



'''simple docstring'''


import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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_      :				List[str]   					=    16
lowercase_      :				Union[str, Any]   					=    32







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Accelerator ,  lowercase_    :      int = 16						):
						lowercase        =							AutoTokenizer.from_pretrained("""bert-base-cased"""						)
						lowercase        =							load_dataset("""glue""" ,  """mrpc"""						)

						def tokenize_function(lowercase_    :      Any						):
												# max_length=None => use the model max length (it's actually the default)
												lowercase        =							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():
												lowercase        =							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
						lowercase        =							tokenized_datasets.rename_column("""label""" ,  """labels"""						)

						def collate_fn(lowercase_    :      Tuple						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase        =							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":
																		lowercase        =							16
												elif accelerator.mixed_precision != "no":
																		lowercase        =							8
												else:
																		lowercase        =							None

												return tokenizer.pad(
												    lowercase_ ,  padding="""longest""" ,  max_length=lowercase_ ,  pad_to_multiple_of=lowercase_ ,  return_tensors="""pt""" ,  )

						# Instantiate dataloaders.
						lowercase        =							DataLoader(
						    tokenized_datasets["""train"""] ,  shuffle=lowercase_ ,  collate_fn=lowercase_ ,  batch_size=lowercase_						)
						lowercase        =							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_      :				Any   					=    mocked_dataloaders  # noqa: F811







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      Optional[Any]						):
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,  lowercase_						) == "1":
												lowercase        =							2
						# Initialize accelerator
						lowercase        =							Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase        =							config["""lr"""]
						lowercase        =							int(config["""num_epochs"""]						)
						lowercase        =							int(config["""seed"""]						)
						lowercase        =							int(config["""batch_size"""]						)

						lowercase        =							evaluate.load("""glue""" ,  """mrpc"""						)

						# New Code #
						# We now can define an inner training loop function. It should take a batch size as the only parameter,
						# and build the dataloaders in there.
						# It also gets our decorator
						@find_executable_batch_size(starting_batch_size=lowercase_						)
						def inner_training_loop(lowercase_    :      Tuple						):
												# And now just move everything below under this function
												# We need to bring in the Accelerator object from earlier
												nonlocal accelerator
												# And reset all of its attributes that could hold onto any memory:
												accelerator.free_memory()

												# Then we can declare the model, optimizer, and everything else:
												set_seed(lowercase_						)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowercase        =							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).
												lowercase        =							model.to(accelerator.device						)

												# Instantiate optimizer
												lowercase        =							AdamW(params=model.parameters() ,  lr=lowercase_						)
												lowercase     ,   lowercase        =							get_dataloaders(lowercase_ ,  lowercase_						)

												# Instantiate scheduler
												lowercase        =							get_linear_schedule_with_warmup(
												    optimizer=lowercase_ ,  num_warmup_steps=100 ,  num_training_steps=(len(lowercase_						) * 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.
												lowercase     ,   lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							accelerator.prepare(
												    lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_						)

												# Now we train the model
												for epoch in range(lowercase_						):
																		model.train()
																		for step, batch in enumerate(lowercase_						):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device						)
																								lowercase        =							model(**lowercase_						)
																								lowercase        =							outputs.loss
																								accelerator.backward(lowercase_						)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							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."""						)
						lowercase        =							parser.parse_args()
						lowercase        =							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(lowercase_ ,  lowercase_						)


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      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=13 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=1000 ,				_lowerCAmelCase=[3, 3, 6, 4] ,				_lowerCAmelCase=[48, 56, 112, 220] ,				)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							is_training
							lowercase        =							use_labels
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							num_labels
							lowercase        =							image_size
							lowercase        =							layer_depths
							lowercase        =							embed_dims







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							lowercase        =							None
							if self.use_labels:
													lowercase        =							ids_tensor([self.batch_size] ,				self.num_labels						)

							lowercase        =							self.get_config()

							return config, pixel_values, labels







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							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=_lowerCAmelCase ,				layer_scale_init_value=1E-5 ,				)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.embed_dims[-1], 7, 7)						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.num_labels
							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase ,				labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)						)

							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()

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







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							((lowercase)     ,   (lowercase)     ,   (lowercase))        =							self.prepare_config_and_inputs()
							lowercase        =							{"""pixel_values""": pixel_values}
							return config, inputs_dict




@require_torch
class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__A			  =					(
	    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							SwiftFormerModelTester(self						)
							lowercase        =							ConfigTester(
							    self ,				config_class=_lowerCAmelCase ,				has_text_modality=_lowerCAmelCase ,				hidden_size=37 ,				num_attention_heads=12 ,				num_hidden_layers=12 ,				)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""						)
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							model_class(_lowerCAmelCase						)
													lowercase        =							model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_lowerCAmelCase ,				nn.Linear						)						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

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

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







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)







	@slow
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase        =							SwiftFormerModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)







	@unittest.skip(reason="""SwiftFormer does not output attentions"""						)
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							def check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						):
													lowercase        =							model_class(_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.eval()

													with torch.no_grad():
																			lowercase        =							model(**self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase						)						)

													lowercase        =							outputs.hidden_states

													lowercase        =							8
													self.assertEqual(len(_lowerCAmelCase						) ,				_lowerCAmelCase						)  # 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(_lowerCAmelCase						)						):
																			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),
																			        ]						) ,				)

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

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

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase        =							True

													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							def _config_zero_init(_lowerCAmelCase						):
													lowercase        =							copy.deepcopy(_lowerCAmelCase						)
													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(_lowerCAmelCase ,				_lowerCAmelCase ,				1E-10						)
																			if isinstance(getattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						) ,				_lowerCAmelCase						):
																									lowercase        =							_config_zero_init(getattr(_lowerCAmelCase ,				_lowerCAmelCase						)						)
																									setattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)
													return configs_no_init

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							lowercase        =							_config_zero_init(_lowerCAmelCase						)
							for model_class in self.all_model_classes:
													lowercase        =							model_class(config=_lowerCAmelCase						)
													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						)    ->    Any:
							'''simple docstring'''






							pass







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




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







	@cached_property
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""						) if is_vision_available() else None







	@slow
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""						).to(_lowerCAmelCase						)

							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(images=_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)

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

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

							lowercase        =							torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)


