatron-bert 345m
								lowerCamelCase		       =MegatronBertConfig()

								# different megatron-bert-*-345m models have different vocab sizes, so override the default
								# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
								lowerCamelCase		       =input_state_dict["""model"""]["""lm_head"""]["""bias"""].numel()
				else:
								lowerCamelCase		       =MegatronBertConfig.from_json_file(args.config_file )

				# Convert.
				print("""Converting""" )
				lowerCamelCase		       =convert_megatron_checkpoint(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )

				# Print the structure of converted state dict.
				if args.print_checkpoint_structure:
								recursive_print(_UpperCAmelCase	,					_UpperCAmelCase )

				# Store the config to file.
				print("""Saving config""" )
				config.save_pretrained(_UpperCAmelCase )

				# Store the state_dict to file.
				lowerCamelCase		       =os.path.join(_UpperCAmelCase	,					"""pytorch_model.bin""" )
				print(F"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
				torch.save(_UpperCAmelCase	,					_UpperCAmelCase )


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

if __name__ == "__main__":
							main()

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












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()





