"""simple docstring"""




import argparse

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)
#
# 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							 =     16
__lowerCamelCase							 =     32






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ = 16 ):






							"""simple docstring"""





							A__		   =							AutoTokenizer.from_pretrained('bert-base-cased' )
							A__		   =							load_dataset('glue'				,			'mrpc' )

							def tokenize_function(UpperCamelCase__ ):
														# max_length=None => use the model max length (it's actually the default)
														A__		   =							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__		   =							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__		   =							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.
														A__		   =							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":
																					A__		   =							16
														elif accelerator.mixed_precision != "no":
																					A__		   =							8
														else:
																					A__		   =							None

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

							# Instantiate dataloaders.
							A__		   =							DataLoader(
							    tokenized_datasets['train']				,			shuffle=UpperCamelCase__				,			collate_fn=UpperCamelCase__				,			batch_size=UpperCamelCase__				,			drop_last=UpperCamelCase__ )
							A__		   =							DataLoader(
							    tokenized_datasets['validation']				,			shuffle=UpperCamelCase__				,			collate_fn=UpperCamelCase__				,			batch_size=UpperCamelCase__				,			drop_last=(accelerator.mixed_precision == 'fp8')				,			)

							return train_dataloader, eval_dataloader






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							Accelerator(cpu=args.cpu				,			mixed_precision=args.mixed_precision )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							A__		   =							config['lr']
							A__		   =							int(config['num_epochs'] )
							A__		   =							int(config['seed'] )
							A__		   =							int(config['batch_size'] )

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

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

							set_seed(UpperCamelCase__ )
							A__   ,     A__		   =							get_dataloaders(UpperCamelCase__				,			UpperCamelCase__ )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A__		   =							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__		   =							model.to(accelerator.device )
							# Instantiate optimizer
							A__		   =							AdamW(params=model.parameters()				,			lr=UpperCamelCase__ )

							# Instantiate scheduler
							A__		   =							get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase__				,			num_warmup_steps=100				,			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__		   =							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 )
																					A__		   =							model(**UpperCamelCase__ )
																					A__		   =							outputs.loss
																					A__		   =							loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase__ )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														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__		   =							model(**UpperCamelCase__ )
																					A__		   =							outputs.logits.argmax(dim=-1 )
																					A__   ,     A__		   =							accelerator.gather_for_metrics((predictions, batch['labels']) )
																					metric.add_batch(
																					    predictions=UpperCamelCase__				,			references=UpperCamelCase__				,			)

														A__		   =							metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''				,			UpperCamelCase__ )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							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.' )
							A__		   =							parser.parse_args()
							A__		   =							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(UpperCamelCase__				,			UpperCamelCase__ )


if __name__ == "__main__":
				main()






"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["VivitImageProcessor"]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VivitModel",
				    "VivitPreTrainedModel",
				    "VivitForVideoClassification",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





