



"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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  			=		"""platform"""

  import jax
  import jax.numpy as jnp

  from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
      FlaxBlenderbotSmallForConditionalGeneration,
      FlaxBlenderbotSmallModel,
      shift_tokens_right,
  )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__=None      ,     lowerCAmelCase__=None      ,     lowerCAmelCase__=None      ,     lowerCAmelCase__=None      ,     lowerCAmelCase__=None      ,     lowerCAmelCase__=None      ,     ):
      if attention_mask is None:
            UpperCAmelCase_     					=				np.where(input_ids != config.pad_token_id      ,     1      ,     0 )
      if decoder_attention_mask is None:
            UpperCAmelCase_     					=				np.where(decoder_input_ids != config.pad_token_id      ,     1      ,     0 )
      if head_mask is None:
            UpperCAmelCase_     					=				np.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
            UpperCAmelCase_     					=				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
            UpperCAmelCase_     					=				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  lowercase__  :







       '''simple docstring'''


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


             '''simple docstring'''




             UpperCAmelCase_     					=				parent
             UpperCAmelCase_     					=				batch_size
             UpperCAmelCase_     					=				seq_length
             UpperCAmelCase_     					=				is_training
             UpperCAmelCase_     					=				use_labels
             UpperCAmelCase_     					=				vocab_size
             UpperCAmelCase_     					=				hidden_size
             UpperCAmelCase_     					=				num_hidden_layers
             UpperCAmelCase_     					=				num_attention_heads
             UpperCAmelCase_     					=				intermediate_size
             UpperCAmelCase_     					=				hidden_act
             UpperCAmelCase_     					=				hidden_dropout_prob
             UpperCAmelCase_     					=				attention_probs_dropout_prob
             UpperCAmelCase_     					=				max_position_embeddings
             UpperCAmelCase_     					=				eos_token_id
             UpperCAmelCase_     					=				pad_token_id
             UpperCAmelCase_     					=				bos_token_id
             UpperCAmelCase_     					=				initializer_range



       def 	lowercase__							(				self     :    List[str]       )					->		int:


             '''simple docstring'''




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

             UpperCAmelCase_     					=				shift_tokens_right(_UpperCAmelCase						,							1						,							2       )

             UpperCAmelCase_     					=				BlenderbotSmallConfig(
                 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						,							)
             UpperCAmelCase_     					=				prepare_blenderbot_inputs_dict(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       )
             return config, inputs_dict



       def 	lowercase__							(				self     :    Optional[Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_     					=				self.prepare_config_and_inputs()
             return config, inputs_dict



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    int       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				20
             UpperCAmelCase_     					=				model_class_name(_UpperCAmelCase       )

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

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

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

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

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

             UpperCAmelCase_     					=				model.decode(_UpperCAmelCase						,							_UpperCAmelCase       )

             UpperCAmelCase_     					=				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     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Optional[int]       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				20
             UpperCAmelCase_     					=				model_class_name(_UpperCAmelCase       )

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

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

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

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

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

             UpperCAmelCase_     					=				model.decode(_UpperCAmelCase						,							_UpperCAmelCase						,							decoder_attention_mask=_UpperCAmelCase       )

             UpperCAmelCase_     					=				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  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	99



       def 	lowercase__							(				self     :    Dict       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				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						,							)

             UpperCAmelCase_     					=				input_ids.shape[0]
             UpperCAmelCase_     					=				BlenderbotSmallConfig(
                 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     :    int       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				self._get_config_and_data()
             UpperCAmelCase_     					=				FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase       )
             UpperCAmelCase_     					=				lm_model(input_ids=_UpperCAmelCase       )
             UpperCAmelCase_     					=				(batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["logits"].shape						,							_UpperCAmelCase       )



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


             '''simple docstring'''




             UpperCAmelCase_     					=				BlenderbotSmallConfig(
                 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						,							)
             UpperCAmelCase_     					=				FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase       )
             UpperCAmelCase_     					=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,							dtype=np.intaa       )
             UpperCAmelCase_     					=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,							dtype=np.intaa       )
             UpperCAmelCase_     					=				lm_model(input_ids=_UpperCAmelCase						,							decoder_input_ids=_UpperCAmelCase       )
             UpperCAmelCase_     					=				(*summary.shape, config.vocab_size)
             self.assertEqual(outputs["logits"].shape						,							_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Any       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,							dtype=np.intaa       )
             UpperCAmelCase_     					=				shift_tokens_right(_UpperCAmelCase						,							1						,							2       )
             UpperCAmelCase_     					=				np.equal(_UpperCAmelCase						,							1       ).astype(np.floataa       ).sum()
             UpperCAmelCase_     					=				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  lowercase__  (       SCREAMING_SNAKE_CASE							,   unittest.TestCase							,   SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	True
       UpperCamelCase		=	(
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase		=	(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



       def 	lowercase__							(				self     :    List[Any]       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				FlaxBlenderbotSmallModelTester(self       )



       def 	lowercase__							(				self     :    List[Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_     					=				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     :    int       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_     					=				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     :    int       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__       ):
                         UpperCAmelCase_     					=				self._prepare_for_class(_UpperCAmelCase						,							_UpperCAmelCase       )
                         UpperCAmelCase_     					=				model_class(_UpperCAmelCase       )

