
"""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 				A__ :

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

			__lowerCAmelCase    :		Union[str, Any]		    =   self.get_config()

			return config, pixel_values, labels

		def 							__lowerCamelCase							(   self				):
			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=_SCREAMING_SNAKE_CASE ,	initializer_range=self.initializer_range ,	out_features=self.out_features ,	out_indices=self.out_indices ,	num_labels=self.num_labels ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Tuple		    =   ConvNextVaModel(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		List[Any]		    =   model(_SCREAMING_SNAKE_CASE				)
			# 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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[Any]		    =   ConvNextVaForImageClassification(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		List[str]		    =   model(_SCREAMING_SNAKE_CASE ,	labels=_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   ConvNextVaBackbone(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Union[str, Any]		    =   model(_SCREAMING_SNAKE_CASE				)

			# 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
			__lowerCAmelCase    :		Optional[int]		    =   None
			__lowerCAmelCase    :		Union[str, Any]		    =   ConvNextVaBackbone(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		List[Any]		    =   model(_SCREAMING_SNAKE_CASE				)

			# 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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   config_and_inputs
			__lowerCAmelCase    :		Optional[int]		    =   {'pixel_values': pixel_values}
			return config, inputs_dict







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   config_and_inputs
			__lowerCAmelCase    :		Dict		    =   {'pixel_values': pixel_values, 'labels': labels}
			return config, inputs_dict




@require_torch
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: str            =			(
		    (
		        ConvNextVaModel,
		        ConvNextVaForImageClassification,
		        ConvNextVaBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		A_		: Optional[int]            =			(
		    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		A_		: Union[str, Any]            =			False
		A_		: int            =			False
		A_		: Dict            =			False
		A_		: int            =			False
		A_		: List[Any]            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   ConvNextVaModelTester(self				)
			__lowerCAmelCase    :		Tuple		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	has_text_modality=_SCREAMING_SNAKE_CASE ,	hidden_size=37				)

		def 							__lowerCamelCase							(   self				):
			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 							__lowerCamelCase							(   self				):
			return

		@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'				)
		def 							__lowerCamelCase							(   self				):
			pass

		@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'				)
		def 							__lowerCamelCase							(   self				):
			pass

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

		def 							__lowerCamelCase							(   self				):
			if not self.model_tester.is_training:
				return

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

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

				__lowerCAmelCase    :		Any		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.train()
				__lowerCAmelCase    :		int		    =   self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	return_labels=_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Union[str, Any]		    =   model(**_SCREAMING_SNAKE_CASE				).loss
				loss.backward()

		def 							__lowerCamelCase							(   self				):
			if not self.model_tester.is_training:
				return

			for model_class in self.all_model_classes:
				__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   self.model_tester.prepare_config_and_inputs_with_labels()
				__lowerCAmelCase    :		Optional[Any]		    =   False
				__lowerCAmelCase    :		Union[str, Any]		    =   True

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

				__lowerCAmelCase    :		List[str]		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.gradient_checkpointing_enable()
				model.train()
				__lowerCAmelCase    :		List[Any]		    =   self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	return_labels=_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[int]		    =   model(**_SCREAMING_SNAKE_CASE				).loss
				loss.backward()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase    :		Optional[int]		    =   ['pixel_values']
				self.assertListEqual(arg_names[:1] ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Union[str, Any]		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()

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

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

				__lowerCAmelCase    :		Any		    =   self.model_tester.num_stages
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	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] ,	)

			__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Dict		    =   True
				check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

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

				check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		Optional[Any]		    =   ConvNextVaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image




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

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







		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Any		    =   self.default_image_processor
			__lowerCAmelCase    :		Union[str, Any]		    =   prepare_img()
			__lowerCAmelCase    :		Tuple		    =   preprocessor(images=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).to(_SCREAMING_SNAKE_CASE				)

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

			# verify the logits
			__lowerCAmelCase    :		List[Any]		    =   torch.Size((1, 10_00)				)
			self.assertEqual(outputs.logits.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   torch.tensor([0.9996, 0.1966, -0.4386]				).to(_SCREAMING_SNAKE_CASE				)
			self.assertTrue(torch.allclose(outputs.logits[0, :3] ,	_SCREAMING_SNAKE_CASE ,	atol=1E-4				)				)

"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   HfArgumentParser(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args_into_dataclasses()[0]
	__lowerCAmelCase    :		int		    =   TensorFlowBenchmark(args=_UpperCamelCase	)
	try:
		__lowerCAmelCase    :		Dict		    =   parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		__lowerCAmelCase    :		List[Any]		    =   'Arg --no_{0} is no longer used, please use --no-{0} instead.'
		__lowerCAmelCase    :		Union[str, Any]		    =   ' '.join(str(_UpperCamelCase	).split(' '	)[:-1]	)
		__lowerCAmelCase    :		List[str]		    =   ''
		__lowerCAmelCase    :		Any		    =   eval(str(_UpperCamelCase	).split(' '	)[-1]	)
		__lowerCAmelCase    :		Dict		    =   []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(_UpperCamelCase	)
		if len(_UpperCamelCase	) > 0:
			__lowerCAmelCase    :		Any		    =   full_error_msg + begin_error_msg + str(_UpperCamelCase	)
		raise ValueError(_UpperCamelCase	)
	benchmark.run()


if __name__ == "__main__":
							main()