




'''simple docstring'''




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


a     :    List[str]      						=					1_6
a     :    List[Any]      						=					3_2



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase = 1_6	)   ->    Any:
							UpperCAmelCase  :	Dict							  =	AutoTokenizer.from_pretrained("""bert-base-cased"""	)
							UpperCAmelCase  :	Optional[Any]							  =	DatasetDict(
							    {
							        """train""": dataset["""train"""].select(_lowercase	),
							        """validation""": dataset["""train"""].select(_lowercase	),
							        """test""": dataset["""validation"""],
							    }	)

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

							def collate_fn(_lowercase	):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														UpperCAmelCase  :	Any							  =	1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					UpperCAmelCase  :	List[str]							  =	1_6
														elif accelerator.mixed_precision != "no":
																					UpperCAmelCase  :	Any							  =	8
														else:
																					UpperCAmelCase  :	Tuple							  =	None

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

							# Instantiate dataloaders.
							UpperCAmelCase  :	int							  =	DataLoader(
							    tokenized_datasets["""train"""]			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase	)
							UpperCAmelCase  :	Tuple							  =	DataLoader(
							    tokenized_datasets["""validation"""]			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase	)

							UpperCAmelCase  :	Tuple							  =	DataLoader(
							    tokenized_datasets["""test"""]			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase	)

							return train_dataloader, eval_dataloader, test_dataloader



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[Any]:
							# New Code #
							UpperCAmelCase  :	List[Any]							  =	[]
							# Download the dataset
							UpperCAmelCase  :	List[Any]							  =	load_dataset("""glue"""			,       """mrpc"""	)
							# Create our splits
							UpperCAmelCase  :	Dict							  =	StratifiedKFold(n_splits=int(args.num_folds	)	)
							# Initialize accelerator
							UpperCAmelCase  :	int							  =	Accelerator(cpu=args.cpu			,       mixed_precision=args.mixed_precision	)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							UpperCAmelCase  :	List[str]							  =	config["""lr"""]
							UpperCAmelCase  :	str							  =	int(config["""num_epochs"""]	)
							UpperCAmelCase  :	str							  =	int(config["""seed"""]	)
							UpperCAmelCase  :	Tuple							  =	int(config["""batch_size"""]	)

							UpperCAmelCase  :	Union[str, Any]							  =	evaluate.load("""glue"""			,       """mrpc"""	)

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

							set_seed(_lowercase	)

							# New Code #
							# Create our folds:
							UpperCAmelCase  :	Optional[int]							  =	kfold.split(np.zeros(datasets["""train"""].num_rows	)			,       datasets["""train"""]["""label"""]	)
							UpperCAmelCase  :	Tuple							  =	[]
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(_lowercase	):
														UpperCAmelCase  :	Tuple							  =	get_fold_dataloaders(
														    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														UpperCAmelCase  :	Optional[Any]							  =	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).
														UpperCAmelCase  :	Any							  =	model.to(accelerator.device	)

														# Instantiate optimizer
														UpperCAmelCase  :	Union[str, Any]							  =	AdamW(params=model.parameters()			,       lr=_lowercase	)

														# Instantiate scheduler
														UpperCAmelCase  :	Any							  =	get_linear_schedule_with_warmup(
														    optimizer=_lowercase			,       num_warmup_steps=1_0_0			,       num_training_steps=(len(_lowercase	) * 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.
														UpperCAmelCase  :	Any							  =	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	)
																												UpperCAmelCase  :	Optional[Any]							  =	model(**_lowercase	)
																												UpperCAmelCase  :	Any							  =	outputs.loss
																												UpperCAmelCase  :	Any							  =	loss / gradient_accumulation_steps
																												accelerator.backward(_lowercase	)
																												if step % gradient_accumulation_steps == 0:
																																			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():
																																			UpperCAmelCase  :	Optional[Any]							  =	model(**_lowercase	)
																												UpperCAmelCase  :	Any							  =	outputs.logits.argmax(dim=-1	)
																												UpperCAmelCase  :	Optional[Any]							  =	accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
																												metric.add_batch(
																												    predictions=_lowercase			,       references=_lowercase			,       )

																					UpperCAmelCase  :	Any							  =	metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F'''epoch {epoch}:'''			,       _lowercase	)

