'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def     __lowerCAmelCase						(       a_		)      ->	int:





					'''simple docstring'''





					SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       os.path.join(args.tf_model_dir       ,      'parameters.json'		)
					SCREAMING_SNAKE_CASE					:			Tuple    	=       json.loads(open(_lowercase		).read()		)
					if not params:
										raise ValueError(
										    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""		)
					if not args.output.endswith('.pt'		):
										SCREAMING_SNAKE_CASE					:			Any    	=       args.output + '.pt'
					SCREAMING_SNAKE_CASE					:			Optional[int]    	=       OrderedDict()
					with tf.device('/CPU:0'		):
										SCREAMING_SNAKE_CASE					:			str    	=       tf.train.load_checkpoint(args.tf_model_dir		)
										SCREAMING_SNAKE_CASE					:			List[str]    	=       reader.get_variable_to_shape_map()
										for key_name in shapes.keys():
															SCREAMING_SNAKE_CASE					:			List[str]    	=       reader.get_tensor(_lowercase		).astype(np.floataa		)
															if key_name.endswith('/adam_m'		) or key_name.endswith('/adam_v'		):
																				continue
															if key_name.startswith('pasts/'		):
																				if key_name.startswith('pasts/mlp'		):
																									SCREAMING_SNAKE_CASE					:			Tuple    	=       int(key_name[9]		)
																				elif key_name.startswith('pasts/out'		):
																									SCREAMING_SNAKE_CASE					:			Dict    	=       8
																				SCREAMING_SNAKE_CASE					:			int    	=       'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																				SCREAMING_SNAKE_CASE					:			Any    	=       vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																				SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(_lowercase		)
															elif key_name.startswith('model/moe'		):
																				SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       int(key_name[9:].split('/'		)[0]		)
																				if key_name.endswith('/switch_gating/kernel'		):
																									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																									SCREAMING_SNAKE_CASE					:			List[str]    	=       vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/softmlp/kernel'		):
																									SCREAMING_SNAKE_CASE					:			int    	=       'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																									SCREAMING_SNAKE_CASE					:			Tuple    	=       vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/wo/kernel'		) or key_name.endswith('/wi/kernel'		):
																									SCREAMING_SNAKE_CASE					:			List[Any]    	=       key_name[-9:-7]
																									for i in range(16		):
																														SCREAMING_SNAKE_CASE					:			Any    	=       'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																														SCREAMING_SNAKE_CASE					:			List[str]    	=       (
																														    vnp[i].transpose([1, 0]		).copy()
																														)  # In Mesh-Tensorflow, it is one array, so it is divided
																														SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(_lowercase		)
															elif key_name.startswith('model/mlp'		):
																				SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       int(key_name[9:].split('/'		)[0]		)
																				if key_name.endswith('/p1/kernel'		):
																									SCREAMING_SNAKE_CASE					:			Tuple    	=       'model.blocks.%d.feed_forward.mlp.wi.weight' % player
																									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/p1/bias'		):
																									SCREAMING_SNAKE_CASE					:			Any    	=       'model.blocks.%d.feed_forward.mlp.wi.bias' % player
																									SCREAMING_SNAKE_CASE					:			List[Any]    	=       vnp.copy()  # same because it is one dimensional
																									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/p2/kernel'		):
																									SCREAMING_SNAKE_CASE					:			str    	=       'model.blocks.%d.feed_forward.mlp.wo.weight' % player
																									SCREAMING_SNAKE_CASE					:			int    	=       vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/p2/bias'		):
																									SCREAMING_SNAKE_CASE					:			Any    	=       'model.blocks.%d.feed_forward.mlp.wo.bias' % player
																									SCREAMING_SNAKE_CASE					:			List[str]    	=       vnp.copy()  # same because it is one dimensional
																									SCREAMING_SNAKE_CASE					:			Any    	=       torch.tensor(_lowercase		)
															elif key_name.startswith('model/ln'		):
																				SCREAMING_SNAKE_CASE					:			int    	=       int(key_name[8:].split('/'		)[0]		)
																				if key_name.endswith('/b'		):
																									SCREAMING_SNAKE_CASE					:			Dict    	=       'model.blocks.%d.feed_forward.norm.bias' % player
																									SCREAMING_SNAKE_CASE					:			Any    	=       vnp.copy()  # same because it is one dimensional
																									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/g'		):
																									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'model.blocks.%d.feed_forward.norm.weight' % player
																									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       vnp.copy()  # same because it is one dimensional
																									SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(_lowercase		)
															elif key_name.startswith('model/att'		):
																				SCREAMING_SNAKE_CASE					:			int    	=       int(key_name[9:].split('/'		)[0]		)
