
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    __lowerCAmelCase			:     int           =   'platform'

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )

def a__    (       A_,     A_,     A_=None,     A_=None,     A_=None,     A_=None,     A_=None,     A_=None,     ):






  '''simple docstring'''





  if attention_mask is None:
    __magic_name__				     =       np.where(input_ids != config.pad_token_id,     1,     0					)
  if decoder_attention_mask is None:
    __magic_name__				     =       np.where(decoder_input_ids != config.pad_token_id,     1,     0					)
  if head_mask is None:
    __magic_name__				     =       np.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    __magic_name__				     =       np.ones((config.decoder_layers, config.decoder_attention_heads)					)
  if cross_attn_head_mask is None:
    __magic_name__				     =       np.ones((config.decoder_layers, config.decoder_attention_heads)					)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: int			,							UpperCamelCase__			: str			,							UpperCamelCase__			: List[Any]=13			,							UpperCamelCase__			: Any=7			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: int=99			,							UpperCamelCase__			: str=16			,							UpperCamelCase__			: int=2			,							UpperCamelCase__			: str=4			,							UpperCamelCase__			: int=4			,							UpperCamelCase__			: Optional[int]="gelu"			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Union[str, Any]=32			,							UpperCamelCase__			: Union[str, Any]=2			,							UpperCamelCase__			: Optional[int]=1			,							UpperCamelCase__			: Tuple=0			,							UpperCamelCase__			: str=0.02			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       seq_length
    __magic_name__				     =       is_training
    __magic_name__				     =       use_labels
    __magic_name__				     =       vocab_size
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       eos_token_id
    __magic_name__				     =       pad_token_id
    __magic_name__				     =       bos_token_id
    __magic_name__				     =       initializer_range
  def 							_lowercase							( self			: Dict    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,							self.vocab_size    )			,							3			,							self.vocab_size    )
    __magic_name__				     =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,							dtype=np.intaa    ))			,							-1    )

    __magic_name__				     =       shift_tokens_right(UpperCamelCase__			,							1			,							2    )

    __magic_name__				     =       BlenderbotConfig(
        vocab_size=self.vocab_size			,							d_model=self.hidden_size			,							encoder_layers=self.num_hidden_layers			,							decoder_layers=self.num_hidden_layers			,							encoder_attention_heads=self.num_attention_heads			,							decoder_attention_heads=self.num_attention_heads			,							encoder_ffn_dim=self.intermediate_size			,							decoder_ffn_dim=self.intermediate_size			,							dropout=self.hidden_dropout_prob			,							attention_dropout=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							eos_token_id=self.eos_token_id			,							bos_token_id=self.bos_token_id			,							pad_token_id=self.pad_token_id			,							initializer_range=self.initializer_range			,							use_cache=UpperCamelCase__			,							)
    __magic_name__				     =       prepare_blenderbot_inputs_dict(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
    return config, inputs_dict
  def 							_lowercase							( self			: str    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.prepare_config_and_inputs()
    return config, inputs_dict
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Optional[Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       20
    __magic_name__				     =       model_class_name(UpperCamelCase__    )

    __magic_name__				     =       model.encode(inputs_dict["""input_ids"""]    )

    __magic_name__     ,    __magic_name__				     =       (
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    __magic_name__				     =       model.init_cache(decoder_input_ids.shape[0]			,							UpperCamelCase__			,							UpperCamelCase__    )
    __magic_name__				     =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,							dtype="""i4"""    )

    __magic_name__				     =       jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]			,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,							)
    __magic_name__				     =       model.decode(
        decoder_input_ids[:, :-1]			,							UpperCamelCase__			,							decoder_attention_mask=UpperCamelCase__			,							past_key_values=UpperCamelCase__			,							decoder_position_ids=UpperCamelCase__			,							)

    __magic_name__				     =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,							dtype="""i4"""    )
    __magic_name__				     =       model.decode(
        decoder_input_ids[:, -1:]			,							UpperCamelCase__			,							decoder_attention_mask=UpperCamelCase__			,							past_key_values=outputs_cache.past_key_values			,							decoder_position_ids=UpperCamelCase__			,							)

