



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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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
#
########################################################################


A__  :  Any								=					16
A__  :  List[str]								=					32


def 			UpperCamelCase(  __UpperCamelCase							:		Accelerator     ,__UpperCamelCase							:		int = 16 ):
		lowerCAmelCase_			:     Union[str, Any]									= AutoTokenizer.from_pretrained('''bert-base-cased''' )
		lowerCAmelCase_			:     Any									= load_dataset('''glue'''     ,'''mrpc''' )

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

		def collate_fn(__UpperCamelCase							:		Optional[Any] ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowerCAmelCase_			:     Dict									= 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_			:     Union[str, Any]									= 8
				else:
						lowerCAmelCase_			:     List[str]									= None

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

		# Instantiate dataloaders.
		lowerCAmelCase_			:     Any									= DataLoader(
		    tokenized_datasets['''train''']     ,shuffle=__UpperCamelCase     ,collate_fn=__UpperCamelCase     ,batch_size=__UpperCamelCase )
		lowerCAmelCase_			:     Optional[Any]									= DataLoader(
		    tokenized_datasets['''validation''']     ,shuffle=__UpperCamelCase     ,collate_fn=__UpperCamelCase     ,batch_size=__UpperCamelCase )

		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

						A__  :  Tuple								=					mocked_dataloaders  # noqa: F811


def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		List[str] ):
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,__UpperCamelCase ) == "1":
				lowerCAmelCase_			:     str									= 2
		# New Code #
		lowerCAmelCase_			:     int									= int(args.gradient_accumulation_steps )
		lowerCAmelCase_			:     Any									= int(args.local_sgd_steps )
		# Initialize accelerator
		lowerCAmelCase_			:     Dict									= Accelerator(
		    cpu=args.cpu     ,mixed_precision=args.mixed_precision     ,gradient_accumulation_steps=__UpperCamelCase )
		if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
				raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase_			:     Union[str, Any]									= config['''lr''']
		lowerCAmelCase_			:     Any									= int(config['''num_epochs'''] )
		lowerCAmelCase_			:     int									= int(config['''seed'''] )
		lowerCAmelCase_			:     Tuple									= int(config['''batch_size'''] )

		lowerCAmelCase_			:     Optional[int]									= evaluate.load('''glue'''     ,'''mrpc''' )

		set_seed(__UpperCamelCase )
		lowerCAmelCase_					,  lowerCAmelCase_			:     List[str]									= get_dataloaders(__UpperCamelCase     ,__UpperCamelCase )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase_			:     List[str]									= 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).
		lowerCAmelCase_			:     Optional[int]									= model.to(accelerator.device )

		# Instantiate optimizer
		lowerCAmelCase_			:     List[Any]									= AdamW(params=model.parameters()     ,lr=__UpperCamelCase )

		# Instantiate scheduler
		lowerCAmelCase_			:     List[Any]									= get_linear_schedule_with_warmup(
		    optimizer=__UpperCamelCase     ,num_warmup_steps=100     ,num_training_steps=(len(__UpperCamelCase ) * num_epochs)     ,)

		# 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_			:     Any									= accelerator.prepare(
		    __UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase )

		# Now we train the model
		for epoch in range(__UpperCamelCase ):
				model.train()
				with LocalSGD(
				    accelerator=__UpperCamelCase     ,model=__UpperCamelCase     ,local_sgd_steps=__UpperCamelCase     ,enabled=local_sgd_steps is not None ) as local_sgd:
						for step, batch in enumerate(__UpperCamelCase ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device )
								# New code #
								# We use the new `accumulate` context manager to perform gradient accumulation
								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
								with accelerator.accumulate(__UpperCamelCase ):
										lowerCAmelCase_			:     Dict									= model(**__UpperCamelCase )
										lowerCAmelCase_			:     str									= output.loss
										accelerator.backward(__UpperCamelCase )
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# LocalSGD-specific line
										local_sgd.step()

				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():
								lowerCAmelCase_			:     int									= model(**__UpperCamelCase )
						lowerCAmelCase_			:     Tuple									= outputs.logits.argmax(dim=-1 )
						lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= accelerator.gather_for_metrics((predictions, batch['''labels''']) )
						metric.add_batch(
						    predictions=__UpperCamelCase     ,references=__UpperCamelCase     ,)

				lowerCAmelCase_			:     Optional[Any]									= metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f"""epoch {epoch}:"""     ,__UpperCamelCase )


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Union[str, 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.'''     ,)
		# New Code #
		parser.add_argument(
		    '''--gradient_accumulation_steps'''     ,type=__UpperCamelCase     ,default=1     ,help='''The number of minibatches to be ran before gradients are accumulated.'''     ,)
		parser.add_argument(
		    '''--local_sgd_steps'''     ,type=__UpperCamelCase     ,default=8     ,help='''Number of local SGD steps or None to disable local SGD''' )
		parser.add_argument('''--cpu'''     ,action='''store_true'''     ,help='''If passed, will train on the CPU.''' )
		lowerCAmelCase_			:     Tuple									= parser.parse_args()
		lowerCAmelCase_			:     Dict									= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__UpperCamelCase     ,__UpperCamelCase )


if __name__ == "__main__":
						main()