                         @jax.jit
                         def encode_jitted(_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Optional[int]=None						,							**_UpperCAmelCase     :    Tuple       ):
                               return model.encode(input_ids=_UpperCAmelCase						,							attention_mask=_UpperCAmelCase       )

                         with self.subTest("JIT Enabled"       ):
                               UpperCAmelCase_     					=				encode_jitted(**_UpperCAmelCase       ).to_tuple()

                         with self.subTest("JIT Disabled"       ):
                               with jax.disable_jit():
                                     UpperCAmelCase_     					=				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       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs_for_common()

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

                         UpperCAmelCase_     					=				{
                             "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     :    Tuple						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Tuple       ):
                               return model.decode(
                                   decoder_input_ids=_UpperCAmelCase						,							decoder_attention_mask=_UpperCAmelCase						,							encoder_outputs=_UpperCAmelCase						,							)

                         with self.subTest("JIT Enabled"       ):
                               UpperCAmelCase_     					=				decode_jitted(**_UpperCAmelCase       ).to_tuple()

                         with self.subTest("JIT Disabled"       ):
                               with jax.disable_jit():
                                     UpperCAmelCase_     					=				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     :    str       )					->		Tuple:


             '''simple docstring'''




             for model_class_name in self.all_model_classes:
                   UpperCAmelCase_     					=				model_class_name.from_pretrained("facebook/blenderbot_small-90M"       )
                   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                   UpperCAmelCase_     					=				np.ones((1, 1)       ) * model.config.eos_token_id
                   UpperCAmelCase_     					=				model(_UpperCAmelCase       )
                   self.assertIsNotNone(_UpperCAmelCase       )











"""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
#
########################################################################


_UpperCamelCase					      =   16
_UpperCamelCase					      =   32



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Accelerator     ,    SCREAMING_SNAKE_CASE							:				DatasetDict     ,    SCREAMING_SNAKE_CASE							:				List[int]     ,    SCREAMING_SNAKE_CASE							:				List[int]     ,    SCREAMING_SNAKE_CASE							:				int = 16 ):


							'''simple docstring'''







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

							def tokenize_function(SCREAMING_SNAKE_CASE							:				Optional[Any] ):
														# max_length=None => use the model max length (it's actually the default)
														__lowerCamelCase   :   Optional[int]       =tokenizer(examples['''sentence1''']     ,    examples['''sentence2''']     ,    truncation=SCREAMING_SNAKE_CASE     ,    max_length=SCREAMING_SNAKE_CASE )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														__lowerCamelCase   :   Optional[Any]       =datasets.map(
														    SCREAMING_SNAKE_CASE     ,    batched=SCREAMING_SNAKE_CASE     ,    remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,    )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							__lowerCamelCase   :   Dict       =tokenized_datasets.rename_column('''label'''     ,    '''labels''' )

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

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

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

							__lowerCamelCase   :   Tuple       =DataLoader(
							    tokenized_datasets['''test''']     ,    shuffle=SCREAMING_SNAKE_CASE     ,    collate_fn=SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE )

							return train_dataloader, eval_dataloader, test_dataloader



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				List[Any] ):


							'''simple docstring'''







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

							__lowerCamelCase   :   List[str]       =evaluate.load('''glue'''     ,    '''mrpc''' )

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

							set_seed(SCREAMING_SNAKE_CASE )

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

														# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
														# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
														# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
														__lowerCamelCase   :   Optional[Any]       =model.to(accelerator.device )

														# Instantiate optimizer
														__lowerCamelCase   :   Any       =AdamW(params=model.parameters()     ,    lr=SCREAMING_SNAKE_CASE )

														# Instantiate scheduler
														__lowerCamelCase   :   Dict       =get_linear_schedule_with_warmup(
														    optimizer=SCREAMING_SNAKE_CASE     ,    num_warmup_steps=100     ,    num_training_steps=(len(SCREAMING_SNAKE_CASE ) * num_epochs) // gradient_accumulation_steps     ,    )

														# Prepare everything
														# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
														# prepare method.
														__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   Any       =accelerator.prepare(
														    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

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

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

																					__lowerCamelCase   :   str       =metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F'epoch {epoch}:'     ,    SCREAMING_SNAKE_CASE )

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



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =argparse.ArgumentParser(description='''Simple example of training script.''' )
							parser.add_argument(
							    '''--mixed_precision'''     ,    type=SCREAMING_SNAKE_CASE     ,    default=SCREAMING_SNAKE_CASE     ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     ,    help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''     ,    )
							parser.add_argument('''--cpu'''     ,    action='''store_true'''     ,    help='''If passed, will train on the CPU.''' )
							# New Code #
							parser.add_argument('''--num_folds'''     ,    type=SCREAMING_SNAKE_CASE     ,    default=3     ,    help='''The number of splits to perform across the dataset''' )
							__lowerCamelCase   :   Any       =parser.parse_args()
							__lowerCamelCase   :   Optional[int]       ={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							main()




