"""simple docstring"""





import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     =	logging.get_logger(__name__)

set_seed(770)


lowerCAmelCase_     =	{
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


lowerCAmelCase_     =	{
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

lowerCAmelCase_     =	os.path.dirname(os.path.abspath(__file__))
lowerCAmelCase_     =	os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowerCAmelCase_     =	os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model_type
							if use_small:
														key += "_small"
							return os.path.join(__SCREAMING_SNAKE_CASE     ,	REMOTE_MODEL_PATHS[key]["""file_name"""]  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							os.makedirs(__SCREAMING_SNAKE_CASE     ,	exist_ok=__SCREAMING_SNAKE_CASE  )
							hf_hub_download(repo_id=__SCREAMING_SNAKE_CASE     ,	filename=__SCREAMING_SNAKE_CASE     ,	local_dir=__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE="text"  )->    Tuple:
							if model_type == "text":
														_SCREAMING_SNAKE_CASE		:						Dict			  =    BarkSemanticModel
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    BarkSemanticConfig
														_SCREAMING_SNAKE_CASE		:						Any			  =    BarkSemanticGenerationConfig
							elif model_type == "coarse":
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    BarkCoarseModel
														_SCREAMING_SNAKE_CASE		:						Any			  =    BarkCoarseConfig
														_SCREAMING_SNAKE_CASE		:						Dict			  =    BarkCoarseGenerationConfig
							elif model_type == "fine":
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    BarkFineModel
														_SCREAMING_SNAKE_CASE		:						str			  =    BarkFineConfig
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							_SCREAMING_SNAKE_CASE		:						Any			  =    F"""{model_type}_small""" if use_small else model_type
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    REMOTE_MODEL_PATHS[model_key]
							if not os.path.exists(__SCREAMING_SNAKE_CASE  ):
														logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""  )
														_download(model_info["""repo_id"""]     ,	model_info["""file_name"""]  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.load(__SCREAMING_SNAKE_CASE     ,	map_location=__SCREAMING_SNAKE_CASE  )
							# this is a hack
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model_args["""vocab_size"""]
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model_args["""vocab_size"""]
														del model_args["vocab_size"]

							# convert Bark model arguments to HF Bark model arguments
							_SCREAMING_SNAKE_CASE		:						str			  =    model_args.pop("""n_head"""  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    model_args.pop("""n_embd"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    model_args.pop("""n_layer"""  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    ConfigClass(**checkpoint["""model_args"""]  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ModelClass(config=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						str			  =    GenerationConfigClass()

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model_generation_config
							_SCREAMING_SNAKE_CASE		:						Dict			  =    checkpoint["""model"""]
							# fixup checkpoint
							_SCREAMING_SNAKE_CASE		:						Any			  =    """_orig_mod."""
							for k, v in list(state_dict.items()  ):
														if k.startswith(__SCREAMING_SNAKE_CASE  ):
																					# replace part of the key with corresponding layer name in HF implementation
																					_SCREAMING_SNAKE_CASE		:						Any			  =    k[len(__SCREAMING_SNAKE_CASE  ) :]
																					for old_layer_name in new_layer_name_dict:
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    new_k.replace(__SCREAMING_SNAKE_CASE     ,	new_layer_name_dict[old_layer_name]  )

																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    state_dict.pop(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    set(state_dict.keys()  ) - set(model.state_dict().keys()  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {k for k in extra_keys if not k.endswith(""".attn.bias"""  )}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    set(model.state_dict().keys()  ) - set(state_dict.keys()  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {k for k in missing_keys if not k.endswith(""".attn.bias"""  )}
							if len(__SCREAMING_SNAKE_CASE  ) != 0:
														raise ValueError(F"""extra keys found: {extra_keys}"""  )
							if len(__SCREAMING_SNAKE_CASE  ) != 0:
														raise ValueError(F"""missing keys: {missing_keys}"""  )
							model.load_state_dict(__SCREAMING_SNAKE_CASE     ,	strict=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    model.num_parameters(exclude_embeddings=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    checkpoint["""best_val_loss"""].item()
							logger.info(F"""model loaded: {round(n_params/1e6     ,	1  )}M params, {round(__SCREAMING_SNAKE_CASE     ,	3  )} loss"""  )
							model.eval()
							model.to(__SCREAMING_SNAKE_CASE  )
							del checkpoint, state_dict

							return model



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE="text"  )->    str:
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							_SCREAMING_SNAKE_CASE		:						Any			  =    """cpu"""  # do conversion on cpu

