
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					:      List[Any]    ,     a					:      int    ,     a					:      Tuple=13    ,     a					:      int=32    ,     a					:      List[Any]=3    ,     a					:      Dict=4    ,     a					:      List[str]=[10, 20, 30, 40]    ,     a					:      Any=[2, 2, 3, 2]    ,     a					:      List[str]=True    ,     a					:      Any=True    ,     a					:      Optional[Any]=37    ,     a					:      Dict="gelu"    ,     a					:      Tuple=10    ,     a					:      Dict=0.0_2    ,     a					:      Optional[Any]=["stage2", "stage3", "stage4"]    ,     a					:      Optional[int]=[2, 3, 4]    ,     a					:      int=None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		parent
							lowerCAmelCase__      :      str         =		batch_size
							lowerCAmelCase__      :      List[str]         =		image_size
							lowerCAmelCase__      :      int         =		num_channels
							lowerCAmelCase__      :      Optional[int]         =		num_stages
							lowerCAmelCase__      :      str         =		hidden_sizes
							lowerCAmelCase__      :      Dict         =		depths
							lowerCAmelCase__      :      List[str]         =		is_training
							lowerCAmelCase__      :      Optional[Any]         =		use_labels
							lowerCAmelCase__      :      Optional[Any]         =		intermediate_size
							lowerCAmelCase__      :      Tuple         =		hidden_act
							lowerCAmelCase__      :      List[Any]         =		num_labels
							lowerCAmelCase__      :      Dict         =		initializer_range
							lowerCAmelCase__      :      List[str]         =		out_features
							lowerCAmelCase__      :      str         =		out_indices
							lowerCAmelCase__      :      Optional[Any]         =		scope






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

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

							return config, pixel_values, labels






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							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 							_lowerCamelCase		(						self					:      int    ,     a					:      Tuple    ,     a					:      Dict    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		ConvNextVaModel(config=__a   )
							model.to(__a   )
							model.eval()
							lowerCAmelCase__      :      Union[str, 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 							_lowerCamelCase		(						self					:      Any    ,     a					:      Tuple    ,     a					:      Any    ,     a					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		ConvNextVaForImageClassification(__a   )
							model.to(__a   )
							model.eval()
							lowerCAmelCase__      :      Union[str, Any]         =		model(__a    ,     labels=__a   )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)   )






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      str    ,     a					:      Any    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		ConvNextVaBackbone(config=__a   )
							model.to(__a   )
							model.eval()
							lowerCAmelCase__      :      Dict         =		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
							lowerCAmelCase__      :      List[str]         =		None
							lowerCAmelCase__      :      str         =		ConvNextVaBackbone(config=__a   )
							model.to(__a   )
							model.eval()
							lowerCAmelCase__      :      Optional[Any]         =		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 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		self.prepare_config_and_inputs()
							lowerCAmelCase__      :      Optional[int]         =		config_and_inputs
							lowerCAmelCase__      :      List[Any]         =		{"""pixel_values""": pixel_values}
							return config, inputs_dict






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.prepare_config_and_inputs()
							lowerCAmelCase__      :      Optional[Any]         =		config_and_inputs
							lowerCAmelCase__      :      int         =		{"""pixel_values""": pixel_values, """labels""": labels}
							return config, inputs_dict







@require_torch
class      A__							(     __a						,   __a						,   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 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		ConvNextVaModelTester(self   )
							lowerCAmelCase__      :      Optional[int]         =		ConfigTester(self    ,     config_class=__a    ,     has_text_modality=__a    ,     hidden_size=37   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''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 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							return






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






							'''simple docstring'''







							pass






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






							'''simple docstring'''







							pass






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






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							if not self.model_tester.is_training:
										return

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

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

										lowerCAmelCase__      :      Union[str, Any]         =		model_class(__a   )
										model.to(__a   )
										model.train()
										lowerCAmelCase__      :      Tuple         =		self._prepare_for_class(__a    ,     __a    ,     return_labels=__a   )
										lowerCAmelCase__      :      Dict         =		model(**__a   ).loss
										loss.backward()






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							if not self.model_tester.is_training:
										return

							for model_class in self.all_model_classes:
										lowerCAmelCase__      :      Tuple         =		self.model_tester.prepare_config_and_inputs_with_labels()
										lowerCAmelCase__      :      Dict         =		False
										lowerCAmelCase__      :      Optional[Any]         =		True

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

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






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.model_tester.prepare_config_and_inputs_for_common()

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

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






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							def check_hidden_states_output(a					:      Any    ,     a					:      Tuple    ,     a					:      Tuple   ):
										lowerCAmelCase__      :      Optional[Any]         =		model_class(__a   )
										model.to(__a   )
										model.eval()

										with torch.no_grad():
													lowerCAmelCase__      :      int         =		model(**self._prepare_for_class(__a    ,     __a   )   )

										lowerCAmelCase__      :      Optional[int]         =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowerCAmelCase__      :      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]    ,     )

							lowerCAmelCase__      :      Optional[Any]         =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCAmelCase__      :      int         =		True
										check_hidden_states_output(__a    ,     __a    ,     __a   )

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

										check_hidden_states_output(__a    ,     __a    ,     __a   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a   )






				@slow
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__      :      Optional[int]         =		ConvNextVaModel.from_pretrained(__a   )
										self.assertIsNotNone(__a   )






def  lowerCAmelCase__			(  )				->		Any:
			lowerCAmelCase__      :      int         =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			return image







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






				@cached_property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'   ) if is_vision_available() else None






				@slow
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'   ).to(__a   )

							lowerCAmelCase__      :      Dict         =		self.default_image_processor
							lowerCAmelCase__      :      Any         =		prepare_img()
							lowerCAmelCase__      :      int         =		preprocessor(images=__a    ,     return_tensors='pt'   ).to(__a   )

							# forward pass
							with torch.no_grad():
										lowerCAmelCase__      :      int         =		model(**__a   )

							# verify the logits
							lowerCAmelCase__      :      List[str]         =		torch.Size((1, 1_000)   )
							self.assertEqual(outputs.logits.shape    ,     __a   )

							lowerCAmelCase__      :      Tuple         =		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   )   )







from math import pi
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  int					):
							return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
	print(arc_length(90, 10))