																				if key_name.endswith('/qkv/kernel'		):
																									SCREAMING_SNAKE_CASE					:			int    	=       vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       state[:, 0, :, :]
																									SCREAMING_SNAKE_CASE					:			List[str]    	=       state[:, 1, :, :]
																									SCREAMING_SNAKE_CASE					:			int    	=       state[:, 2, :, :]
																									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
																									    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]		)
																									    .transpose([1, 0]		)
																									    .copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			Any    	=       (
																									    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]		)
																									    .transpose([1, 0]		)
																									    .copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			int    	=       (
																									    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]		)
																									    .transpose([1, 0]		)
																									    .copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			Dict    	=       'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(_lowercase		)
																									SCREAMING_SNAKE_CASE					:			Dict    	=       'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																									SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor(_lowercase		)
																									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																									SCREAMING_SNAKE_CASE					:			Any    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/o/kernel'		):
																									SCREAMING_SNAKE_CASE					:			str    	=       'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																									SCREAMING_SNAKE_CASE					:			Dict    	=       (
																									    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]		).transpose([1, 0]		).copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									SCREAMING_SNAKE_CASE					:			Any    	=       torch.tensor(_lowercase		)
															elif key_name.startswith('model/an'		):
																				SCREAMING_SNAKE_CASE					:			List[Any]    	=       int(key_name[8:].split('/'		)[0]		)
																				if key_name.endswith('/b'		):
																									SCREAMING_SNAKE_CASE					:			Tuple    	=       'model.blocks.%d.self_attn.norm.bias' % player
																									SCREAMING_SNAKE_CASE					:			int    	=       vnp.copy()  # same because it is one dimensional
																									SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(_lowercase		)
																				elif key_name.endswith('/g'		):
																									SCREAMING_SNAKE_CASE					:			Dict    	=       'model.blocks.%d.self_attn.norm.weight' % player
																									SCREAMING_SNAKE_CASE					:			Any    	=       vnp.copy()  # same because it is one dimensional
																									SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor(_lowercase		)
															elif (
															    key_name.startswith('model/wte'		)
															    or key_name.startswith('model/wpe'		)
															    or key_name.startswith('model/ete'		)
															):
																				SCREAMING_SNAKE_CASE					:			List[Any]    	=       {'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																				    key_name[-3:]
																				]
																				SCREAMING_SNAKE_CASE					:			str    	=       'model.%s.weight' % nlayer
																				SCREAMING_SNAKE_CASE					:			Optional[int]    	=       vnp.copy()  # same in embedded
																				SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(_lowercase		)
																				if key_name.startswith('model/wte'		):
																									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       'lm_head.weight'
																									SCREAMING_SNAKE_CASE					:			List[str]    	=       vnp.copy()  # same in embedded
																									SCREAMING_SNAKE_CASE					:			Any    	=       torch.tensor(_lowercase		)
															elif key_name.startswith('model/wob'		):
																				SCREAMING_SNAKE_CASE					:			Any    	=       'final_logits_bias'
																				SCREAMING_SNAKE_CASE					:			List[str]    	=       vnp.copy()  # same in embedded
																				SCREAMING_SNAKE_CASE					:			str    	=       state.reshape((1, -1)		)
																				SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(_lowercase		)
															elif key_name == "model/dense/kernel":
																				SCREAMING_SNAKE_CASE					:			Dict    	=       'model.last_project.weight'
																				SCREAMING_SNAKE_CASE					:			Any    	=       vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
																				SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor(_lowercase		)
															elif key_name == "model/dense_1/bias":
																				SCREAMING_SNAKE_CASE					:			str    	=       'model.last_project.bias'
																				SCREAMING_SNAKE_CASE					:			Dict    	=       vnp.copy()  # same because it is one dimensional
																				SCREAMING_SNAKE_CASE					:			Dict    	=       torch.tensor(_lowercase		)
					torch.save(_lowercase       ,      args.output		)


if __name__ == "__main__":
	_lowerCAmelCase  :Tuple  				=    argparse.ArgumentParser(
	    description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
	)
	parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
	parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
	_lowerCAmelCase  :List[Any]  				=    parser.parse_args()
	convert_tf_gptsan_to_pt(args)