    __magic_name__				     =       model.decode(UpperCamelCase__			,							UpperCamelCase__    )

    __magic_name__				     =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
    self.parent.assertTrue(diff < 1E-3			,							msg=F'''Max diff is {diff}'''    )





  def 							_lowercase							( self			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Tuple    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       20
    __magic_name__				     =       model_class_name(UpperCamelCase__    )

    __magic_name__				     =       model.encode(inputs_dict["""input_ids"""]    )

    __magic_name__     ,    __magic_name__				     =       (
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    __magic_name__				     =       jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])    ),
        ]			,							axis=-1			,							)

    __magic_name__				     =       model.init_cache(decoder_input_ids.shape[0]			,							UpperCamelCase__			,							UpperCamelCase__    )
    __magic_name__				     =       jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]			,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,							)

    __magic_name__				     =       model.decode(
        decoder_input_ids[:, :-1]			,							UpperCamelCase__			,							decoder_attention_mask=UpperCamelCase__			,							past_key_values=UpperCamelCase__			,							decoder_position_ids=UpperCamelCase__			,							)
    __magic_name__				     =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,							dtype="""i4"""    )
    __magic_name__				     =       model.decode(
        decoder_input_ids[:, -1:]			,							UpperCamelCase__			,							past_key_values=outputs_cache.past_key_values			,							decoder_attention_mask=UpperCamelCase__			,							decoder_position_ids=UpperCamelCase__			,							)

    __magic_name__				     =       model.decode(UpperCamelCase__			,							UpperCamelCase__			,							decoder_attention_mask=UpperCamelCase__    )

    __magic_name__				     =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
    self.parent.assertTrue(diff < 1E-3			,							msg=F'''Max diff is {diff}'''    )







@require_flax
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  a__           =		99
  def 							_lowercase							( self			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       np.array(
        [
            [71, 82, 18, 33, 46, 91, 2],
            [68, 34, 26, 58, 30, 82, 2],
            [5, 97, 17, 39, 94, 40, 2],
            [76, 83, 94, 25, 70, 78, 2],
            [87, 59, 41, 35, 48, 66, 2],
            [55, 13, 16, 58, 5, 2, 1],  # note padding
            [64, 27, 31, 51, 12, 75, 2],
            [52, 64, 86, 17, 83, 39, 2],
            [48, 61, 9, 24, 71, 82, 2],
            [26, 1, 60, 48, 22, 13, 2],
            [21, 5, 62, 28, 14, 76, 2],
            [45, 98, 37, 86, 59, 48, 2],
            [70, 70, 50, 9, 28, 0, 2],
        ]			,							dtype=np.intaa			,							)

    __magic_name__				     =       input_ids.shape[0]
    __magic_name__				     =       BlenderbotConfig(
        vocab_size=self.vocab_size			,							d_model=24			,							encoder_layers=2			,							decoder_layers=2			,							encoder_attention_heads=2			,							decoder_attention_heads=2			,							encoder_ffn_dim=32			,							decoder_ffn_dim=32			,							max_position_embeddings=48			,							eos_token_id=2			,							pad_token_id=1			,							bos_token_id=0			,							)
    return config, input_ids, batch_size
  def 							_lowercase							( self			: Tuple    )			->	Dict:







    """simple docstring"""






    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       self._get_config_and_data()
    __magic_name__				     =       FlaxBlenderbotForConditionalGeneration(UpperCamelCase__    )
    __magic_name__				     =       lm_model(input_ids=UpperCamelCase__    )
    __magic_name__				     =       (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       BlenderbotConfig(
        vocab_size=self.vocab_size			,							d_model=14			,							encoder_layers=2			,							decoder_layers=2			,							encoder_attention_heads=2			,							decoder_attention_heads=2			,							encoder_ffn_dim=8			,							decoder_ffn_dim=8			,							max_position_embeddings=48			,							)
    __magic_name__				     =       FlaxBlenderbotForConditionalGeneration(UpperCamelCase__    )
    __magic_name__				     =       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,							dtype=np.intaa    )
    __magic_name__				     =       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,							dtype=np.intaa    )
    __magic_name__				     =       lm_model(input_ids=UpperCamelCase__			,							decoder_input_ids=UpperCamelCase__    )
    __magic_name__				     =       (*summary.shape, config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape			,							UpperCamelCase__    )





  def 							_lowercase							( self			: Union[str, Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,							dtype=np.intaa    )
    __magic_name__				     =       shift_tokens_right(UpperCamelCase__			,							1			,							2    )
    __magic_name__				     =       np.equal(UpperCamelCase__			,							1    ).astype(np.floataa    ).sum()
    __magic_name__				     =       np.equal(UpperCamelCase__			,							1    ).astype(np.floataa    ).sum()
    self.assertEqual(shifted.shape			,							input_ids.shape    )
    self.assertEqual(UpperCamelCase__			,							n_pad_before - 1    )
    self.assertTrue(np.equal(shifted[:, 0]			,							2    ).all()    )







