
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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE     :   Any							 =      1_6
_SCREAMING_SNAKE_CASE     :   Dict							 =      3_2

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    Any							)			->							int:

				"""simple docstring"""







				if args.with_tracking:
								A__	:						List[str]								=				Accelerator(
								    cpu=args.cpu				,    mixed_precision=args.mixed_precision				,    log_with='''all'''				,    project_dir=args.project_dir							)
				else:
								A__	:						Dict								=				Accelerator(cpu=args.cpu				,    mixed_precision=args.mixed_precision							)

				if hasattr(args.checkpointing_steps				,    '''isdigit'''							):
								if args.checkpointing_steps == "epoch":
												A__	:						Tuple								=				args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												A__	:						Dict								=				int(args.checkpointing_steps							)
								else:
												raise ValueError(
												    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."							)
				else:
								A__	:						int								=				None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__	:						Any								=				config['''lr''']
				A__	:						Any								=				int(config['''num_epochs''']							)
				A__	:						List[str]								=				int(config['''seed''']							)
				A__	:						List[Any]								=				int(config['''batch_size''']							)

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								A__	:						str								=				os.path.split(__UpperCamelCase							)[-1].split('''.'''							)[0]
								accelerator.init_trackers(__UpperCamelCase				,    __UpperCamelCase							)

				A__	:						Optional[int]								=				AutoTokenizer.from_pretrained('''bert-base-cased'''							)
				A__	:						Any								=				load_dataset('''glue'''				,    '''mrpc'''							)
				A__	:						Union[str, Any]								=				evaluate.load('''glue'''				,    '''mrpc'''							)

				def tokenize_function(__UpperCamelCase			:    Any							):
								# max_length=None => use the model max length (it's actually the default)
								A__	:						Optional[int]								=				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():
								A__	:						Optional[Any]								=				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
				A__	:						Dict								=				tokenized_datasets.rename_column('''label'''				,    '''labels'''							)

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

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

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

				# Instantiate dataloaders.
				A__	:						Tuple								=				DataLoader(
				    tokenized_datasets['''train''']				,    shuffle=__UpperCamelCase				,    collate_fn=__UpperCamelCase				,    batch_size=__UpperCamelCase							)
				A__	:						Union[str, Any]								=				DataLoader(
				    tokenized_datasets['''validation''']				,    shuffle=__UpperCamelCase				,    collate_fn=__UpperCamelCase				,    batch_size=__UpperCamelCase							)

				set_seed(__UpperCamelCase							)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__	:						Optional[int]								=				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).
				A__	:						Dict								=				model.to(accelerator.device							)

				# Instantiate optimizer
				A__	:						Optional[int]								=				AdamW(params=model.parameters()				,    lr=__UpperCamelCase							)

				# Instantiate scheduler
				A__	:						Optional[int]								=				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.
				A__							,				A__							,				A__							,				A__							,				A__	:						int								=				accelerator.prepare(
				    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

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

				# Potentially load in the weights and states from a previous save
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"							)
												accelerator.load_state(args.resume_from_checkpoint							)
												A__	:						Optional[int]								=				os.path.basename(args.resume_from_checkpoint							)
								else:
												# Get the most recent checkpoint
												A__	:						int								=				[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
												dirs.sort(key=os.path.getctime							)
												A__	:						Dict								=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								A__	:						List[str]								=				os.path.splitext(__UpperCamelCase							)[0]

								if "epoch" in training_difference:
												A__	:						Dict								=				int(training_difference.replace('''epoch_'''				,    ''''''							)							) + 1
												A__	:						Optional[int]								=				None
								else:
												A__	:						List[Any]								=				int(training_difference.replace('''step_'''				,    ''''''							)							)
												A__	:						Dict								=				resume_step // len(__UpperCamelCase							)
												resume_step -= starting_epoch * len(__UpperCamelCase							)

    # Now we train the model
				for epoch in range(__UpperCamelCase				,    __UpperCamelCase							):
								model.train()
								if args.with_tracking:
												A__	:						Optional[int]								=				0
								if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
												# We need to skip steps until we reach the resumed step
												A__	:						Optional[Any]								=				accelerator.skip_first_batches(__UpperCamelCase				,    __UpperCamelCase							)
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												A__	:						Optional[int]								=				train_dataloader
								for step, batch in enumerate(__UpperCamelCase							):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device							)
												A__	:						List[Any]								=				model(**__UpperCamelCase							)
												A__	:						Optional[int]								=				outputs.loss
												A__	:						Optional[int]								=				loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(__UpperCamelCase							)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

												if isinstance(__UpperCamelCase				,    __UpperCamelCase							):
																A__	:						int								=				F"step_{overall_step}"
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								A__	:						Optional[int]								=				os.path.join(args.output_dir				,    __UpperCamelCase							)
																				accelerator.save_state(__UpperCamelCase							)

								model.eval()
								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():
																A__	:						Optional[Any]								=				model(**__UpperCamelCase							)
												A__	:						List[str]								=				outputs.logits.argmax(dim=-1							)
												A__							,				A__	:						Union[str, Any]								=				accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
												metric.add_batch(
												    predictions=__UpperCamelCase				,    references=__UpperCamelCase				,    )

								A__	:						Optional[int]								=				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,    __UpperCamelCase							)
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': eval_metric['''accuracy'''],
												        '''f1''': eval_metric['''f1'''],
												        '''train_loss''': total_loss.item() / len(__UpperCamelCase							),
												        '''epoch''': epoch,
												    }				,    step=__UpperCamelCase				,    )

								if checkpointing_steps == "epoch":
												A__	:						Optional[Any]								=				F"epoch_{epoch}"
												if args.output_dir is not None:
																A__	:						List[Any]								=				os.path.join(args.output_dir				,    __UpperCamelCase							)
												accelerator.save_state(__UpperCamelCase							)

				if args.with_tracking:
								accelerator.end_training()

def 					SCREAMING_SNAKE_CASE  (   )			->							List[Any]:

				"""simple docstring"""







				A__	:						List[Any]								=				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.'''							)
				parser.add_argument(
				    '''--checkpointing_steps'''				,    type=__UpperCamelCase				,    default=__UpperCamelCase				,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,    )
				parser.add_argument(
				    '''--resume_from_checkpoint'''				,    type=__UpperCamelCase				,    default=__UpperCamelCase				,    help='''If the training should continue from a checkpoint folder.'''				,    )
				parser.add_argument(
				    '''--with_tracking'''				,    action='''store_true'''				,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''				,    )
				parser.add_argument(
				    '''--output_dir'''				,    type=__UpperCamelCase				,    default='''.'''				,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,    )
				parser.add_argument(
				    '''--project_dir'''				,    type=__UpperCamelCase				,    default='''logs'''				,    help='''Location on where to store experiment tracking logs` and relevent project information'''				,    )
				A__	:						Optional[Any]								=				parser.parse_args()
				A__	:						List[Any]								=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase				,    __UpperCamelCase							)


if __name__ == "__main__":
					main()

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      [
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Dict							 =      [
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)