
import inspect
import unittest

from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaModel
					from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.config_class(**self.inputs_dict					)
					self.parent.assertTrue(hasattr(A				,"tf_padding"					)					)
					self.parent.assertTrue(hasattr(A				,"depth_multiplier"					)					)





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					Optional[Any]				,A   :					int				,A   :					List[Any]=13				,A   :					int=3				,A   :					Optional[Any]=32				,A   :					Union[str, Any]=0.25				,A   :					Tuple=8				,A   :					Optional[int]=True				,A   :					Union[str, Any]=10_24				,A   :					Any=32				,A   :					Optional[int]="relu6"				,A   :					int=0.1				,A   :					Optional[Any]=0.02				,A   :					Optional[Any]=True				,A   :					List[str]=True				,A   :					str=10				,A   :					str=None				,):
					__A       =					parent
					__A       =					batch_size
					__A       =					num_channels
					__A       =					image_size
					__A       =					depth_multiplier
					__A       =					min_depth
					__A       =					tf_padding
					__A       =					int(last_hidden_size * depth_multiplier					)
					__A       =					output_stride
					__A       =					hidden_act
					__A       =					classifier_dropout_prob
					__A       =					use_labels
					__A       =					is_training
					__A       =					num_labels
					__A       =					initializer_range
					__A       =					scope






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

					__A       =					self.get_config()

					return config, pixel_values, labels, pixel_labels






	def 							UpperCamelCase_    (			self   :					Any					):
					return MobileNetVaConfig(
					    num_channels=self.num_channels				,image_size=self.image_size				,depth_multiplier=self.depth_multiplier				,min_depth=self.min_depth				,tf_padding=self.tf_padding				,hidden_act=self.hidden_act				,classifier_dropout_prob=self.classifier_dropout_prob				,initializer_range=self.initializer_range				,)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					str				,A   :					Tuple				,A   :					Optional[int]				,A   :					List[str]					):
					__A       =					MobileNetVaModel(config=A					)
					model.to(A					)
					model.eval()
					__A       =					model(A					)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,(
					        self.batch_size,
					        self.last_hidden_size,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )				,)






	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					Union[str, Any]				,A   :					List[Any]				,A   :					int				,A   :					Union[str, Any]					):
					__A       =					self.num_labels
					__A       =					MobileNetVaForImageClassification(A					)
					model.to(A					)
					model.eval()
					__A       =					model(A				,labels=A					)
					self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_labels)					)







	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.prepare_config_and_inputs()
					__A     ,				__A     ,				__A     ,				__A       =					config_and_inputs
					__A       =					{"pixel_values": pixel_values}
					return config, inputs_dict





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




	'''simple docstring'''





	snake_case_          =	(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
	snake_case_          =	(
	    {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	snake_case_          =	False
	snake_case_          =	False
	snake_case_          =	False
	snake_case_          =	False






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					MobileNetVaModelTester(self					)
					__A       =					MobileNetVaConfigTester(self				,config_class=A				,has_text_modality=A					)






	def 							UpperCamelCase_    (			self   :					str					):
					self.config_tester.run_common_tests()






	@unittest.skip(reason="MobileNetV1 does not use inputs_embeds"					)
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					pass






	@unittest.skip(reason="MobileNetV1 does not support input and output embeddings"					)
	def 							UpperCamelCase_    (			self   :					Tuple					):
					pass






	@unittest.skip(reason="MobileNetV1 does not output attentions"					)
	def 							UpperCamelCase_    (			self   :					Any					):
					pass






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A     ,				__A       =					self.model_tester.prepare_config_and_inputs_for_common()

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

									__A       =					["pixel_values"]
									self.assertListEqual(arg_names[:1]				,A					)






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					def check_hidden_states_output(A   :					List[Any]				,A   :					List[Any]				,A   :					Optional[int]					):
									__A       =					model_class(A					)
									model.to(A					)
									model.eval()

									with torch.no_grad():
													__A       =					model(**self._prepare_for_class(A				,A					)					)

									__A       =					outputs.hidden_states

									__A       =					26
									self.assertEqual(len(A					)				,A					)

					__A     ,				__A       =					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A       =					True
									check_hidden_states_output(A				,A				,A					)

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

									check_hidden_states_output(A				,A				,A					)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A					)







	@slow
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A       =					MobileNetVaModel.from_pretrained(A					)
									self.assertIsNotNone(A					)





def 		UpperCAmelCase     (      )						->      str:







				"""simple docstring"""



				__A       =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				return image





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




	'''simple docstring'''





	@cached_property
	def 							UpperCamelCase_    (			self   :					List[str]					):
					return (
					    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"					) if is_vision_available() else None
					)







	@slow
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"					).to(A					)

					__A       =					self.default_image_processor
					__A       =					prepare_img()
					__A       =					image_processor(images=A				,return_tensors="pt"					).to(A					)

					# forward pass
					with torch.no_grad():
									__A       =					model(**A					)

					# verify the logits
					__A       =					torch.Size((1, 10_01)					)
					self.assertEqual(outputs.logits.shape				,A					)

					__A       =					torch.tensor([-4.17_39, -1.12_33, 3.12_05]					).to(A					)

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



_UpperCAmelCase						:  Any									=			6_5521





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       1
							snake_case_     							=       0
							for plain_chr in plain_text:
														snake_case_     							=       (a + ord(UpperCamelCase__ )) % MOD_ADLER
														snake_case_     							=       (b + a) % MOD_ADLER
							return (b << 16) | a




