


"""simple docstring"""
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    __A   (_lowerCamelCase):





							'''simple docstring'''

							def 						lowerCAmelCase				( self							:					Any				)    ->Tuple:



														"""simple docstring"""



														snake_case_							=						self.config_class(**self.inputs_dict				)
														self.parent.assertTrue(hasattr(lowerCAmelCase_						,				"""tf_padding"""				)				)
														self.parent.assertTrue(hasattr(lowerCAmelCase_						,				"""depth_multiplier"""				)				)





class    __A   :





							'''simple docstring'''

							def __init__( self							:					Union[str, Any]						,				UpperCAmelCase_							:					Union[str, Any]						,				UpperCAmelCase_							:					List[Any]=13						,				UpperCAmelCase_							:					Any=3						,				UpperCAmelCase_							:					List[Any]=32						,				UpperCAmelCase_							:					Any=0.25						,				UpperCAmelCase_							:					List[str]=8						,				UpperCAmelCase_							:					str=True						,				UpperCAmelCase_							:					List[Any]=1_024						,				UpperCAmelCase_							:					Optional[int]=32						,				UpperCAmelCase_							:					Optional[Any]="relu6"						,				UpperCAmelCase_							:					int=0.1						,				UpperCAmelCase_							:					Tuple=0.02						,				UpperCAmelCase_							:					Optional[int]=True						,				UpperCAmelCase_							:					Optional[Any]=True						,				UpperCAmelCase_							:					Any=10						,				UpperCAmelCase_							:					int=None						,				)    ->Tuple:



														"""simple docstring"""



														snake_case_							=						parent
														snake_case_							=						batch_size
														snake_case_							=						num_channels
														snake_case_							=						image_size
														snake_case_							=						depth_multiplier
														snake_case_							=						min_depth
														snake_case_							=						tf_padding
														snake_case_							=						int(last_hidden_size * depth_multiplier				)
														snake_case_							=						output_stride
														snake_case_							=						hidden_act
														snake_case_							=						classifier_dropout_prob
														snake_case_							=						use_labels
														snake_case_							=						is_training
														snake_case_							=						num_labels
														snake_case_							=						initializer_range
														snake_case_							=						scope



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



														"""simple docstring"""



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

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

														snake_case_							=						self.get_config()

														return config, pixel_values, labels, pixel_labels



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



														"""simple docstring"""



														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 						lowerCAmelCase				( self							:					int						,				UpperCAmelCase_							:					List[Any]						,				UpperCAmelCase_							:					List[Any]						,				UpperCAmelCase_							:					Optional[Any]						,				UpperCAmelCase_							:					List[str]				)    ->List[Any]:



														"""simple docstring"""



														snake_case_							=						MobileNetVaModel(config=lowerCAmelCase_				)
														model.to(lowerCAmelCase_				)
														model.eval()
														snake_case_							=						model(lowerCAmelCase_				)
														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 						lowerCAmelCase				( self							:					int						,				UpperCAmelCase_							:					Union[str, Any]						,				UpperCAmelCase_							:					Optional[int]						,				UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					str				)    ->List[str]:



														"""simple docstring"""



														snake_case_							=						self.num_labels
														snake_case_							=						MobileNetVaForImageClassification(lowerCAmelCase_				)
														model.to(lowerCAmelCase_				)
														model.eval()
														snake_case_							=						model(lowerCAmelCase_						,				labels=lowerCAmelCase_				)
														self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)				)



							def 						lowerCAmelCase				( self							:					int				)    ->Any:



														"""simple docstring"""



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





@require_torch
class    __A   (_lowerCamelCase  ,						_lowerCamelCase  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						List[Any]												=			(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							__lowercase:						List[str]												=			(
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowercase:						List[Any]												=			False
							__lowercase:						Optional[Any]												=			False
							__lowercase:						Optional[Any]												=			False
							__lowercase:						Dict												=			False



							def 						lowerCAmelCase				( self							:					str				)    ->Any:



