
'''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 the checkpointing capability,
# 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
#
########################################################################

__lowercase							:				str					=       16
__lowercase							:				Any					=       32




def lowercase_		(				_lowercase     ,    _lowercase = 16			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Optional[Any]       =						AutoTokenizer.from_pretrained('''bert-base-cased'''			)
					lowerCamelCase_   : List[Any]       =						load_dataset('''glue'''     ,    '''mrpc'''			)

					def tokenize_function(_lowercase			):
										# max_length=None => use the model max length (it's actually the default)
										lowerCamelCase_   : List[Any]       =						tokenizer(examples['''sentence1''']     ,    examples['''sentence2''']     ,    truncation=_lowercase     ,    max_length=_lowercase			)
										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(
										    _lowercase     ,    batched=_lowercase     ,    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_   : Tuple       =						tokenized_datasets.rename_column('''label'''     ,    '''labels'''			)

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

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

					# Instantiate dataloaders.
					lowerCamelCase_   : Union[str, Any]       =						DataLoader(
					    tokenized_datasets['''train''']     ,    shuffle=_lowercase     ,    collate_fn=_lowercase     ,    batch_size=_lowercase			)
					lowerCamelCase_   : List[str]       =						DataLoader(
					    tokenized_datasets['''validation''']     ,    shuffle=_lowercase     ,    collate_fn=_lowercase     ,    batch_size=_lowercase			)

					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

					__lowercase							:				Dict					=       mocked_dataloaders  # noqa: F811




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							Tuple:
					'''simple docstring'''

					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,    _lowercase			) == "1":
										lowerCamelCase_   : str       =						2
					# Initialize accelerator
					lowerCamelCase_   : int       =						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_   : Tuple       =						int(config['''num_epochs''']			)
					lowerCamelCase_   : Union[str, Any]       =						int(config['''seed''']			)
					lowerCamelCase_   : Optional[Any]       =						int(config['''batch_size''']			)

					# New Code #
					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps     ,    '''isdigit'''			):
										if args.checkpointing_steps == "epoch":
															lowerCamelCase_   : Optional[Any]       =						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															lowerCamelCase_   : Any       =						int(args.checkpointing_steps			)
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
					else:
										lowerCamelCase_   : int       =						None

					set_seed(_lowercase			)

					lowerCamelCase_, lowerCamelCase_   : int       =						get_dataloaders(_lowercase     ,    _lowercase			)
					lowerCamelCase_   : Optional[Any]       =						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_   : Any       =						MAX_GPU_BATCH_SIZE

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowerCamelCase_   : Tuple       =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,    return_dict=_lowercase			)

					# 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_   : Any       =						model.to(accelerator.device			)

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

					# Instantiate scheduler
					lowerCamelCase_   : Optional[int]       =						get_linear_schedule_with_warmup(
					    optimizer=_lowercase     ,    num_warmup_steps=100     ,    num_training_steps=(len(_lowercase			) * 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_   : Optional[int]       =						accelerator.prepare(
					    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase			)

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					lowerCamelCase_   : str       =						0
					# We also need to keep track of the stating epoch so files are named properly
					lowerCamelCase_   : List[str]       =						0

					# We need to load the checkpoint back in before training here with `load_state`
					# The total number of epochs is adjusted based on where the state is being loaded from,
					# as we assume continuation of the same training script
					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			)
															lowerCamelCase_   : List[Any]       =						os.path.basename(args.resume_from_checkpoint			)
										else:
															# Get the most recent checkpoint
															lowerCamelCase_   : List[str]       =						[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
															dirs.sort(key=os.path.getctime			)
															lowerCamelCase_   : Dict       =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										lowerCamelCase_   : List[str]       =						os.path.splitext(_lowercase			)[0]

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

    # Now we train the model
					for epoch in range(_lowercase     ,    _lowercase			):
										model.train()
										# New Code #
										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
															lowerCamelCase_   : Tuple       =						accelerator.skip_first_batches(_lowercase     ,    _lowercase			)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															lowerCamelCase_   : int       =						train_dataloader
										for step, batch in enumerate(_lowercase			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device			)
															lowerCamelCase_   : Union[str, Any]       =						model(**_lowercase			)
															lowerCamelCase_   : int       =						outputs.loss
															lowerCamelCase_   : int       =						loss / gradient_accumulation_steps
															accelerator.backward(_lowercase			)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
															# New Code #
															overall_step += 1

															# New Code #
															# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
															# These are saved to folders named `step_{overall_step}`
															# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
															# If mixed precision was used, will also save a "scalar.bin" file
															if isinstance(_lowercase     ,    _lowercase			):
																				lowerCamelCase_   : Optional[Any]       =						F"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														lowerCamelCase_   : Any       =						os.path.join(args.output_dir     ,    _lowercase			)
																									accelerator.save_state(_lowercase			)

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

										lowerCamelCase_   : Tuple       =						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""     ,    _lowercase			)

										# New Code #
										# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
										# These are saved to folders named `epoch_{epoch}`
										# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
										# If mixed precision was used, will also save a "scalar.bin" file
										if checkpointing_steps == "epoch":
															lowerCamelCase_   : Any       =						F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				lowerCamelCase_   : Optional[int]       =						os.path.join(args.output_dir     ,    _lowercase			)
															accelerator.save_state(_lowercase			)




def lowercase_		(				)       ->							Any:
					'''simple docstring'''

					lowerCamelCase_   : int       =						argparse.ArgumentParser(description='''Simple example of training script.'''			)
					parser.add_argument(
					    '''--mixed_precision'''     ,    type=_lowercase     ,    default=_lowercase     ,    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=_lowercase     ,    default=_lowercase     ,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''     ,    )
					parser.add_argument(
					    '''--output_dir'''     ,    type=_lowercase     ,    default='''.'''     ,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''     ,    )
					parser.add_argument(
					    '''--resume_from_checkpoint'''     ,    type=_lowercase     ,    default=_lowercase     ,    help='''If the training should continue from a checkpoint folder.'''     ,    )
					lowerCamelCase_   : List[Any]       =						parser.parse_args()
					lowerCamelCase_   : Optional[Any]       =						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(_lowercase     ,    _lowercase			)


if __name__ == "__main__":
					main()




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