														# New Code #
														# We also run predictions on the test set at the very end
														UpperCAmelCase  :	Optional[Any]							  =	[]
														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():
																												UpperCAmelCase  :	Optional[Any]							  =	model(**_lowercase	)
																					UpperCAmelCase  :	List[str]							  =	outputs.logits
																					UpperCAmelCase  :	Dict							  =	accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
																					fold_predictions.append(predictions.cpu()	)
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu()	)
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(_lowercase			,       dim=0	)	)
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							UpperCAmelCase  :	Dict							  =	torch.cat(_lowercase			,       dim=0	)
							UpperCAmelCase  :	int							  =	torch.stack(_lowercase			,       dim=0	).sum(dim=0	).div(int(args.num_folds	)	).argmax(dim=-1	)
							UpperCAmelCase  :	List[Any]							  =	metric.compute(predictions=_lowercase			,       references=_lowercase	)
							accelerator.print("""Average test metrics from all folds:"""			,       _lowercase	)



def    __lowerCamelCase						(      )   ->    Optional[Any]:
							UpperCAmelCase  :	List[Any]							  =	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."""	)
							# New Code #
							parser.add_argument("""--num_folds"""			,       type=_lowercase			,       default=3			,       help="""The number of splits to perform across the dataset"""	)
							UpperCAmelCase  :	Optional[int]							  =	parser.parse_args()
							UpperCAmelCase  :	Optional[int]							  =	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
							training_function(_lowercase			,       _lowercase	)


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     UpperCamelCase_				:





	def __init__(						self	,	A	,	A=13	,	A=32	,	A=3	,	A=4	,	A=[10, 20, 30, 40]	,	A=[2, 2, 3, 2]	,	A=True	,	A=True	,	A=37	,	A="gelu"	,	A=10	,	A=0.0_2	,	A=["stage2", "stage3", "stage4"]	,	A=[2, 3, 4]	,	A=None	,	)    ->    int:
								UpperCAmelCase  :	str							  =	parent
								UpperCAmelCase  :	List[Any]							  =	batch_size
								UpperCAmelCase  :	Dict							  =	image_size
								UpperCAmelCase  :	Tuple							  =	num_channels
								UpperCAmelCase  :	Union[str, Any]							  =	num_stages
								UpperCAmelCase  :	Any							  =	hidden_sizes
								UpperCAmelCase  :	str							  =	depths
								UpperCAmelCase  :	Optional[int]							  =	is_training
								UpperCAmelCase  :	Union[str, Any]							  =	use_labels
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	str							  =	hidden_act
								UpperCAmelCase  :	List[str]							  =	num_labels
								UpperCAmelCase  :	Tuple							  =	initializer_range
								UpperCAmelCase  :	Optional[Any]							  =	out_features
								UpperCAmelCase  :	List[str]							  =	out_indices
								UpperCAmelCase  :	Any							  =	scope





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Tuple							  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								UpperCAmelCase  :	List[Any]							  =	None
								if self.use_labels:
															UpperCAmelCase  :	Dict							  =	ids_tensor([self.batch_size]	,	self.num_labels					)

								UpperCAmelCase  :	List[str]							  =	self.get_config()

								return config, pixel_values, labels





	def 				_lowercase(						self					)    ->    Optional[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=A	,	initializer_range=self.initializer_range	,	out_features=self.out_features	,	out_indices=self.out_indices	,	num_labels=self.num_labels	,	)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	int							  =	ConvNextVaModel(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	List[Any]							  =	model(A					)
								# 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	,	A	,	A	,	A					)    ->    Any:
								UpperCAmelCase  :	List[str]							  =	ConvNextVaForImageClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	int							  =	model(A	,	labels=A					)
								self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels)					)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Any:
								UpperCAmelCase  :	Optional[Any]							  =	ConvNextVaBackbone(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Any							  =	model(A					)

								# 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
								UpperCAmelCase  :	Any							  =	None
								UpperCAmelCase  :	Optional[int]							  =	ConvNextVaBackbone(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	int							  =	model(A					)

								# 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					)    ->    List[str]:
								UpperCAmelCase  :	Dict							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	config_and_inputs
								UpperCAmelCase  :	str							  =	{"""pixel_values""": pixel_values}
								return config, inputs_dict






