'''simple docstring'''





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)






class 				UpperCAmelCase (       unittest.TestCase ):




				'''simple docstring'''







				def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=5    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=16    ,							lowercase__=2    ,							lowercase__=0.0_2    ,							lowercase__=4    ,							)							-> Tuple:
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       parent
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       batch_size
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       seq_length
									SCREAMING_SNAKE_CASE					:			int    	=       is_training
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_attention_mask
									SCREAMING_SNAKE_CASE					:			str    	=       use_token_type_ids
									SCREAMING_SNAKE_CASE					:			int    	=       use_labels
									SCREAMING_SNAKE_CASE					:			List[str]    	=       vocab_size
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_size
									SCREAMING_SNAKE_CASE					:			Dict    	=       num_hidden_layers
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       num_attention_heads
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       intermediate_size
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       hidden_act
									SCREAMING_SNAKE_CASE					:			int    	=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       max_position_embeddings
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       type_vocab_size
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       type_sequence_label_size
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       initializer_range
									SCREAMING_SNAKE_CASE					:			str    	=       num_choices






				def       _UpperCamelCase    (       self )							-> Tuple:
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

									SCREAMING_SNAKE_CASE					:			Tuple    	=       None
									if self.use_attention_mask:
														SCREAMING_SNAKE_CASE					:			Optional[int]    	=       random_attention_mask([self.batch_size, self.seq_length] )

									SCREAMING_SNAKE_CASE					:			List[Any]    	=       None
									if self.use_token_type_ids:
														SCREAMING_SNAKE_CASE					:			Tuple    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size )

									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							type_vocab_size=self.type_vocab_size    ,							is_decoder=lowerCamelCase__    ,							initializer_range=self.initializer_range    ,							)

									return config, input_ids, token_type_ids, attention_mask






				def       _UpperCamelCase    (       self )							-> Tuple:
									SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE					:			Tuple    	=       config_and_inputs
									SCREAMING_SNAKE_CASE					:			List[str]    	=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict






				def       _UpperCamelCase    (       self )							-> List[Any]:
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       config_and_inputs

									SCREAMING_SNAKE_CASE					:			int    	=       True
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
									SCREAMING_SNAKE_CASE					:			Any    	=       ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2 )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 				UpperCAmelCase (       __lowerCAmelCase   ,    unittest.TestCase ):




				'''simple docstring'''







				snake_case__							:   List[Any]							=						True

				snake_case__							:   Optional[Any]							=						(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)






				def       _UpperCamelCase    (       self )							-> Any:
									SCREAMING_SNAKE_CASE					:			Any    	=       FlaxRobertaPreLayerNormModelTester(self )






				@slow
				def       _UpperCamelCase    (       self )							-> List[str]:






									for model_class_name in self.all_model_classes:
														SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,							from_pt=lowerCamelCase__ )
														SCREAMING_SNAKE_CASE					:			Dict    	=       model(np.ones((1, 1) ) )
														self.assertIsNotNone(lowerCamelCase__ )






@require_flax
class 				UpperCAmelCase (       unittest.TestCase ):




				'''simple docstring'''







				@slow
				def       _UpperCamelCase    (       self )							-> str:
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,							from_pt=lowerCamelCase__ )

									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,							dtype=jnp.intaa )
									SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowerCamelCase__ )[0]
									SCREAMING_SNAKE_CASE					:			int    	=       [1, 11, 50_265]
									self.assertEqual(list(output.shape )    ,							lowerCamelCase__ )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]    ,							dtype=np.floataa )
									self.assertTrue(np.allclose(output[:, :3, :3]    ,							lowerCamelCase__    ,							atol=1E-4 ) )






				@slow
				def       _UpperCamelCase    (       self )							-> List[str]:
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,							from_pt=lowerCamelCase__ )

									SCREAMING_SNAKE_CASE					:			Dict    	=       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,							dtype=jnp.intaa )
									SCREAMING_SNAKE_CASE					:			str    	=       model(lowerCamelCase__ )[0]
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE					:			Dict    	=       np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]    ,							dtype=np.floataa )
									self.assertTrue(np.allclose(output[:, :3, :3]    ,							lowerCamelCase__    ,							atol=1E-4 ) )




'''simple docstring'''





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


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


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





     '''simple docstring'''





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

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

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

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

     def collate_fn(a_		):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               SCREAMING_SNAKE_CASE					:			Any    	=       16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE					:			Any    	=       8
          else:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       None

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

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

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

     return train_dataloader, eval_dataloader, test_dataloader


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





     '''simple docstring'''





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

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

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

     set_seed(a_		)

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

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

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

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

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

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

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

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

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


def     __lowerCAmelCase						(       )      ->	Tuple:





     '''simple docstring'''





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


if __name__ == "__main__":
 main()



