

"""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  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





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



					def 	snake_case ( self							:						Dict  )->      List[str]:
								lowerCamelCase__	:	Tuple				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								lowerCamelCase__	:	Optional[int]				=None
								if self.use_labels:
											lowerCamelCase__	:	List[Any]				=ids_tensor([self.batch_size],						self.num_labels  )

								lowerCamelCase__	:	Optional[Any]				=self.get_config()

								return config, pixel_values, labels



					def 	snake_case ( self							:						List[str]  )->      Union[str, 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=snake_case_,						initializer_range=self.initializer_range,						out_features=self.out_features,						out_indices=self.out_indices,						num_labels=self.num_labels,						)



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						str,						lowerCamelCase							:						str  )->      Any:
								lowerCamelCase__	:	Union[str, Any]				=ConvNextVaModel(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								lowerCamelCase__	:	Optional[Any]				=model(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 	snake_case ( self							:						Any,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Any  )->      str:
								lowerCamelCase__	:	Optional[int]				=ConvNextVaForImageClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								lowerCamelCase__	:	str				=model(snake_case_,						labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels)  )



					def 	snake_case ( self							:						str,						lowerCamelCase							:						int,						lowerCamelCase							:						int,						lowerCamelCase							:						Optional[Any]  )->      str:
								lowerCamelCase__	:	Tuple				=ConvNextVaBackbone(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								lowerCamelCase__	:	Optional[Any]				=model(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__	:	Any				=None
								lowerCamelCase__	:	str				=ConvNextVaBackbone(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								lowerCamelCase__	:	str				=model(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 	snake_case ( self							:						int  )->      List[str]:
								lowerCamelCase__	:	Tuple				=self.prepare_config_and_inputs()
								lowerCamelCase__	:	str				=config_and_inputs
								lowerCamelCase__	:	Dict				={"pixel_values": pixel_values}
								return config, inputs_dict




					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								lowerCamelCase__	:	List[str]				=self.prepare_config_and_inputs()
								lowerCamelCase__	:	Optional[int]				=config_and_inputs
								lowerCamelCase__	:	Union[str, Any]				={"pixel_values": pixel_values, "labels": labels}
								return config, inputs_dict





@require_torch
class  __SCREAMING_SNAKE_CASE			(   _snake_case	,     _snake_case	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(
					    (
					        ConvNextVaModel,
					        ConvNextVaForImageClassification,
					        ConvNextVaBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					_a         =							(
					    {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					_a         =							False
					_a         =							False
					_a         =							False
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								lowerCamelCase__	:	Any				=ConvNextVaModelTester(self  )
								lowerCamelCase__	:	List[str]				=ConfigTester(self,						config_class=snake_case_,						has_text_modality=snake_case_,						hidden_size=37  )



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								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 	snake_case ( self							:						int  )->      Any:
								return



					@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds'''  )
					def 	snake_case ( self							:						Optional[int]  )->      List[str]:
								pass



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



					@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking'''  )
					def 	snake_case ( self							:						List[str]  )->      int:
								pass



					def 	snake_case ( self							:						Any  )->      str:
								if not self.model_tester.is_training:
											return

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

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

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



					def 	snake_case ( self							:						str  )->      Optional[Any]:
								if not self.model_tester.is_training:
											return

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	str				=self.model_tester.prepare_config_and_inputs_with_labels()
											lowerCamelCase__	:	List[str]				=False
											lowerCamelCase__	:	Optional[int]				=True

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

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



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								lowerCamelCase__	:	List[str]				=self.model_tester.prepare_config_and_inputs_for_common()

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

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



					def 	snake_case ( self							:						Any  )->      Tuple:
								lowerCamelCase__	:	Optional[Any]				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_  )



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								def check_hidden_states_output(lowerCamelCase							:						Any,						lowerCamelCase							:						int,						lowerCamelCase							:						int  ):
											lowerCamelCase__	:	Optional[Any]				=model_class(snake_case_  )
											model.to(snake_case_  )
											model.eval()

											with torch.no_grad():
														lowerCamelCase__	:	Tuple				=model(**self._prepare_for_class(snake_case_,						snake_case_  )  )

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

											lowerCamelCase__	:	Union[str, Any]				=self.model_tester.num_stages
											self.assertEqual(len(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__	:	Any				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	Optional[int]				=True
											check_hidden_states_output(snake_case_,						snake_case_,						snake_case_  )

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

											check_hidden_states_output(snake_case_,						snake_case_,						snake_case_  )



					def 	snake_case ( self							:						Dict  )->      Tuple:
								lowerCamelCase__	:	Optional[int]				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case_  )




					@slow
					def 	snake_case ( self							:						Tuple  )->      Any:
								for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCamelCase__	:	Dict				=ConvNextVaModel.from_pretrained(snake_case_  )
											self.assertIsNotNone(snake_case_  )






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
			return image

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


					'''simple docstring'''





					@cached_property
					def 	snake_case ( self							:						List[str]  )->      List[str]:
								return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224'''  ) if is_vision_available() else None




					@slow
					def 	snake_case ( self							:						Tuple  )->      Dict:
								lowerCamelCase__	:	List[Any]				=ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224'''  ).to(snake_case_  )

								lowerCamelCase__	:	Any				=self.default_image_processor
								lowerCamelCase__	:	List[str]				=prepare_img()
								lowerCamelCase__	:	Tuple				=preprocessor(images=snake_case_,						return_tensors='''pt'''  ).to(snake_case_  )

								# forward pass
								with torch.no_grad():
											lowerCamelCase__	:	Union[str, Any]				=model(**snake_case_  )

								# verify the logits
								lowerCamelCase__	:	str				=torch.Size((1, 1000)  )
								self.assertEqual(outputs.logits.shape,						snake_case_  )

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









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 10							,	__lowerCamelCase       :					int = 22			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=range(1							,	__lowerCamelCase			)
			lowerCamelCase__	:	str				=range(1							,	__lowerCamelCase			)
			return sum(
			    1 for power in powers for base in bases if len(str(base**power			)			) == power			)


if __name__ == "__main__":
		print(f'{solution(1_0, 2_2) = }')




