


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
#
########################################################################

lowerCAmelCase__ :			Union[str, Any]         =16
lowerCAmelCase__ :			str         =32






def 	a__ (							A__,			A__ = 1_6							):
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				AutoTokenizer.from_pretrained('bert-base-cased'							)
			SCREAMING_SNAKE_CASE_		:    str 		=				load_dataset('glue',			'mrpc'							)

			def tokenize_function(A__							):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				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
			# starting with the main process first:
			with accelerator.main_process_first():
						SCREAMING_SNAKE_CASE_		:    Tuple 		=				datasets.map(
						    A__,			batched=A__,			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
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				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.
						SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									SCREAMING_SNAKE_CASE_		:    str 		=				1_6
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				8
						else:
									SCREAMING_SNAKE_CASE_		:    Any 		=				None

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

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

			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

				lowerCAmelCase__ :			Optional[int]         =mocked_dataloaders  # noqa: F811






def 	a__ (							A__,			A__							):
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS',			A__							) == "1":
						SCREAMING_SNAKE_CASE_		:    Dict 		=				2
			# Initialize accelerator
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				Accelerator(cpu=args.cpu,			mixed_precision=args.mixed_precision							)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				config['lr']
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				int(config['num_epochs']							)
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				int(config['seed']							)
			SCREAMING_SNAKE_CASE_		:    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":
									SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									SCREAMING_SNAKE_CASE_		:    Dict 		=				int(args.checkpointing_steps							)
						else:
									raise ValueError(
									    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
			else:
						SCREAMING_SNAKE_CASE_		:    List[str] 		=				None

			set_seed(A__							)

			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    str 		=				get_dataloaders(A__,			A__							)
			SCREAMING_SNAKE_CASE_		:    str 		=				evaluate.load('glue',			'mrpc'							)

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

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				AutoModelForSequenceClassification.from_pretrained('bert-base-cased',			return_dict=A__							)

			# 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).
			SCREAMING_SNAKE_CASE_		:    Dict 		=				model.to(accelerator.device							)

			# Instantiate optimizer
			SCREAMING_SNAKE_CASE_		:    str 		=				AdamW(params=model.parameters(),			lr=A__							)

			# Instantiate scheduler
			SCREAMING_SNAKE_CASE_		:    int 		=				get_linear_schedule_with_warmup(
			    optimizer=A__,			num_warmup_steps=1_0_0,			num_training_steps=(len(A__							) * 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.
			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				accelerator.prepare(
			    A__,			A__,			A__,			A__,			A__							)

			# New Code #
			# We need to keep track of how many total steps we have iterated over
			SCREAMING_SNAKE_CASE_		:    int 		=				0
			# We also need to keep track of the stating epoch so files are named properly
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				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							)
									SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				os.path.basename(args.resume_from_checkpoint							)
						else:
									# Get the most recent checkpoint
									SCREAMING_SNAKE_CASE_		:    Any 		=				[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
									dirs.sort(key=os.path.getctime							)
									SCREAMING_SNAKE_CASE_		:    int 		=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						SCREAMING_SNAKE_CASE_		:    Dict 		=				os.path.splitext(A__							)[0]

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

    # Now we train the model
			for epoch in range(A__,			A__							):
						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
									SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				accelerator.skip_first_batches(A__,			A__							)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									SCREAMING_SNAKE_CASE_		:    Tuple 		=				train_dataloader
						for step, batch in enumerate(A__							):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device							)
									SCREAMING_SNAKE_CASE_		:    int 		=				model(**A__							)
									SCREAMING_SNAKE_CASE_		:    List[str] 		=				outputs.loss
									SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				loss / gradient_accumulation_steps
									accelerator.backward(A__							)
									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(A__,			A__							):
												SCREAMING_SNAKE_CASE_		:    List[str] 		=				F'''step_{overall_step}'''
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		SCREAMING_SNAKE_CASE_		:    Tuple 		=				os.path.join(args.output_dir,			A__							)
															accelerator.save_state(A__							)

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

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

						# 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":
									SCREAMING_SNAKE_CASE_		:    Any 		=				F'''epoch_{epoch}'''
									if args.output_dir is not None:
												SCREAMING_SNAKE_CASE_		:    Tuple 		=				os.path.join(args.output_dir,			A__							)
									accelerator.save_state(A__							)






def 	a__ (							):
			SCREAMING_SNAKE_CASE_		:    str 		=				argparse.ArgumentParser(description='Simple example of training script.'							)
			parser.add_argument(
			    '--mixed_precision',			type=A__,			default=A__,			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=A__,			default=A__,			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=A__,			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=A__,			default=A__,			help='If the training should continue from a checkpoint folder.',			)
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				parser.parse_args()
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
			training_function(A__,			A__							)


if __name__ == "__main__":
				main()









import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput







@dataclass
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42







class   __lowercase						(unittest.TestCase     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				CustomOutput(images=np.random.rand(1 ,	3 ,	4 ,	4					)					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	np.ndarray					)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs['images'] ,	np.ndarray					)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0] ,	np.ndarray					)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				CustomOutput(images=[PIL.Image.new('RGB' ,	(4, 4)					)]					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	lowerCAmelCase__					)
										assert isinstance(outputs.images[0] ,	PIL.Image.Image					)
										assert isinstance(outputs['images'] ,	lowerCAmelCase__					)
										assert isinstance(outputs['images'][0] ,	PIL.Image.Image					)
										assert isinstance(outputs[0] ,	lowerCAmelCase__					)
										assert isinstance(outputs[0][0] ,	PIL.Image.Image					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				CustomOutput({'images': np.random.rand(1 ,	3 ,	4 ,	4					)}					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	np.ndarray					)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs['images'] ,	np.ndarray					)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0] ,	np.ndarray					)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										SCREAMING_SNAKE_CASE_		:    Any 		=				CustomOutput({'images': [PIL.Image.new('RGB' ,	(4, 4)					)]}					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	lowerCAmelCase__					)
										assert isinstance(outputs.images[0] ,	PIL.Image.Image					)
										assert isinstance(outputs['images'] ,	lowerCAmelCase__					)
										assert isinstance(outputs['images'][0] ,	PIL.Image.Image					)
										assert isinstance(outputs[0] ,	lowerCAmelCase__					)
										assert isinstance(outputs[0][0] ,	PIL.Image.Image					)