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    _get_ckpt_path(__SCREAMING_SNAKE_CASE     ,	use_small=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    _load_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	model_type=__SCREAMING_SNAKE_CASE     ,	use_small=__SCREAMING_SNAKE_CASE  )

							# load bark initial model
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    _bark_load_model(__SCREAMING_SNAKE_CASE     ,	"""cpu"""     ,	model_type=__SCREAMING_SNAKE_CASE     ,	use_small=__SCREAMING_SNAKE_CASE  )

							if model_type == "text":
														_SCREAMING_SNAKE_CASE		:						Dict			  =    bark_model["""model"""]

							if model.num_parameters(exclude_embeddings=__SCREAMING_SNAKE_CASE  ) != bark_model.get_num_params():
														raise ValueError("""initial and new models don't have the same number of parameters"""  )

							# check if same output as the bark model
							_SCREAMING_SNAKE_CASE		:						Any			  =    5
							_SCREAMING_SNAKE_CASE		:						int			  =    10

							if model_type in ["text", "coarse"]:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.randint(256     ,	(batch_size, sequence_length)     ,	dtype=torch.int  )
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    bark_model(__SCREAMING_SNAKE_CASE  )[0]

														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(__SCREAMING_SNAKE_CASE  )

														# take last logits
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    output_new_model_total.logits[:, [-1], :]

							else:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    3
														_SCREAMING_SNAKE_CASE		:						str			  =    8
														_SCREAMING_SNAKE_CASE		:						int			  =    torch.randint(256     ,	(batch_size, sequence_length, n_codes_total)     ,	dtype=torch.int  )

														_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    bark_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    output_new_model_total.logits

							# output difference should come from the difference of self-attention implementation design
							if output_new_model.shape != output_old_model.shape:
														raise ValueError("""initial and new outputs don't have the same shape"""  )
							if (output_new_model - output_old_model).abs().max().item() > 1e-3:
														raise ValueError("""initial and new outputs are not equal"""  )

							Path(__SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE  )
							model.save_pretrained(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)->    int:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    os.path.join(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						int			  =    BarkSemanticConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE     ,	"""config.json"""  )  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    BarkCoarseConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE     ,	"""config.json"""  )  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    BarkFineConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE     ,	"""config.json"""  )  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    EncodecConfig.from_pretrained("""facebook/encodec_24khz"""  )

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    BarkSemanticModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    BarkCoarseModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    BarkFineModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    EncodecModel.from_pretrained("""facebook/encodec_24khz"""  )

							_SCREAMING_SNAKE_CASE		:						Any			  =    BarkConfig.from_sub_model_configs(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config     ,	coarseAcoustic.generation_config     ,	fineAcoustic.generation_config  )

							_SCREAMING_SNAKE_CASE		:						str			  =    BarkModel(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    semantic
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    coarseAcoustic
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    fineAcoustic
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    codec

							_SCREAMING_SNAKE_CASE		:						str			  =    bark_generation_config

							Path(__SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE  )
							bark.save_pretrained(__SCREAMING_SNAKE_CASE     ,	repo_id=__SCREAMING_SNAKE_CASE     ,	push_to_hub=__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			# Required parameters

			parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

			lowerCAmelCase_     =	parser.parse_args()

			load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)



"""simple docstring"""





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


lowerCAmelCase_     =	16
lowerCAmelCase_     =	32



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16  )->    str:
							_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    DatasetDict(
							    {
							        """train""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """validation""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """test""": dataset["""validation"""],
							    }  )

							def tokenize_function(__SCREAMING_SNAKE_CASE  ):
														# max_length=None => use the model max length (it's actually the default)
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    tokenizer(examples["""sentence1"""]     ,	examples["""sentence2"""]     ,	truncation=__SCREAMING_SNAKE_CASE     ,	max_length=__SCREAMING_SNAKE_CASE  )
														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():
														_SCREAMING_SNAKE_CASE		:						str			  =    datasets.map(
														    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE     ,	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
							_SCREAMING_SNAKE_CASE		:						Any			  =    tokenized_datasets.rename_column("""label"""     ,	"""labels"""  )

							def collate_fn(__SCREAMING_SNAKE_CASE  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_SCREAMING_SNAKE_CASE		:						Any			  =    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":
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    16
														elif accelerator.mixed_precision != "no":
																					_SCREAMING_SNAKE_CASE		:						Any			  =    8
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    None

