


"""simple docstring"""







from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFTransfoXLForSequenceClassification,
         TFTransfoXLLMHeadModel,
         TFTransfoXLModel,
     )


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							parent
         lowerCAmelCase     :     Tuple								=							13
         lowerCAmelCase     :     Tuple								=							7
         lowerCAmelCase     :     str								=							30
         lowerCAmelCase     :     Optional[Any]								=							self.seq_length + self.mem_len
         lowerCAmelCase     :     Dict								=							15
         lowerCAmelCase     :     Any								=							True
         lowerCAmelCase     :     int								=							True
         lowerCAmelCase     :     List[Any]								=							99
         lowerCAmelCase     :     Tuple								=							[10, 50, 80]
         lowerCAmelCase     :     Optional[Any]								=							32
         lowerCAmelCase     :     str								=							32
         lowerCAmelCase     :     str								=							4
         lowerCAmelCase     :     Optional[Any]								=							8
         lowerCAmelCase     :     Tuple								=							128
         lowerCAmelCase     :     Optional[int]								=							2
         lowerCAmelCase     :     Union[str, Any]								=							2
         lowerCAmelCase     :     List[str]								=							None
         lowerCAmelCase     :     Tuple								=							1
         lowerCAmelCase     :     int								=							0
         lowerCAmelCase     :     Tuple								=							3
         lowerCAmelCase     :     str								=							self.vocab_size - 1
         lowerCAmelCase     :     str								=							0.01

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size						)
         lowerCAmelCase     :     int								=							ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size						)

         lowerCAmelCase     :     int								=							None
         if self.use_labels:
           lowerCAmelCase     :     Any								=							ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size						)

         lowerCAmelCase     :     List[Any]								=							TransfoXLConfig(
             vocab_size=self.vocab_size		,     mem_len=self.mem_len		,     clamp_len=self.clamp_len		,     cutoffs=self.cutoffs		,     d_model=self.hidden_size		,     d_embed=self.d_embed		,     n_head=self.num_attention_heads		,     d_head=self.d_head		,     d_inner=self.d_inner		,     div_val=self.div_val		,     n_layer=self.num_hidden_layers		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.vocab_size - 1		,     init_range=self.init_range		,     num_labels=self.num_labels		,     )

         return (config, input_ids_a, input_ids_a, lm_labels)

       def        lowercase__							(						self						):






         """simple docstring"""





         random.seed(self.seed						)
         tf.random.set_seed(self.seed						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							TFTransfoXLModel(_a						)

         lowerCAmelCase     :     str								=							model(_a						).to_tuple()

         lowerCAmelCase     :     Optional[int]								=							{'input_ids': input_ids_a, 'mems': mems_a}

         lowerCAmelCase     :     Dict								=							model(_a						).to_tuple()

         self.parent.assertEqual(hidden_states_a.shape		,     (self.batch_size, self.seq_length, self.hidden_size)						)
         self.parent.assertEqual(hidden_states_a.shape		,     (self.batch_size, self.seq_length, self.hidden_size)						)
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							TFTransfoXLLMHeadModel(_a						)

         lowerCAmelCase     :     Any								=							model(_a						).to_tuple()

         lowerCAmelCase     :     Optional[int]								=							{'input_ids': input_ids_a, 'labels': lm_labels}
         lowerCAmelCase     :     int								=							model(_a						).to_tuple()

         lowerCAmelCase     :     Any								=							model([input_ids_a, mems_a]						).to_tuple()

         lowerCAmelCase     :     List[Any]								=							{'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

         lowerCAmelCase     :     List[Any]								=							model(_a						).to_tuple()

         self.parent.assertEqual(lm_logits_a.shape		,     (self.batch_size, self.seq_length, self.vocab_size)						)
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )

         self.parent.assertEqual(lm_logits_a.shape		,     (self.batch_size, self.seq_length, self.vocab_size)						)
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]		,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,     )

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							TFTransfoXLForSequenceClassification(_a						)
         lowerCAmelCase     :     Any								=							model(_a						)
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.prepare_config_and_inputs()
         (lowerCAmelCase)     :     Any								=							config_and_inputs
         lowerCAmelCase     :     Union[str, Any]								=							{'input_ids': input_ids_a}
         return config, inputs_dict


@require_tf
class       SCREAMING_SNAKE_CASE__				(		lowercase__   ,    lowercase__   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Any   =(
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       a  :				Optional[Any]   =() if is_tf_available() else ()
       a  :				Union[str, Any]   =(
           {
               '''feature-extraction''': TFTransfoXLModel,
               '''text-classification''': TFTransfoXLForSequenceClassification,
               '''text-generation''': TFTransfoXLLMHeadModel,
               '''zero-shot''': TFTransfoXLForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
       a  :				Optional[Any]   =False
       a  :				Optional[Any]   =False
       a  :				Union[str, Any]   =False
       a  :				int   =False

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if pipeline_test_casse_name == "TextGenerationPipelineTests":
           # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
           # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
           # tokenizer.
           return True

         return False

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							TFTransfoXLModelTester(self						)
         lowerCAmelCase     :     List[str]								=							ConfigTester(self		,     config_class=_a		,     d_embed=37						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.config_tester.run_common_tests()

       def        lowercase__							(						self						):






