import inspect
import unittest

from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
							from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class 						lowercase    :




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Any=13	,							_UpperCamelCase   :					str=32	,							_UpperCamelCase   :					List[str]=3	,							_UpperCamelCase   :					int=4	,							_UpperCamelCase   :					Dict=[10, 20, 30, 40]	,							_UpperCamelCase   :					int=[2, 2, 3, 2]	,							_UpperCamelCase   :					Optional[Any]=True	,							_UpperCamelCase   :					str=True	,							_UpperCamelCase   :					List[Any]=37	,							_UpperCamelCase   :					List[str]="gelu"	,							_UpperCamelCase   :					List[Any]=10	,							_UpperCamelCase   :					List[str]=0.0_2	,							_UpperCamelCase   :					Tuple=["stage2", "stage3", "stage4"]	,							_UpperCamelCase   :					Union[str, Any]=[2, 3, 4]	,							_UpperCamelCase   :					int=None	,							)					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       num_stages
												SCREAMING_SNAKE_CASE    			=       hidden_sizes
												SCREAMING_SNAKE_CASE    			=       depths
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       num_labels
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       out_features
												SCREAMING_SNAKE_CASE    			=       out_indices
												SCREAMING_SNAKE_CASE    			=       scope




							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

												SCREAMING_SNAKE_CASE    			=       self.get_config()
												return config, pixel_values, labels




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[int]:



												'''simple docstring'''

												return ConvNextConfig(
												    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 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_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 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextForImageClassification(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextBackbone(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_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
												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       ConvNextBackbone(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_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 	__snake_case(						self   :					List[str]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       Union[str, Any]								=							(
							    (
							        ConvNextModel,
							        ConvNextForImageClassification,
							        ConvNextBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__						:       List[Any]								=							(
							    {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__						:       Any								=							True
							lowercase__						:       str								=							False
							lowercase__						:       List[Any]								=							False
							lowercase__						:       List[str]								=							False
							lowercase__						:       List[str]								=							False




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							has_text_modality=_UpperCamelCase	,							hidden_size=37      )




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												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   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												return




							@unittest.skip(reason="ConvNext does not use inputs_embeds"      )
							def 	__snake_case(						self   :					int      )					->				str:



												'''simple docstring'''

												pass




							@unittest.skip(reason="ConvNext does not support input and output embeddings"      )
							def 	__snake_case(						self   :					Optional[int]      )					->				Union[str, Any]:



												'''simple docstring'''

												pass




							@unittest.skip(reason="ConvNext does not use feedforward chunking"      )
							def 	__snake_case(						self   :					List[Any]      )					->				Dict:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
																	self.assertListEqual(arg_names[:1]	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					Any      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Dict      ):
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	model.to(_UpperCamelCase      )
																	model.eval()

																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE    			=       model(**self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase      )      )

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

																	SCREAMING_SNAKE_CASE    			=       self.model_tester.num_stages
																	self.assertEqual(len(_UpperCamelCase      )	,							expected_num_stages + 1      )

																	# ConvNext'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]	,							)

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_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"]
																	SCREAMING_SNAKE_CASE    			=       True

																	check_hidden_states_output(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase      )





							@slow
							def 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

												for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       ConvNextModel.from_pretrained(_UpperCamelCase      )
																	self.assertIsNotNone(_UpperCamelCase      )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
					return image


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




							@cached_property
							def 	__snake_case(						self   :					Dict      )					->				Any:



												'''simple docstring'''

												return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"      ) if is_vision_available() else None





							@slow
							def 	__snake_case(						self   :					int      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"      ).to(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.default_image_processor
												SCREAMING_SNAKE_CASE    			=       prepare_img()
												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      ).to(_UpperCamelCase      )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )

												# verify the logits
												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 1_000)      )
												self.assertEqual(outputs.logits.shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]      ).to(_UpperCamelCase      )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )



@require_torch
class 						lowercase    (     unittest.TestCase				,       a     ):
							lowercase__						:       Any								=							(ConvNextBackbone,) if is_torch_available() else ()
							lowercase__						:       Optional[int]								=							ConvNextConfig

							lowercase__						:       Union[str, Any]								=							False




							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextModelTester(self      )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					assert isinstance(UpperCAmelCase__      , UpperCAmelCase__  ), F"The input value of [n={number}] is not an integer"

					if number == 1:
										return 2
					elif number < 1:
										SCREAMING_SNAKE_CASE    			=       F"The input value of [n={number}] has to be > 0"
										raise ValueError(UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       sylvester(number - 1  )
										SCREAMING_SNAKE_CASE    			=       num - 1
										SCREAMING_SNAKE_CASE    			=       num
										return lower * upper + 1


if __name__ == "__main__":
							print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")




