



'''simple docstring'''

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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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									=   16
__lowerCAmelCase									=   32



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_ = 16  )			->      Any:
		_a     :      List[Any]        =							AutoTokenizer.from_pretrained('bert-base-cased'  )
		_a     :      Any        =							load_dataset('glue'						,			'mrpc'  )

		def tokenize_function(lowerCAmelCase_  ):
				# max_length=None => use the model max length (it's actually the default)
				_a     :      Tuple        =							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     :      Optional[Any]        =							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     :      Union[str, Any]        =							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     :      Any        =							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     :      Dict        =							16
				elif accelerator.mixed_precision != "no":
						_a     :      Any        =							8
				else:
						_a     :      Optional[Any]        =							None

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

		# Instantiate dataloaders.
		_a     :      Dict        =							DataLoader(
		    tokenized_datasets['train']						,			shuffle=lowerCAmelCase_						,			collate_fn=lowerCAmelCase_						,			batch_size=lowerCAmelCase_  )
		_a     :      Dict        =							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

							__lowerCAmelCase									=   mocked_dataloaders  # noqa: F811



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

		_a     :      int        =							evaluate.load('glue'						,			'mrpc'  )

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

		set_seed(lowerCAmelCase_  )
		_a       ,					_a     :      Union[str, Any]        =							get_dataloaders(lowerCAmelCase_						,			lowerCAmelCase_  )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_a     :      Tuple        =							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     :      Union[str, Any]        =							model.to(accelerator.device  )

		# Instantiate optimizer
		_a     :      int        =							AdamW(params=model.parameters()						,			lr=lowerCAmelCase_  )

		# Instantiate scheduler
		_a     :      Optional[int]        =							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     :      Optional[int]        =							accelerator.prepare(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		# Now we train the model
		for epoch in range(lowerCAmelCase_  ):
				model.train()
				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     :      str        =							model(**lowerCAmelCase_  )
						_a     :      Union[str, Any]        =							outputs.loss
						_a     :      Optional[Any]        =							loss / gradient_accumulation_steps
						accelerator.backward(lowerCAmelCase_  )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				_a     :      int        =							0
				for step, batch in enumerate(lowerCAmelCase_  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device  )
						with torch.no_grad():
								_a     :      List[str]        =							model(**lowerCAmelCase_  )
						_a     :      Optional[int]        =							outputs.logits.argmax(dim=-1  )
						_a       ,					_a     :      Union[str, Any]        =							accelerator.gather((predictions, batch['labels'])  )
						# New Code #
						# First we check if it's a distributed system
						if accelerator.use_distributed:
								# Then see if we're on the last batch of our eval dataloader
								if step == len(lowerCAmelCase_  ) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										_a     :      Optional[Any]        =							predictions[: len(eval_dataloader.dataset  ) - samples_seen]
										_a     :      int        =							references[: len(eval_dataloader.dataset  ) - samples_seen]
								else:
										# Otherwise we add the number of samples seen
										samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
						metric.add_batch(
						    predictions=lowerCAmelCase_						,			references=lowerCAmelCase_						,			)

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



def      __lowerCamelCase     (      )			->      List[str]:
		_a     :      Dict        =							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.'  )
		_a     :      Any        =							parser.parse_args()
		_a     :      Optional[Any]        =							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowerCAmelCase_						,			lowerCAmelCase_  )


if __name__ == "__main__":
							main()









'''simple docstring'''

import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class 	__magic_name__						:







							def __init__(							self					:       List[str]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       List[str]=13	,_UpperCAmelCase					:       Any=32	,_UpperCAmelCase					:       Union[str, Any]=3	,_UpperCAmelCase					:       Optional[int]=4	,_UpperCAmelCase					:       Optional[Any]=[10, 20, 30, 40]	,_UpperCAmelCase					:       Tuple=[2, 2, 3, 2]	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]=37	,_UpperCAmelCase					:       Optional[int]="gelu"	,_UpperCAmelCase					:       Optional[Any]=10	,_UpperCAmelCase					:       Tuple=0.02	,_UpperCAmelCase					:       Any=["stage2", "stage3", "stage4"]	,_UpperCAmelCase					:       Any=[2, 3, 4]	,_UpperCAmelCase					:       Tuple=None	,):
									_a     :      Optional[Any]        =							parent
									_a     :      List[Any]        =							batch_size
									_a     :      str        =							image_size
									_a     :      Union[str, Any]        =							num_channels
									_a     :      List[Any]        =							num_stages
									_a     :      Dict        =							hidden_sizes
									_a     :      int        =							depths
									_a     :      Tuple        =							is_training
									_a     :      List[str]        =							use_labels
									_a     :      Dict        =							intermediate_size
									_a     :      int        =							hidden_act
									_a     :      int        =							num_labels
									_a     :      Any        =							initializer_range
									_a     :      Tuple        =							out_features
									_a     :      int        =							out_indices
									_a     :      List[Any]        =							scope







							def 							__lowercase			(							self					:       Dict		):
									_a     :      List[Any]        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a     :      Union[str, Any]        =							None
									if self.use_labels:
											_a     :      Tuple        =							ids_tensor([self.batch_size]	,self.num_labels		)