@require_flax
class        UpperCAmelCase_			( _A  ,       unittest.TestCase  ,       _A     ):


  '''simple docstring'''







  a__           =		True
  a__           =		(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  a__           =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
  def 							_lowercase							( self			: List[str]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       FlaxBlenderbotModelTester(self    )
  def 							_lowercase							( self			: Optional[int]    )			->	Tuple:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	Dict:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Any    )			->	int:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__    ):
        __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )
        __magic_name__				     =       model_class(UpperCamelCase__    )

        @jax.jit
        def encode_jitted(UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Dict=None			,							**UpperCamelCase__			: Union[str, Any]    ):
          return model.encode(input_ids=UpperCamelCase__			,							attention_mask=UpperCamelCase__    )

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       encode_jitted(**UpperCamelCase__    ).to_tuple()

        with self.subTest("""JIT Disabled"""    ):
          with jax.disable_jit():
            __magic_name__				     =       encode_jitted(**UpperCamelCase__    ).to_tuple()

        self.assertEqual(len(UpperCamelCase__    )			,							len(UpperCamelCase__    )    )
        for jitted_output, output in zip(UpperCamelCase__			,							UpperCamelCase__    ):
          self.assertEqual(jitted_output.shape			,							output.shape    )
  def 							_lowercase							( self			: int    )			->	List[Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__    ):
        __magic_name__				     =       model_class(UpperCamelCase__    )
        __magic_name__				     =       model.encode(inputs_dict["""input_ids"""]			,							inputs_dict["""attention_mask"""]    )

        __magic_name__				     =       {
            """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
            """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
            """encoder_outputs""": encoder_outputs,
        }

        @jax.jit
        def decode_jitted(UpperCamelCase__			: Dict			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Optional[int]    ):
          return model.decode(
              decoder_input_ids=UpperCamelCase__			,							decoder_attention_mask=UpperCamelCase__			,							encoder_outputs=UpperCamelCase__			,							)

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       decode_jitted(**UpperCamelCase__    ).to_tuple()

        with self.subTest("""JIT Disabled"""    ):
          with jax.disable_jit():
            __magic_name__				     =       decode_jitted(**UpperCamelCase__    ).to_tuple()

        self.assertEqual(len(UpperCamelCase__    )			,							len(UpperCamelCase__    )    )
        for jitted_output, output in zip(UpperCamelCase__			,							UpperCamelCase__    ):
          self.assertEqual(jitted_output.shape			,							output.shape    )
  @slow
  def 							_lowercase							( self			: Dict    )			->	Optional[Any]:







    """simple docstring"""






    for model_class_name in self.all_model_classes:
      __magic_name__				     =       model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""    )
      # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
      __magic_name__				     =       np.ones((1, 1)    ) * model.config.eos_token_id
      __magic_name__				     =       model(UpperCamelCase__    )
      self.assertIsNotNone(UpperCamelCase__    )





  @unittest.skipUnless(jax_device != """cpu"""			,							"""3B test too slow on CPU."""    )
  @slow
  def 							_lowercase							( self			: Optional[int]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
    __magic_name__				     =       {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

    __magic_name__				     =       FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""			,							from_pt=UpperCamelCase__    )
    __magic_name__				     =       BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""    )

    __magic_name__				     =       ["""Sam"""]
    __magic_name__				     =       tokenizer(UpperCamelCase__			,							return_tensors="""jax"""    )

    __magic_name__				     =       model.generate(**UpperCamelCase__			,							**UpperCamelCase__    )
    __magic_name__				     =       """Sam is a great name. It means \"sun\" in Gaelic."""

    __magic_name__				     =       tokenizer.batch_decode(UpperCamelCase__			,							**UpperCamelCase__    )
    assert generated_txt[0].strip() == tgt_text



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()

