






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

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

											snake_case__													=   self.get_config()

											return config, pixel_values, labels
							def lowerCAmelCase_       (   self:					List[str]  )						->						int:
											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 lowerCAmelCase_       (   self:					List[Any]				,		UpperCamelCase:					str				,		UpperCamelCase:					Optional[int]				,		UpperCamelCase:					Optional[int]  )						->						Optional[int]:
											snake_case__													=   ConvNextVaModel(config=UpperCamelCase_  )
											model.to(UpperCamelCase_  )
											model.eval()
											snake_case__													=   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 lowerCAmelCase_       (   self:					str				,		UpperCamelCase:					Tuple				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					List[str]  )						->						List[Any]:
											snake_case__													=   ConvNextVaForImageClassification(UpperCamelCase_  )
											model.to(UpperCamelCase_  )
											model.eval()
											snake_case__													=   model(UpperCamelCase_				,		labels=UpperCamelCase_  )
											self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)  )
							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					Any				,		UpperCamelCase:					Union[str, Any]  )						->						Union[str, Any]:
											snake_case__													=   ConvNextVaBackbone(config=UpperCamelCase_  )
											model.to(UpperCamelCase_  )
											model.eval()
											snake_case__													=   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
											snake_case__													=   None
											snake_case__													=   ConvNextVaBackbone(config=UpperCamelCase_  )
											model.to(UpperCamelCase_  )
											model.eval()
											snake_case__													=   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 lowerCAmelCase_       (   self:					int  )						->						Optional[Any]:
											snake_case__													=   self.prepare_config_and_inputs()
											snake_case__							,  snake_case__							,  snake_case__													=   config_and_inputs
											snake_case__													=   {'pixel_values': pixel_values}
											return config, inputs_dict


							def lowerCAmelCase_       (   self:					List[Any]  )						->						List[str]:
											snake_case__													=   self.prepare_config_and_inputs()
											snake_case__							,  snake_case__							,  snake_case__													=   config_and_inputs
											snake_case__													=   {'pixel_values': pixel_values, 'labels': labels}
											return config, inputs_dict


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

							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							def lowerCAmelCase_       (   self:					Any  )						->						int:
											snake_case__													=   ConvNextVaModelTester(self  )
											snake_case__													=   ConfigTester(self				,		config_class=UpperCamelCase_				,		has_text_modality=UpperCamelCase_				,		hidden_size=37  )
							def lowerCAmelCase_       (   self:					Any  )						->						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 lowerCAmelCase_       (   self:					Optional[Any]  )						->						Union[str, Any]:
											return
							@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'  )
							def lowerCAmelCase_       (   self:					Any  )						->						Union[str, Any]:
											pass
							@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'  )
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Union[str, Any]:
											pass
							@unittest.skip(reason='ConvNextV2 does not use feedforward chunking'  )
							def lowerCAmelCase_       (   self:					Optional[int]  )						->						int:
											pass
							def lowerCAmelCase_       (   self:					str  )						->						Tuple:







											if not self.model_tester.is_training:
															return

											for model_class in self.all_model_classes:
															snake_case__							,  snake_case__													=   self.model_tester.prepare_config_and_inputs_with_labels()
															snake_case__													=   True

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

															snake_case__													=   model_class(UpperCamelCase_  )
															model.to(UpperCamelCase_  )
															model.train()
															snake_case__													=   self._prepare_for_class(UpperCamelCase_				,		UpperCamelCase_				,		return_labels=UpperCamelCase_  )
															snake_case__													=   model(**UpperCamelCase_  ).loss
															loss.backward()
							def lowerCAmelCase_       (   self:					Union[str, Any]  )						->						Union[str, Any]:







											if not self.model_tester.is_training:
															return

											for model_class in self.all_model_classes:
															snake_case__							,  snake_case__													=   self.model_tester.prepare_config_and_inputs_with_labels()
															snake_case__													=   False
															snake_case__													=   True

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

															snake_case__													=   model_class(UpperCamelCase_  )
															model.to(UpperCamelCase_  )
															model.gradient_checkpointing_enable()
															model.train()
															snake_case__													=   self._prepare_for_class(UpperCamelCase_				,		UpperCamelCase_				,		return_labels=UpperCamelCase_  )
															snake_case__													=   model(**UpperCamelCase_  ).loss
															loss.backward()
							def lowerCAmelCase_       (   self:					Union[str, Any]  )						->						Any:
											snake_case__							,  snake_case__													=   self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case__													=   ['pixel_values']
															self.assertListEqual(arg_names[:1]				,		UpperCamelCase_  )
							def lowerCAmelCase_       (   self:					str  )						->						List[str]:
											snake_case__													=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase_  )
							def lowerCAmelCase_       (   self:					str  )						->						str:







											def check_hidden_states_output(UpperCamelCase:					Optional[int]				,		UpperCamelCase:					Dict				,		UpperCamelCase:					Union[str, Any]  ):
															snake_case__													=   model_class(UpperCamelCase_  )
															model.to(UpperCamelCase_  )
															model.eval()

															with torch.no_grad():
																			snake_case__													=   model(**self._prepare_for_class(UpperCamelCase_				,		UpperCamelCase_  )  )

															snake_case__													=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															snake_case__													=   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]				,		)

											snake_case__							,  snake_case__													=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__													=   True
															check_hidden_states_output(UpperCamelCase_				,		UpperCamelCase_				,		UpperCamelCase_  )

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

															check_hidden_states_output(UpperCamelCase_				,		UpperCamelCase_				,		UpperCamelCase_  )
							def lowerCAmelCase_       (   self:					Optional[int]  )						->						int:
											snake_case__													=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_  )


							@slow
							def lowerCAmelCase_       (   self:					List[Any]  )						->						int:







											for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__													=   ConvNextVaModel.from_pretrained(UpperCamelCase_  )
															self.assertIsNotNone(UpperCamelCase_  )


def    a_       (					)	->	Optional[int]:







				"""simple docstring"""
				snake_case__													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				return image


@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							@cached_property
							def lowerCAmelCase_       (   self:					Dict  )						->						Dict:
											return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'  ) if is_vision_available() else None


							@slow
							def lowerCAmelCase_       (   self:					Union[str, Any]  )						->						Tuple:
											snake_case__													=   ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'  ).to(UpperCamelCase_  )

											snake_case__													=   self.default_image_processor
											snake_case__													=   prepare_img()
											snake_case__													=   preprocessor(images=UpperCamelCase_				,		return_tensors='pt'  ).to(UpperCamelCase_  )

											# forward pass
											with torch.no_grad():
															snake_case__													=   model(**UpperCamelCase_  )

											# verify the logits
											snake_case__													=   torch.Size((1, 10_00)  )
											self.assertEqual(outputs.logits.shape				,		UpperCamelCase_  )

											snake_case__													=   torch.tensor([0.9_996, 0.1_966, -0.4_386]  ).to(UpperCamelCase_  )
											self.assertTrue(torch.allclose(outputs.logits[0, :3]				,		UpperCamelCase_				,		atol=1e-4  )  )








from ..utils import DummyObject, requires_backends


class        _a							(			metaclass=UpperCamelCase__			):
							_lowercase	:			Any        = ['''torch''', '''scipy''']

							def __init__(							self:	int    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									requires_backends(self    ,						['''torch''', '''scipy''']							)

							@classmethod
							def       lowerCamelCase_ (							cls:	Optional[int]    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	List[Any]							)			->   int:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


							@classmethod
							def       lowerCamelCase_ (							cls:	Any    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Dict							)			->   Optional[int]:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


