





'''simple docstring'''




import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


a__					:			Optional[int]          =     16
a__					:			str          =     32




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = 16							)   ->List[Any]:
	snake_case__	       =   AutoTokenizer.from_pretrained('bert-base-cased'							)
	snake_case__	       =   load_dataset('glue'					,				'mrpc'							)

	def tokenize_function(UpperCAmelCase_							):
		# max_length=None => use the model max length (it's actually the default)
		snake_case__	       =   tokenizer(examples['sentence1']					,				examples['sentence2']					,				truncation=UpperCAmelCase_					,				max_length=UpperCAmelCase_							)
		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():
		snake_case__	       =   datasets.map(
		    UpperCAmelCase_					,				batched=UpperCAmelCase_					,				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
	snake_case__	       =   tokenized_datasets.rename_column('label'					,				'labels'							)

	def collate_fn(UpperCAmelCase_							):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		snake_case__	       =   1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			snake_case__	       =   16
		elif accelerator.mixed_precision != "no":
			snake_case__	       =   8
		else:
			snake_case__	       =   None

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

	# Instantiate dataloaders.
	snake_case__	       =   DataLoader(
	    tokenized_datasets['train']					,				shuffle=UpperCAmelCase_					,				collate_fn=UpperCAmelCase_					,				batch_size=UpperCAmelCase_							)
	snake_case__	       =   DataLoader(
	    tokenized_datasets['validation']					,				shuffle=UpperCAmelCase_					,				collate_fn=UpperCAmelCase_					,				batch_size=UpperCAmelCase_							)

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			a__					:			Optional[Any]          =     mocked_dataloaders  # noqa: F811




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'					,				UpperCAmelCase_							) == "1":
		snake_case__	       =   2
	# Initialize accelerator
	snake_case__	       =   Accelerator(cpu=args.cpu					,				mixed_precision=args.mixed_precision							)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	snake_case__	       =   config['lr']
	snake_case__	       =   int(config['num_epochs']							)
	snake_case__	       =   int(config['seed']							)
	snake_case__	       =   int(config['batch_size']							)

	snake_case__	       =   evaluate.load('glue'					,				'mrpc'							)

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

	set_seed(UpperCAmelCase_							)
	snake_case__       ,  snake_case__	       =   get_dataloaders(UpperCAmelCase_					,				UpperCAmelCase_							)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case__	       =   AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,				return_dict=UpperCAmelCase_							)

	# 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).
	snake_case__	       =   model.to(accelerator.device							)

	# Instantiate optimizer
	snake_case__	       =   AdamW(params=model.parameters()					,				lr=UpperCAmelCase_							)

	# Instantiate scheduler
	snake_case__	       =   get_linear_schedule_with_warmup(
	    optimizer=UpperCAmelCase_					,				num_warmup_steps=1_00					,				num_training_steps=(len(UpperCAmelCase_							) * 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.
	snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   accelerator.prepare(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

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

		model.eval()
		snake_case__	       =   0
		for step, batch in enumerate(UpperCAmelCase_							):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device							)
			with torch.no_grad():
				snake_case__	       =   model(**UpperCAmelCase_							)
			snake_case__	       =   outputs.logits.argmax(dim=-1							)
			snake_case__       ,  snake_case__	       =   accelerator.gather((predictions, batch['labels'])							)
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(UpperCAmelCase_							) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					snake_case__	       =   predictions[: len(eval_dataloader.dataset							) - samples_seen]
					snake_case__	       =   references[: len(eval_dataloader.dataset							) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=UpperCAmelCase_					,				references=UpperCAmelCase_					,				)

		snake_case__	       =   metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'''epoch {epoch}:'''					,				UpperCAmelCase_							)




def 						__lowerCamelCase			(	)   ->Optional[Any]:
	snake_case__	       =   argparse.ArgumentParser(description='Simple example of training script.'							)
	parser.add_argument(
	    '--mixed_precision'					,				type=UpperCAmelCase_					,				default=UpperCAmelCase_					,				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.'							)
	snake_case__	       =   parser.parse_args()
	snake_case__	       =   {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(UpperCAmelCase_					,				UpperCAmelCase_							)


if __name__ == "__main__":
			main()










'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)






class 				__snake_case     (					unittest.TestCase							):





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





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

		snake_case__	       =   None
		if self.use_attention_mask:
			snake_case__	       =   random_attention_mask([self.batch_size, self.seq_length]		)

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

		snake_case__	       =   RobertaConfig(
		    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=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs

		snake_case__	       =   True
		snake_case__	       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
		snake_case__	       =   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
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

	__lowerCAmelCase       						=  (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



