





"""simple docstring"""





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase=99      ,						lowercase=13      ,						lowercase=7      ,						lowercase=9      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=32      ,						lowercase=5      ,						lowercase=4      ,						lowercase=37      ,						lowercase=8      ,						lowercase=0.1      ,						lowercase=0.002      ,						lowercase=1      ,						lowercase=0      ,						lowercase=0      ,						lowercase=None      ,						lowercase=None      ,						)		->     Optional[Any]:
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       encoder_seq_length
            lowerCAmelCase  						=       decoder_seq_length
            # For common tests
            lowerCAmelCase  						=       self.decoder_seq_length
            lowerCAmelCase  						=       is_training
            lowerCAmelCase  						=       use_attention_mask
            lowerCAmelCase  						=       use_labels
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       d_ff
            lowerCAmelCase  						=       relative_attention_num_buckets
            lowerCAmelCase  						=       dropout_rate
            lowerCAmelCase  						=       initializer_factor
            lowerCAmelCase  						=       eos_token_id
            lowerCAmelCase  						=       pad_token_id
            lowerCAmelCase  						=       decoder_start_token_id
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       decoder_layers





      def     _snake_case			(       self )		->     str:
            return TaConfig.from_pretrained("""google/umt5-base""" )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						)		->     Optional[Any]:
            if attention_mask is None:
                  lowerCAmelCase  						=       input_ids.ne(config.pad_token_id )
            if decoder_attention_mask is None:
                  lowerCAmelCase  						=       decoder_input_ids.ne(config.pad_token_id )
            if head_mask is None:
                  lowerCAmelCase  						=       torch.ones(config.num_hidden_layers      ,						config.num_attention_heads      ,						device=lowercase )
            if decoder_head_mask is None:
                  lowerCAmelCase  						=       torch.ones(config.num_decoder_layers      ,						config.num_attention_heads      ,						device=lowercase )
            if cross_attn_head_mask is None:
                  lowerCAmelCase  						=       torch.ones(
                      config.num_decoder_layers      ,						config.num_attention_heads      ,						device=lowercase )
            return {
                "input_ids": input_ids,
                "decoder_input_ids": decoder_input_ids,
                "attention_mask": attention_mask,
                "decoder_attention_mask": decoder_attention_mask,
                "head_mask": head_mask,
                "decoder_head_mask": decoder_head_mask,
                "cross_attn_head_mask": cross_attn_head_mask,
            }





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       ids_tensor([self.batch_size, self.encoder_seq_length]      ,						self.vocab_size )
            lowerCAmelCase  						=       ids_tensor([self.batch_size, self.decoder_seq_length]      ,						self.vocab_size )

            # we need to clamp the input ids here to avoid having pad token in between
            # this is because for NllbMoe the position_ids are prepared such that
            # all pad tokens have pos id = 2 and rest are between 2..seq_length
            # and the seq_length here is seq_length - num_pad_tokens
            # but when using past, there is no way of knowing if the past input ids had
            # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
            # position_ids being off by num_pad_tokens in past input
            lowerCAmelCase  						=       input_ids.clamp(self.pad_token_id + 1 )
            lowerCAmelCase  						=       decoder_input_ids.clamp(self.pad_token_id + 1 )

            lowerCAmelCase  						=       self.get_config()
            lowerCAmelCase  						=       config.num_attention_heads
            lowerCAmelCase  						=       self.prepare_inputs_dict(lowercase      ,						lowercase      ,						lowercase )
            return config, input_dict





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.prepare_config_and_inputs()
            return config, inputs_dict





      def     _snake_case			(       self )		->     List[str]:
            return TaConfig(
                vocab_size=166      ,						d_model=self.hidden_size      ,						d_ff=self.d_ff      ,						d_kv=self.hidden_size // self.num_attention_heads      ,						num_layers=self.num_hidden_layers      ,						num_decoder_layers=self.decoder_layers      ,						num_heads=self.num_attention_heads      ,						relative_attention_num_buckets=self.relative_attention_num_buckets      ,						dropout_rate=self.dropout_rate      ,						initializer_factor=self.initializer_factor      ,						eos_token_id=self.eos_token_id      ,						bos_token_id=self.pad_token_id      ,						pad_token_id=self.pad_token_id      ,						decoder_start_token_id=self.decoder_start_token_id      ,						)