'''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  :Tuple  				=    16
_lowerCAmelCase  :List[Any]  				=    32


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_       ,      a_ = 16		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       AutoTokenizer.from_pretrained('bert-base-cased'		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       DatasetDict(
         {
             'train': dataset['train'].select(a_		),
             'validation': dataset['train'].select(a_		),
             'test': dataset['validation'],
         }		)

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

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

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

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       DataLoader(
         tokenized_datasets['train']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       DataLoader(
         tokenized_datasets['validation']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     SCREAMING_SNAKE_CASE					:			Any    	=       DataLoader(
         tokenized_datasets['test']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     return train_dataloader, eval_dataloader, test_dataloader


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       []
     # Download the dataset
     SCREAMING_SNAKE_CASE					:			Any    	=       load_dataset('glue'       ,      'mrpc'		)
     # Create our splits
     SCREAMING_SNAKE_CASE					:			Dict    	=       StratifiedKFold(n_splits=int(args.num_folds		)		)
     # Initialize accelerator
     SCREAMING_SNAKE_CASE					:			Tuple    	=       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					:			Dict    	=       config['lr']
     SCREAMING_SNAKE_CASE					:			Tuple    	=       int(config['num_epochs']		)
     SCREAMING_SNAKE_CASE					:			Any    	=       int(config['seed']		)
     SCREAMING_SNAKE_CASE					:			Any    	=       int(config['batch_size']		)

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       evaluate.load('glue'       ,      'mrpc'		)

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

     set_seed(a_		)

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

          # 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					:			Dict    	=       model.to(accelerator.device		)

          # Instantiate optimizer
          SCREAMING_SNAKE_CASE					:			int    	=       AdamW(params=model.parameters()       ,      lr=a_		)

          # Instantiate scheduler
          SCREAMING_SNAKE_CASE					:			int    	=       get_linear_schedule_with_warmup(
              optimizer=a_       ,      num_warmup_steps=100       ,      num_training_steps=(len(a_		) * 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					:			List[Any]    	=       accelerator.prepare(
              a_       ,      a_       ,      a_       ,      a_       ,      a_		)

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

               model.eval()
               for step, batch in enumerate(a_		):
                    # 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					:			Union[str, Any]    	=       model(**a_		)
                    SCREAMING_SNAKE_CASE					:			Any    	=       outputs.logits.argmax(dim=-1		)
                    SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       accelerator.gather_for_metrics((predictions, batch['labels'])		)
                    metric.add_batch(
                        predictions=a_       ,      references=a_       ,      )

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

          # New Code #
          # We also run predictions on the test set at the very end
          SCREAMING_SNAKE_CASE					:			Any    	=       []
          for step, batch in enumerate(a_		):
               # 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					:			Optional[int]    	=       model(**a_		)
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       outputs.logits
               SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       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(a_       ,      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					:			int    	=       torch.cat(a_       ,      dim=0		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.stack(a_       ,      dim=0		).sum(dim=0		).div(int(args.num_folds		)		).argmax(dim=-1		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       metric.compute(predictions=a_       ,      references=a_		)
     accelerator.print('Average test metrics from all folds:'       ,      a_		)


def     __lowerCAmelCase						(       )      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       argparse.ArgumentParser(description='Simple example of training script.'		)
     parser.add_argument(
         '--mixed_precision'       ,      type=a_       ,      default=a_       ,      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=a_       ,      default=3       ,      help='The number of splits to perform across the dataset'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       parser.parse_args()
     SCREAMING_SNAKE_CASE					:			Dict    	=       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(a_       ,      a_		)


if __name__ == "__main__":
 main()