									_a     :      str        =							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase			(							self					:       Any		):
									return ConvNextVaConfig(
									    num_channels=self.num_channels	,hidden_sizes=self.hidden_sizes	,depths=self.depths	,num_stages=self.num_stages	,hidden_act=self.hidden_act	,is_decoder=_UpperCAmelCase	,initializer_range=self.initializer_range	,out_features=self.out_features	,out_indices=self.out_indices	,num_labels=self.num_labels	,)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Optional[Any]        =							ConvNextVaModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Any        =							model(_UpperCAmelCase		)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape	,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       int		):
									_a     :      List[Any]        =							ConvNextVaForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      List[str]        =							model(_UpperCAmelCase	,labels=_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Optional[int]        =							ConvNextVaBackbone(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Dict        =							model(_UpperCAmelCase		)

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps		)	,len(config.out_features		)		)
									self.parent.assertListEqual(list(result.feature_maps[0].shape		)	,[self.batch_size, self.hidden_sizes[1], 4, 4]		)

									# verify channels
									self.parent.assertEqual(len(model.channels		)	,len(config.out_features		)		)
									self.parent.assertListEqual(model.channels	,config.hidden_sizes[1:]		)

									# verify backbone works with out_features=None
									_a     :      Tuple        =							None
									_a     :      List[Any]        =							ConvNextVaBackbone(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      List[str]        =							model(_UpperCAmelCase		)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps		)	,1		)
									self.parent.assertListEqual(list(result.feature_maps[0].shape		)	,[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

									# verify channels
									self.parent.assertEqual(len(model.channels		)	,1		)
									self.parent.assertListEqual(model.channels	,[config.hidden_sizes[-1]]		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Any        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      Union[str, Any]        =							config_and_inputs
									_a     :      Any        =							{'pixel_values': pixel_values}
									return config, inputs_dict







							def 							__lowercase			(							self					:       str		):
									_a     :      Tuple        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      Tuple        =							config_and_inputs
									_a     :      List[Any]        =							{'pixel_values': pixel_values, 'labels': labels}
									return config, inputs_dict

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					(
							    (
							        ConvNextVaModel,
							        ConvNextVaForImageClassification,
							        ConvNextVaBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase       :    str   				=					(
							    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    int   				=					False
							lowerCAmelCase       :    str   				=					False
							lowerCAmelCase       :    Optional[Any]   				=					False
							lowerCAmelCase       :    List[str]   				=					False
							lowerCAmelCase       :    Optional[int]   				=					False







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      str        =							ConvNextVaModelTester(self		)
									_a     :      Tuple        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase	,hidden_size=37		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







							def 							__lowercase			(							self					:       str		):
									return







							@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'		)
							def 							__lowercase			(							self					:       List[Any]		):
									pass







							@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'		)
							def 							__lowercase			(							self					:       Optional[int]		):
									pass







							@unittest.skip(reason='ConvNextV2 does not use feedforward chunking'		)
							def 							__lowercase			(							self					:       Any		):
									pass







							def 							__lowercase			(							self					:       List[str]		):
									if not self.model_tester.is_training:
											return

									for model_class in self.all_model_classes:
											_a       ,					_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs_with_labels()
											_a     :      Any        =							True

											if model_class.__name__ in [
											    *get_values(_UpperCAmelCase		),
											    *get_values(_UpperCAmelCase		),
											]:
													continue

											_a     :      Optional[Any]        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a     :      str        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      Optional[int]        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       str		):
									if not self.model_tester.is_training:
											return

									for model_class in self.all_model_classes:
											_a       ,					_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs_with_labels()
											_a     :      Optional[int]        =							False
											_a     :      Tuple        =							True

											if (
											    model_class.__name__
											    in [*get_values(_UpperCAmelCase		), *get_values(_UpperCAmelCase		)]
											    or not model_class.supports_gradient_checkpointing
											):
													continue

											_a     :      Tuple        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a     :      Any        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      List[Any]        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       List[Any]		):
									_a       ,					_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a     :      int        =							['pixel_values']
											self.assertListEqual(arg_names[:1]	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any		):
									def check_hidden_states_output(_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Dict		):
											_a     :      Union[str, Any]        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()

											with torch.no_grad():
													_a     :      List[Any]        =							model(**self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase		)		)

											_a     :      Any        =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											_a     :      str        =							self.model_tester.num_stages
											self.assertEqual(len(_UpperCAmelCase		)	,expected_num_stages + 1		)

											# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]		)	,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,)

									_a       ,					_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      int        =							True
											check_hidden_states_output(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											_a     :      Optional[Any]        =							True

											check_hidden_states_output(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       int		):
									for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a     :      Any        =							ConvNextVaModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)



def      __lowerCamelCase     (      )			->      List[Any]:
		_a     :      List[Any]        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							@cached_property
							def 							__lowercase			(							self					:       Optional[Any]		):
									return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'		) if is_vision_available() else None







							@slow
							def 							__lowercase			(							self					:       Any		):
									_a     :      List[str]        =							ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'		).to(_UpperCAmelCase		)

									_a     :      Optional[int]        =							self.default_image_processor
									_a     :      str        =							prepare_img()
									_a     :      str        =							preprocessor(images=_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a     :      Dict        =							model(**_UpperCAmelCase		)

									# verify the logits
									_a     :      Optional[Any]        =							torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							torch.tensor([0.99_96, 0.19_66, -0.43_86]		).to(_UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]	,_UpperCAmelCase	,atol=1E-4		)		)