      def     _snake_case			(       self )		->     Optional[Any]:
            return TaConfig(
                vocab_size=self.vocab_size      ,						d_model=self.hidden_size      ,						d_ff=self.d_ff      ,						d_kv=self.hidden_size // self.num_attention_heads      ,						num_layers=self.num_hidden_layers      ,						num_decoder_layers=self.decoder_layers      ,						num_heads=self.num_attention_heads      ,						relative_attention_num_buckets=self.relative_attention_num_buckets      ,						dropout_rate=self.dropout_rate      ,						initializer_factor=self.initializer_factor      ,						eos_token_id=self.eos_token_id      ,						bos_token_id=self.pad_token_id      ,						pad_token_id=self.pad_token_id      ,						decoder_start_token_id=self.decoder_start_token_id      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     str:
            lowerCAmelCase  						=       UMTaModel(config=lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       model(
                input_ids=lowercase      ,						decoder_input_ids=lowercase      ,						attention_mask=lowercase      ,						decoder_attention_mask=lowercase      ,						)
            lowerCAmelCase  						=       model(input_ids=lowercase      ,						decoder_input_ids=lowercase )
            lowerCAmelCase  						=       result.last_hidden_state
            lowerCAmelCase  						=       result.past_key_values
            lowerCAmelCase  						=       result.encoder_last_hidden_state

            self.parent.assertEqual(encoder_output.size()      ,						(self.batch_size, self.encoder_seq_length, self.hidden_size) )
            self.parent.assertEqual(decoder_output.size()      ,						(self.batch_size, self.decoder_seq_length, self.hidden_size) )
            # There should be `num_layers` key value embeddings stored in decoder_past
            self.parent.assertEqual(len(lowercase )      ,						config.num_layers )
            # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
            self.parent.assertEqual(len(decoder_past[0] )      ,						4 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     Optional[Any]:
            lowerCAmelCase  						=       UMTaModel(config=lowercase ).get_decoder().to(lowercase ).eval()
            # first forward pass
            lowerCAmelCase  						=       model(lowercase      ,						use_cache=lowercase )
            lowerCAmelCase  						=       model(lowercase )
            lowerCAmelCase  						=       model(lowercase      ,						use_cache=lowercase )

            self.parent.assertTrue(len(lowercase ) == len(lowercase ) )
            self.parent.assertTrue(len(lowercase ) == len(lowercase ) + 1 )

            lowerCAmelCase						,		lowerCAmelCase  						=       outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            lowerCAmelCase  						=       ids_tensor((self.batch_size, 1)      ,						config.vocab_size )

            # append to next input_ids and
            lowerCAmelCase  						=       torch.cat([input_ids, next_tokens]      ,						dim=-1 )

            lowerCAmelCase  						=       model(lowercase )["""last_hidden_state"""]
            lowerCAmelCase  						=       model(lowercase      ,						past_key_values=lowercase )["""last_hidden_state"""]

            # select random slice
            lowerCAmelCase  						=       ids_tensor((1,)      ,						output_from_past.shape[-1] ).item()
            lowerCAmelCase  						=       output_from_no_past[:, -1, random_slice_idx].detach()
            lowerCAmelCase  						=       output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(lowercase      ,						lowercase      ,						atol=1e-3 ) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						)		->     str:
            lowerCAmelCase  						=       UMTaModel(config=lowercase ).to(lowercase ).half().eval()
            lowerCAmelCase  						=       model(**lowercase )["""last_hidden_state"""]
            self.parent.assertFalse(torch.isnan(lowercase ).any().item() )







@require_torch
class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      _SCREAMING_SNAKE_CASE					=      (UMTaForConditionalGeneration,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE					=      (
          {
              'conversational': UMTaForConditionalGeneration,
              'feature-extraction': UMTaModel,
              'summarization': UMTaForConditionalGeneration,
              'text2text-generation': UMTaForConditionalGeneration,
              'translation': UMTaForConditionalGeneration,
              'question-answering': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      _SCREAMING_SNAKE_CASE					=      [0.8, 0.9]





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       UMTaModelTester(self )





      @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase  						=       UMTaModel(config_and_inputs[0] ).to(lowercase )
            with tempfile.TemporaryDirectory() as tmpdirname:
                  torch.onnx.export(
                      lowercase      ,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,						f'{tmpdirname}/t5_test.onnx'      ,						export_params=lowercase      ,						opset_version=9      ,						input_names=["""input_ids""", """decoder_input_ids"""]      ,						)





      @unittest.skipIf(torch_device == """cpu"""      ,						"""Cant do half precision""" )
      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*lowercase )