'''simple docstring'''




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=3       ,      lowercase=True       ,      lowercase=True       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=224       ,      lowercase=1000       ,      lowercase=[3, 3, 6, 4]       ,      lowercase=[48, 56, 112, 220]       ,      ):
				A_	:						Dict	=     parent
				A_	:						List[Any]	=     batch_size
				A_	:						Dict	=     num_channels
				A_	:						Optional[Any]	=     is_training
				A_	:						List[str]	=     use_labels
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[int]	=     attention_probs_dropout_prob
				A_	:						Tuple	=     num_labels
				A_	:						List[str]	=     image_size
				A_	:						str	=     layer_depths
				A_	:						Optional[int]	=     embed_dims


	def     _a	(self							):
				A_	:						Union[str, Any]	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				A_	:						int	=     None
				if self.use_labels:
							A_	:						Tuple	=     ids_tensor([self.batch_size]       ,      self.num_labels							)

				A_	:						int	=     self.get_config()

				return config, pixel_values, labels


	def     _a	(self							):
				return SwiftFormerConfig(
				    depths=self.layer_depths       ,      embed_dims=self.embed_dims       ,      mlp_ratio=4       ,      downsamples=[True, True, True, True]       ,      hidden_act="""gelu"""       ,      num_labels=self.num_labels       ,      down_patch_size=3       ,      down_stride=2       ,      down_pad=1       ,      drop_rate=0.0       ,      drop_path_rate=0.0       ,      use_layer_scale=lowercase       ,      layer_scale_init_value=1E-5       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     SwiftFormerModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Union[str, Any]	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.embed_dims[-1], 7, 7)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Any	=     self.num_labels
				A_	:						Any	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Optional[int]	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)

				A_	:						int	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_	:						int	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
				A_	:						Dict	=     model(lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)


	def     _a	(self							):
				((A_),    (A_),    (A_))	:						int	=     self.prepare_config_and_inputs()
				A_	:						Any	=     {"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__SCREAMING_SNAKE_CASE   :		int					=					False
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		List[str]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False


	def     _a	(self							):
				A_	:						Optional[int]	=     SwiftFormerModelTester(self							)
				A_	:						Any	=     ConfigTester(
				    self       ,      config_class=lowercase       ,      has_text_modality=lowercase       ,      hidden_size=37       ,      num_attention_heads=12       ,      num_hidden_layers=12       ,      )


	def     _a	(self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						Optional[Any]	=     model_class(lowercase							)
							A_	:						Optional[int]	=     model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase       ,      nn.Linear							)							)


	def     _a	(self							):
				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     model_class(lowercase							)
							A_	:						Optional[int]	=     inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_	:						int	=     [*signature.parameters.keys()]

							A_	:						Tuple	=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]       ,      lowercase							)


	def     _a	(self							):
				A_	:						str	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def     _a	(self							):
				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						Optional[Any]	=     SwiftFormerModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)


	@unittest.skip(reason="""SwiftFormer does not output attentions"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				def check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							):
							A_	:						str	=     model_class(lowercase							)
							model.to(lowercase							)
							model.eval()

							with torch.no_grad():
										A_	:						Optional[int]	=     model(**self._prepare_for_class(lowercase       ,      lowercase							)							)

							A_	:						Any	=     outputs.hidden_states

							A_	:						Any	=     8
							self.assertEqual(len(lowercase							)       ,      lowercase							)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(lowercase							)							):
										self.assertEqual(
										    hidden_states[i].shape       ,      torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ]							)       ,      )

				A_,    A_	:						str	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     True
							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A_	:						str	=     True

							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)


	def     _a	(self							):
				def _config_zero_init(lowercase							):
							A_	:						Optional[Any]	=     copy.deepcopy(lowercase							)
							for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowercase       ,      lowercase       ,      1E-10							)
										if isinstance(getattr(lowercase       ,      lowercase       ,      lowercase							)       ,      lowercase							):
													A_	:						Any	=     _config_zero_init(getattr(lowercase       ,      lowercase							)							)
													setattr(lowercase       ,      lowercase       ,      lowercase							)
							return configs_no_init

				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

				A_	:						Any	=     _config_zero_init(lowercase							)
				for model_class in self.all_model_classes:
							A_	:						List[str]	=     model_class(config=lowercase							)
							for name, param in model.named_parameters():
										if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1E9) / 1E9).round().item()       ,      [0.0, 1.0]       ,      msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,      )


	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
	def     _a	(self							):
				pass






def 	a			( ):
			'''simple docstring'''





			A_	:						List[str]	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image







@require_torch
@require_vision
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@cached_property
	def     _a	(self							):
				return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None


	@slow
	def     _a	(self							):
				A_	:						Any	=     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(lowercase							)

				A_	:						Dict	=     self.default_image_processor
				A_	:						Dict	=     prepare_img()
				A_	:						int	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_	:						int	=     model(**lowercase							)

				# verify the logits
				A_	:						int	=     torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape       ,      lowercase							)

				A_	:						List[str]	=     torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]							).to(lowercase							)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,      lowercase       ,      atol=1E-4							)							)