														"""simple docstring"""



														snake_case_							=						MobileNetVaModelTester(self				)
														snake_case_							=						MobileNetVaConfigTester(self						,				config_class=lowerCAmelCase_						,				has_text_modality=lowerCAmelCase_				)



							def 						lowerCAmelCase				( self							:					Tuple				)    ->int:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""				)
							def 						lowerCAmelCase				( self							:					str				)    ->Tuple:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""				)
							def 						lowerCAmelCase				( self							:					Dict				)    ->Any:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""MobileNetV1 does not output attentions"""				)
							def 						lowerCAmelCase				( self							:					Dict				)    ->int:



														"""simple docstring"""



														pass



							def 						lowerCAmelCase				( self							:					List[Any]				)    ->List[str]:



														"""simple docstring"""



														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(lowerCAmelCase_				)
																					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]						,				lowerCAmelCase_				)



							def 						lowerCAmelCase				( self							:					str				)    ->List[Any]:



														"""simple docstring"""



														snake_case_							=						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_				)



							def 						lowerCAmelCase				( self							:					List[Any]				)    ->Dict:



														"""simple docstring"""



														def check_hidden_states_output(UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					Optional[Any]						,				UpperCAmelCase_							:					Tuple				):
																					snake_case_							=						model_class(lowerCAmelCase_				)
																					model.to(lowerCAmelCase_				)
																					model.eval()

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

																					snake_case_							=						outputs.hidden_states

																					snake_case_							=						26
																					self.assertEqual(len(lowerCAmelCase_				)						,				lowerCAmelCase_				)

														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(lowerCAmelCase_						,				lowerCAmelCase_						,				lowerCAmelCase_				)

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

																					check_hidden_states_output(lowerCAmelCase_						,				lowerCAmelCase_						,				lowerCAmelCase_				)



							def 						lowerCAmelCase				( self							:					Dict				)    ->Tuple:



														"""simple docstring"""



														snake_case_							=						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_				)



							@slow
							def 						lowerCAmelCase				( self							:					List[Any]				)    ->List[str]:



														"""simple docstring"""



														for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					snake_case_							=						MobileNetVaModel.from_pretrained(lowerCAmelCase_				)
																					self.assertIsNotNone(lowerCAmelCase_				)







def   _a (					)			-> List[Any]:
							snake_case_							=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
							return image





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





							'''simple docstring'''

							@cached_property
							def 						lowerCAmelCase				( self							:					Optional[int]				)    ->List[Any]:



														"""simple docstring"""



														return (
														    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""				) if is_vision_available() else None
														)



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



														"""simple docstring"""



														snake_case_							=						MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""				).to(lowerCAmelCase_				)

														snake_case_							=						self.default_image_processor
														snake_case_							=						prepare_img()
														snake_case_							=						image_processor(images=lowerCAmelCase_						,				return_tensors="""pt"""				).to(lowerCAmelCase_				)

														# forward pass
														with torch.no_grad():
																					snake_case_							=						model(**lowerCAmelCase_				)

														# verify the logits
														snake_case_							=						torch.Size((1, 1_001)				)
														self.assertEqual(outputs.logits.shape						,				lowerCAmelCase_				)

														snake_case_							=						torch.tensor([-4.1_739, -1.1_233, 3.1_205]				).to(lowerCAmelCase_				)

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





'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 1_000_000	)       ->	int:
	_snake_case   						=		limit + 1
	_snake_case   						=		[0] * limit
	for first_term in range(1			,				__A	):
		for n in range(__A			,				__A			,				__A	):
			_snake_case   						=		first_term + n / first_term
			if common_difference % 4:  # d must be divisble by 4
				continue
			else:
				common_difference /= 4
				if (
				    first_term > common_difference
				    and first_term < 4 * common_difference
				):  # since x,y,z are positive integers
					frequency[n] += 1  # so z>0 and a>d ,also 4d<a

	_snake_case   						=		sum(1 for x in frequency[1:limit] if x == 10	)

	return count


if __name__ == "__main__":
					print(F'''{solution() = }''')