         """simple docstring"""





         self.model_tester.set_seed()
         lowerCAmelCase     :     Any								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*_a						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.model_tester.set_seed()
         lowerCAmelCase     :     Any								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*_a						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_a						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase     :     str								=							[TFTransfoXLForSequenceClassification]

         for model_class in self.all_model_classes:
           lowerCAmelCase     :     Optional[Any]								=							model_class(_a						)
           assert isinstance(model.get_input_embeddings()		,     tf.keras.layers.Layer						)
           if model_class in list_other_models_with_output_ebd:
             lowerCAmelCase     :     Tuple								=							model.get_output_embeddings()
             assert isinstance(_a		,     tf.keras.layers.Layer						)
             lowerCAmelCase     :     Tuple								=							model.get_bias()
             assert name is None
           else:
             lowerCAmelCase     :     Tuple								=							model.get_output_embeddings()
             assert x is None
             lowerCAmelCase     :     List[str]								=							model.get_bias()
             assert name is None

       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     :     Optional[int]								=							TFTransfoXLModel.from_pretrained(_a						)
           self.assertIsNotNone(_a						)







       @unittest.skip(reason="This model doesn\'t play well with fit() due to not returning a single loss."						)
       def        lowercase__							(						self						):






         """simple docstring"""





         pass


@require_tf
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       @unittest.skip("Skip test until #12651 is resolved."						)
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"						)
         # fmt: off
         lowerCAmelCase     :     str								=							tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]		,     dtype=tf.intaa						)  # noqa: E231
         # fmt: on
         #  In 1991 , the remains of Russian Tsar Nicholas II and his family
         #  ( except for Alexei and Maria ) are discovered .
         #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
         #  remainder of the story . 1883 Western Siberia ,
         #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
         #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
         #  father initially slaps him for making such an accusation , Rasputin watches as the
         #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
         #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
         #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

         # fmt: off
         lowerCAmelCase     :     Any								=							[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
         # fmt: on
         #  In 1991, the remains of Russian Tsar Nicholas II and his family (
         #  except for Alexei and Maria ) are discovered. The voice of young son,
         #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
         #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
         #  and a group of men to perform magic. Rasputin has a vision and
         #  denounces one of the men as a horse thief. Although his father initially
         #  slaps him for making such an accusation, Rasputin watches as the man
         #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
         #  of the Virgin Mary, prompting him to become a priest.
         #  Rasputin quickly becomes famous, with people, even a bishop, begging for
         #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
         # Nicholas II and his family were discovered. The voice of <unk> young son,
         # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

         lowerCAmelCase     :     List[Any]								=							model.generate(_a		,     max_length=200		,     do_sample=_a						)
         self.assertListEqual(output_ids[0].numpy().tolist()		,     _a						)







'''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         =    1_6
__lowerCAmelCase         =    3_2





def 					UpperCAmelCase_		(__a      :    Accelerator ,	__a      :    DatasetDict ,	__a      :    List[int] ,	__a      :    List[int] ,	__a      :    int = 1_6 ):






   """simple docstring"""

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

   def tokenize_function(__a      :    List[Any] ):
      # max_length=None => use the model max length (it's actually the default)
      _a				:	Optional[int]			=   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():
      _a				:	List[str]			=   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
   _a				:	List[Any]			=   tokenized_datasets.rename_column('label' ,	'labels' )

   def collate_fn(__a      :    int ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _a				:	Dict			=   1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _a				:	Tuple			=   1_6
      elif accelerator.mixed_precision != "no":
         _a				:	List[Any]			=   8
      else:
         _a				:	List[Any]			=   None

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

   # Instantiate dataloaders.
   _a				:	Any			=   DataLoader(
       tokenized_datasets['train'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )
   _a				:	Optional[int]			=   DataLoader(
       tokenized_datasets['validation'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   _a				:	Optional[Any]			=   DataLoader(
       tokenized_datasets['test'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   return train_dataloader, eval_dataloader, test_dataloader





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Union[str, Any] ):






   """simple docstring"""

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

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

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

   set_seed(__a )

   # New Code #
   # Create our folds:
   _a				:	int			=   kfold.split(np.zeros(datasets['train'].num_rows ) ,	datasets['train']['label'] )
   _a				:	Any			=   []
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(__a ):
      _a,  _a,  _a				:	Optional[Any]			=   get_fold_dataloaders(
          __a ,	__a ,	__a ,	__a ,	)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _a				:	Dict			=   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).
      _a				:	List[Any]			=   model.to(accelerator.device )

      # Instantiate optimizer
      _a				:	List[str]			=   AdamW(params=model.parameters() ,	lr=__a )

      # Instantiate scheduler
      _a				:	List[Any]			=   get_linear_schedule_with_warmup(
          optimizer=__a ,	num_warmup_steps=1_0_0 ,	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.
      _a,  _a,  _a,  _a,  _a				:	Union[str, 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 )
            _a				:	Dict			=   model(**__a )
            _a				:	int			=   outputs.loss
            _a				:	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():
               _a				:	Union[str, Any]			=   model(**__a )
            _a				:	Tuple			=   outputs.logits.argmax(dim=-1 )
            _a,  _a				:	Any			=   accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=__a ,	references=__a ,	)

         _a				:	List[Any]			=   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
      _a				:	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():
            _a				:	Tuple			=   model(**__a )
         _a				:	Dict			=   outputs.logits
         _a,  _a				:	Optional[int]			=   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:
   _a				:	Dict			=   torch.cat(__a ,	dim=0 )
   _a				:	Any			=   torch.stack(__a ,	dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
   _a				:	str			=   metric.compute(predictions=__a ,	references=__a )
   accelerator.print('Average test metrics from all folds:' ,	__a )





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Any			=   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' )
   _a				:	Any			=   parser.parse_args()
   _a				:	int			=   {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
   training_function(__a ,	__a )


if __name__ == "__main__":
  main()



