





import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase				:	Dict      						=  1_6
lowercase				:	List[Any]      						=  3_2



def A_     (		A__		,				A__ = 16		,				A__ = "bert-base-cased"    )  ->		Dict:
							a__						:     List[str]			=    AutoTokenizer.from_pretrained(A__    )
							a__						:     Union[str, Any]			=    load_dataset('glue'		,				'mrpc'    )

							def tokenize_function(A__    ):
														# max_length=None => use the model max length (it's actually the default)
														a__						:     Tuple			=    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
							a__						:     List[str]			=    datasets.map(
							    A__		,				batched=A__		,				remove_columns=['idx', 'sentence1', 'sentence2']		,				load_from_cache_file=A__    )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							a__						:     List[str]			=    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.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(A__		,				padding='max_length'		,				max_length=128		,				return_tensors='pt'    )
														return tokenizer.pad(A__		,				padding='longest'		,				return_tensors='pt'    )

							# Instantiate dataloaders.
							a__						:     Dict			=    DataLoader(
							    tokenized_datasets['train']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )
							a__						:     Tuple			=    DataLoader(
							    tokenized_datasets['validation']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

							return train_dataloader, eval_dataloader



def A_     (		A__		,				A__    )  ->		List[str]:
							# Initialize accelerator
							a__						:     Any			=    Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							a__						:     List[Any]			=    config['lr']
							a__						:     int			=    int(config['num_epochs']    )
							a__						:     Optional[Any]			=    int(config['seed']    )
							a__						:     Optional[int]			=    int(config['batch_size']    )
							a__						:     Optional[int]			=    args.model_name_or_path

							set_seed(A__    )
							a__						:     str			=    get_dataloaders(A__		,				A__		,				A__    )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							a__						:     Tuple			=    AutoModelForSequenceClassification.from_pretrained(A__		,				return_dict=A__    )

							# Instantiate optimizer
							a__						:     str			=    (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							a__						:     int			=    optimizer_cls(params=model.parameters()		,				lr=A__    )

							if accelerator.state.deepspeed_plugin is not None:
														a__						:     List[str]			=    accelerator.state.deepspeed_plugin.deepspeed_config[
														    'gradient_accumulation_steps'
														]
							else:
														a__						:     Dict			=    1
							a__						:     Optional[int]			=    (len(A__    ) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														a__						:     Optional[Any]			=    get_linear_schedule_with_warmup(
														    optimizer=A__		,				num_warmup_steps=0		,				num_training_steps=A__		,				)
							else:
														a__						:     List[str]			=    DummyScheduler(A__		,				total_num_steps=A__		,				warmup_num_steps=0    )

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

							# We need to keep track of how many total steps we have iterated over
							a__						:     Dict			=    0
							# We also need to keep track of the stating epoch so files are named properly
							a__						:     Optional[Any]			=    0

							# Now we train the model
							a__						:     List[Any]			=    evaluate.load('glue'		,				'mrpc'    )
							a__						:     Optional[int]			=    0
							a__						:     Any			=    {}
							for epoch in range(A__		,				A__    ):
														model.train()
														for step, batch in enumerate(A__    ):
																					a__						:     Union[str, Any]			=    model(**A__    )
																					a__						:     List[Any]			=    outputs.loss
																					a__						:     Dict			=    loss / gradient_accumulation_steps
																					accelerator.backward(A__    )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														a__						:     int			=    0
														for step, batch in enumerate(A__    ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device    )
																					with torch.no_grad():
																												a__						:     Union[str, Any]			=    model(**A__    )
																					a__						:     Dict			=    outputs.logits.argmax(dim=-1    )
																					# It is slightly faster to call this once, than multiple times
																					a__						:     str			=    accelerator.gather(
																					    (predictions, batch['labels'])    )  # If we are in a multiprocess environment, the last batch has duplicates
																					if accelerator.use_distributed:
																												if step == len(A__    ) - 1:
																																			a__						:     int			=    predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																																			a__						:     Union[str, Any]			=    references[: len(eval_dataloader.dataset    ) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=A__		,				references=A__		,				)

														a__						:     Optional[int]			=    metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'epoch {epoch}:'		,				A__    )
														a__						:     Optional[int]			=    eval_metric['accuracy']

														if best_performance < eval_metric["accuracy"]:
																					a__						:     Any			=    eval_metric['accuracy']

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir		,				'all_results.json'    )		,				'w'    ) as f:
																					json.dump(A__		,				A__    )



def A_     (		)  ->		Tuple:
							a__						:     int			=    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
							parser.add_argument(
							    '--model_name_or_path'		,				type=A__		,				default='bert-base-cased'		,				help='Path to pretrained model or model identifier from huggingface.co/models.'		,				required=A__		,				)
							parser.add_argument(
							    '--output_dir'		,				type=A__		,				default='.'		,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,				)
							parser.add_argument(
							    '--performance_lower_bound'		,				type=A__		,				default=A__		,				help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'		,				)
							parser.add_argument(
							    '--num_epochs'		,				type=A__		,				default=3		,				help='Number of train epochs.'		,				)
							a__						:     Optional[Any]			=    parser.parse_args()
							a__						:     List[Any]			=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
							training_function(A__		,				A__    )


if __name__ == "__main__":
				main()









import unittest

import numpy as np

from transformers import BertConfig, 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():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )





class 			A__   (							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=0.02 ,  lowercase=4 ,  )      ->						Tuple:







          '''simple docstring'''

          a__						:     str			=    parent
          a__						:     Optional[Any]			=    batch_size
          a__						:     str			=    seq_length
          a__						:     int			=    is_training
          a__						:     str			=    use_attention_mask
          a__						:     List[str]			=    use_token_type_ids
          a__						:     Optional[Any]			=    use_labels
          a__						:     List[Any]			=    vocab_size
          a__						:     Tuple			=    hidden_size
          a__						:     Dict			=    num_hidden_layers
          a__						:     List[str]			=    num_attention_heads
          a__						:     int			=    intermediate_size
          a__						:     Any			=    hidden_act
          a__						:     Optional[int]			=    hidden_dropout_prob
          a__						:     Tuple			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Optional[int]			=    type_vocab_size
          a__						:     List[Any]			=    type_sequence_label_size
          a__						:     Union[str, Any]			=    initializer_range
          a__						:     str			=    num_choices

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

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

          a__						:     Dict			=    None
          if self.use_token_type_ids:
                 a__						:     int			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     Dict			=    BertConfig(
              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=lowercase ,  initializer_range=self.initializer_range ,  )

          return config, input_ids, token_type_ids, attention_mask

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     Optional[int]			=    config_and_inputs
          a__						:     str			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     int			=    config_and_inputs

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

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





@require_flax
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  True

   __A       :    Tuple	  =  (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[Any]			=    FlaxBertModelTester(self)

   @slow
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    FlaxBertModel.from_pretrained('bert-base-cased')
          a__						:     Optional[Any]			=    model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)