	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[str]							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	config_and_inputs
								UpperCAmelCase  :	List[str]							  =	{"""pixel_values""": pixel_values, """labels""": labels}
								return config, inputs_dict




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

	lowercase    =				False
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False





	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Dict							  =	ConvNextVaModelTester(self					)
								UpperCAmelCase  :	List[str]							  =	ConfigTester(self	,	config_class=A	,	has_text_modality=A	,	hidden_size=37					)





	def 				_lowercase(						self					)    ->    int:
								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					)    ->    List[str]:
								return





	@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""					)
	def 				_lowercase(						self					)    ->    Dict:
								pass





	@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""					)
	def 				_lowercase(						self					)    ->    Any:
								pass





	@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""					)
	def 				_lowercase(						self					)    ->    int:
								pass





	def 				_lowercase(						self					)    ->    Dict:
								if not self.model_tester.is_training:
															return

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

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

															UpperCAmelCase  :	Any							  =	model_class(A					)
															model.to(A					)
															model.train()
															UpperCAmelCase  :	List[str]							  =	self._prepare_for_class(A	,	A	,	return_labels=A					)
															UpperCAmelCase  :	List[str]							  =	model(**A					).loss
															loss.backward()





	def 				_lowercase(						self					)    ->    Tuple:
								if not self.model_tester.is_training:
															return

								for model_class in self.all_model_classes:
															UpperCAmelCase  ,  UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs_with_labels()
															UpperCAmelCase  :	List[str]							  =	False
															UpperCAmelCase  :	int							  =	True

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

															UpperCAmelCase  :	Dict							  =	model_class(A					)
															model.to(A					)
															model.gradient_checkpointing_enable()
															model.train()
															UpperCAmelCase  :	Any							  =	self._prepare_for_class(A	,	A	,	return_labels=A					)
															UpperCAmelCase  :	Any							  =	model(**A					).loss
															loss.backward()





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	self.model_tester.prepare_config_and_inputs_for_common()

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

															UpperCAmelCase  :	Optional[Any]							  =	["""pixel_values"""]
															self.assertListEqual(arg_names[:1]	,	A					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Tuple							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    List[str]:
								def check_hidden_states_output(A	,	A	,	A					):
															UpperCAmelCase  :	Optional[Any]							  =	model_class(A					)
															model.to(A					)
															model.eval()

															with torch.no_grad():
																						UpperCAmelCase  :	Dict							  =	model(**self._prepare_for_class(A	,	A					)					)

															UpperCAmelCase  :	Tuple							  =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															UpperCAmelCase  :	Optional[Any]							  =	self.model_tester.num_stages
															self.assertEqual(len(A					)	,	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]	,	)

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

								for model_class in self.all_model_classes:
															UpperCAmelCase  :	str							  =	True
															check_hidden_states_output(A	,	A	,	A					)

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

															check_hidden_states_output(A	,	A	,	A					)





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Dict							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A					)






	@slow
	def 				_lowercase(						self					)    ->    Any:
								for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase  :	Tuple							  =	ConvNextVaModel.from_pretrained(A					)
															self.assertIsNotNone(A					)




def    __lowerCamelCase						(      )   ->    Optional[int]:
							UpperCAmelCase  :	Optional[int]							  =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
							return image


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





	@cached_property
	def 				_lowercase(						self					)    ->    str:
								return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""					) if is_vision_available() else None






	@slow
	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Any							  =	ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""					).to(A					)

								UpperCAmelCase  :	List[Any]							  =	self.default_image_processor
								UpperCAmelCase  :	Any							  =	prepare_img()
								UpperCAmelCase  :	Tuple							  =	preprocessor(images=A	,	return_tensors="""pt"""					).to(A					)

								# forward pass
								with torch.no_grad():
															UpperCAmelCase  :	Optional[Any]							  =	model(**A					)

								# verify the logits
								UpperCAmelCase  :	Dict							  =	torch.Size((1, 1000)					)
								self.assertEqual(outputs.logits.shape	,	A					)

								UpperCAmelCase  :	Dict							  =	torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]					).to(A					)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]	,	A	,	atol=1e-4					)					)