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

							# Instantiate dataloaders.
							_SCREAMING_SNAKE_CASE		:						int			  =    DataLoader(
							    tokenized_datasets["""train"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    DataLoader(
							    tokenized_datasets["""validation"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    DataLoader(
							    tokenized_datasets["""test"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							return train_dataloader, eval_dataloader, test_dataloader



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							# New Code #
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							# Download the dataset
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    load_dataset("""glue"""     ,	"""mrpc"""  )
							# Create our splits
							_SCREAMING_SNAKE_CASE		:						Dict			  =    StratifiedKFold(n_splits=int(args.num_folds  )  )
							# Initialize accelerator
							_SCREAMING_SNAKE_CASE		:						Any			  =    Accelerator(cpu=args.cpu     ,	mixed_precision=args.mixed_precision  )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    config["""lr"""]
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    int(config["""num_epochs"""]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    int(config["""seed"""]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    int(config["""batch_size"""]  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    evaluate.load("""glue"""     ,	"""mrpc"""  )

							# If the batch size is too big we use gradient accumulation
							_SCREAMING_SNAKE_CASE		:						Any			  =    1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    batch_size // MAX_GPU_BATCH_SIZE
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    MAX_GPU_BATCH_SIZE

							set_seed(__SCREAMING_SNAKE_CASE  )

							# New Code #
							# Create our folds:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    kfold.split(np.zeros(datasets["""train"""].num_rows  )     ,	datasets["""train"""]["""label"""]  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_fold_dataloaders(
														    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														_SCREAMING_SNAKE_CASE		:						Any			  =    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""     ,	return_dict=__SCREAMING_SNAKE_CASE  )

														# 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).
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    model.to(accelerator.device  )

														# Instantiate optimizer
														_SCREAMING_SNAKE_CASE		:						int			  =    AdamW(params=model.parameters()     ,	lr=__SCREAMING_SNAKE_CASE  )

														# Instantiate scheduler
														_SCREAMING_SNAKE_CASE		:						int			  =    get_linear_schedule_with_warmup(
														    optimizer=__SCREAMING_SNAKE_CASE     ,	num_warmup_steps=100     ,	num_training_steps=(len(__SCREAMING_SNAKE_CASE  ) * 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.
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    accelerator.prepare(
														    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														# Now we train the model
														for epoch in range(__SCREAMING_SNAKE_CASE  ):
																					model.train()
																					for step, batch in enumerate(__SCREAMING_SNAKE_CASE  ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device  )
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(**__SCREAMING_SNAKE_CASE  )
																												_SCREAMING_SNAKE_CASE		:						Dict			  =    outputs.loss
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    loss / gradient_accumulation_steps
																												accelerator.backward(__SCREAMING_SNAKE_CASE  )
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

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

																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F"""epoch {epoch}:"""     ,	__SCREAMING_SNAKE_CASE  )

														# New Code #
														# We also run predictions on the test set at the very end
														_SCREAMING_SNAKE_CASE		:						str			  =    []
														for step, batch in enumerate(__SCREAMING_SNAKE_CASE  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					with torch.no_grad():
																												_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
																					fold_predictions.append(predictions.cpu()  )
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu()  )
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(__SCREAMING_SNAKE_CASE     ,	dim=0  )  )
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.cat(__SCREAMING_SNAKE_CASE     ,	dim=0  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.stack(__SCREAMING_SNAKE_CASE     ,	dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
							_SCREAMING_SNAKE_CASE		:						int			  =    metric.compute(predictions=__SCREAMING_SNAKE_CASE     ,	references=__SCREAMING_SNAKE_CASE  )
							accelerator.print("""Average test metrics from all folds:"""     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Any			  =    argparse.ArgumentParser(description="""Simple example of training script."""  )
							parser.add_argument(
							    """--mixed_precision"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=__SCREAMING_SNAKE_CASE     ,	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."""  )
							# New Code #
							parser.add_argument("""--num_folds"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=3     ,	help="""The number of splits to perform across the dataset"""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			main()


