






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

snake_case_   :	Any							      =16
snake_case_   :	Dict							      =32



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__ = 16	):


		'''simple docstring'''

		__A    =							AutoTokenizer.from_pretrained("bert-base-cased"	)
		__A    =							load_dataset("glue"					,     "mrpc"	)

		def tokenize_function(lowerCAmelCase__	):
				# max_length=None => use the model max length (it's actually the default)
				__A    =							tokenizer(examples["sentence1"]					,     examples["sentence2"]					,     truncation=lowerCAmelCase__					,     max_length=lowerCAmelCase__	)
				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(
				    lowerCAmelCase__					,     batched=lowerCAmelCase__					,     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(lowerCAmelCase__	):
				# 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(
				    lowerCAmelCase__					,     padding="longest"					,     max_length=lowerCAmelCase__					,     pad_to_multiple_of=lowerCAmelCase__					,     return_tensors="pt"					,     )

		# Instantiate dataloaders.
		__A    =							DataLoader(
		    tokenized_datasets["train"]					,     shuffle=lowerCAmelCase__					,     collate_fn=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							DataLoader(
		    tokenized_datasets["validation"]					,     shuffle=lowerCAmelCase__					,     collate_fn=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)

		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

					snake_case_   :	Any							      =mocked_dataloaders  # noqa: F811



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		if os.environ.get("TESTING_MOCKED_DATALOADERS"					,     lowerCAmelCase__	) == "1":
				__A    =							2
		# Initialize accelerator
		__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"]	)

		# 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":
						__A    =							args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						__A    =							int(args.checkpointing_steps	)
				else:
						raise ValueError(
						    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
		else:
				__A    =							None

		set_seed(lowerCAmelCase__	)

		__A ,   __A    =							get_dataloaders(lowerCAmelCase__					,     lowerCAmelCase__	)
		__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

		# 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=lowerCAmelCase__	)

		# 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=lowerCAmelCase__	)

		# Instantiate scheduler
		__A    =							get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase__					,     num_warmup_steps=100					,     num_training_steps=(len(lowerCAmelCase__	) * 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(
		    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

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

				if "epoch" in training_difference:
						__A    =							int(training_difference.replace("epoch_"					,     ""	)	) + 1
						__A    =							None
				else:
						__A    =							int(training_difference.replace("step_"					,     ""	)	)
						__A    =							resume_step // len(lowerCAmelCase__	)
						resume_step -= starting_epoch * len(lowerCAmelCase__	)

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

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

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

				# 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":
						__A    =							F"""epoch_{epoch}"""
						if args.output_dir is not None:
								__A    =							os.path.join(args.output_dir					,     lowerCAmelCase__	)
						accelerator.save_state(lowerCAmelCase__	)



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							argparse.ArgumentParser(description="Simple example of training script."	)
		parser.add_argument(
		    "--mixed_precision"					,     type=lowerCAmelCase__					,     default=lowerCAmelCase__					,     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=lowerCAmelCase__					,     default=lowerCAmelCase__					,     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=lowerCAmelCase__					,     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=lowerCAmelCase__					,     default=lowerCAmelCase__					,     help="If the training should continue from a checkpoint folder."					,     )
		__A    =							parser.parse_args()
		__A    =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
		training_function(lowerCAmelCase__					,     lowerCAmelCase__	)


if __name__ == "__main__":
					main()









import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SegformerImageProcessor





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=False							, )				->				Tuple:
				__A    =							size if size is not None else {"height": 30, "width": 30}
				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_reduce_labels






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)

		return image, map



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)
		__A    =							Image.open(dataset[2]["file"]	)
		__A    =							Image.open(dataset[3]["file"]	)

		return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Dict 					= SegformerImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							SegformerImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_reduce_labels"						)						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size							, {"height": 30, "width": 30}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)

				__A    =							self.image_processing_class.from_dict(self.image_processor_dict							, size=42							, reduce_labels=lowercase__						)
				self.assertEqual(image_processor.size							, {"height": 42, "width": 42}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				pass






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, Image.Image						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, numpify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, np.ndarray						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Dict:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				__A    =							[]
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)
						maps.append(torch.zeros(image.shape[-2:]						).long()						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, maps[0]							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test not batched input (PIL images)
				__A ,   __A    =							prepare_semantic_single_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched input (PIL images)
				__A ,   __A    =							prepare_semantic_batch_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        2,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				__A ,   __A    =							prepare_semantic_single_inputs()
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 150						)

				__A    =							True
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