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase  						=       config_and_inputs[0]
            lowerCAmelCase  						=       UMTaForConditionalGeneration(lowercase ).eval()
            model.to(lowercase )

            lowerCAmelCase  						=       {
                """head_mask""": torch.zeros(config.num_layers      ,						config.num_heads      ,						device=lowercase ),
                """decoder_head_mask""": torch.zeros(config.num_decoder_layers      ,						config.num_heads      ,						device=lowercase ),
                """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers      ,						config.num_heads      ,						device=lowercase ),
            }

            for attn_name, (name, mask) in zip(lowercase      ,						head_masking.items() ):
                  lowerCAmelCase  						=       {name: mask}
                  # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                  if name == "head_mask":
                        lowerCAmelCase  						=       torch.ones(
                            config.num_decoder_layers      ,						config.num_heads      ,						device=lowercase )

                  lowerCAmelCase  						=       model.generate(
                      config_and_inputs[1]["""input_ids"""]      ,						num_beams=1      ,						max_length=3      ,						output_attentions=lowercase      ,						return_dict_in_generate=lowercase      ,						**lowercase      ,						)
                  # We check the state of decoder_attentions and cross_attentions just from the last step
                  lowerCAmelCase  						=       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                  self.assertEqual(sum([w.sum().item() for w in attn_weights] )      ,						0.0 )





      @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
      def     _snake_case			(       self )		->     Union[str, Any]:
            pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 		lowercase					(					unittest.TestCase					):





      @slow
      @unittest.skip(
          """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""      ,						return_dict=lowercase ).to(lowercase )
            lowerCAmelCase  						=       AutoTokenizer.from_pretrained("""google/umt5-small"""      ,						use_fast=lowercase      ,						legacy=lowercase )
            lowerCAmelCase  						=       [
                """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
                """No se como puedo <extra_id_0>.""",
                """This is the reason why we <extra_id_0> them.""",
                """The <extra_id_0> walks in <extra_id_1>, seats""",
                """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
            ]
            lowerCAmelCase  						=       tokenizer(lowercase      ,						return_tensors="""pt"""      ,						padding=lowercase ).input_ids
            # fmt: off
            lowerCAmelCase  						=       torch.tensor(
                [
                    [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                    [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
                ] )
            # fmt: on
            torch.testing.assert_allclose(lowercase      ,						lowercase )

            lowerCAmelCase  						=       model.generate(input_ids.to(lowercase ) )
            lowerCAmelCase  						=       [
                """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
                """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            ]
            lowerCAmelCase  						=       tokenizer.batch_decode(lowercase )
            self.assertEqual(lowercase      ,						lowercase )








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			:     List[Any]           =   16
__lowerCAmelCase			:     Any           =   32

def a__    (       A_,     A_,     A_,     A_,     A_ = 16					):






  '''simple docstring'''





  __magic_name__				     =       AutoTokenizer.from_pretrained("""bert-base-cased"""					)
  __magic_name__				     =       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)
    __magic_name__				     =       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():
    __magic_name__				     =       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
  __magic_name__				     =       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.
    __magic_name__				     =       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":
      __magic_name__				     =       16
    elif accelerator.mixed_precision != "no":
      __magic_name__				     =       8
    else:
      __magic_name__				     =       None

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

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

  __magic_name__				     =       DataLoader(
      tokenized_datasets["""test"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)

  return train_dataloader, eval_dataloader, test_dataloader

def a__    (       A_,     A_					):






  '''simple docstring'''





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

  __magic_name__				     =       evaluate.load("""glue""",     """mrpc"""					)

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

  set_seed(A_					)

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

    # Instantiate optimizer
    __magic_name__				     =       AdamW(params=model.parameters(),     lr=A_					)

    # Instantiate scheduler
    __magic_name__				     =       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.
    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       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					)
        __magic_name__				     =       model(**A_					)
        __magic_name__				     =       outputs.loss
        __magic_name__				     =       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():
          __magic_name__				     =       model(**A_					)
        __magic_name__				     =       outputs.logits.argmax(dim=-1					)
        __magic_name__     ,    __magic_name__				     =       accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
        metric.add_batch(
            predictions=A_,     references=A_,     )

      __magic_name__				     =       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
    __magic_name__				     =       []
    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():
        __magic_name__				     =       model(**A_					)
      __magic_name__				     =       outputs.logits
      __magic_name__     ,    __magic_name__				     =       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:
  __magic_name__				     =       torch.cat(A_,     dim=0					)
  __magic_name__				     =       torch.stack(A_,     dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
  __magic_name__				     =       metric.compute(predictions=A_,     references=A_					)
  accelerator.print("""Average test metrics from all folds:""",     A_					)

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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"""					)
  __magic_name__				     =       parser.parse_args()
  __magic_name__				     =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(A_,     A_					)


if __name__ == "__main__":
    main()

