

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, MobileNetVaForSemanticSegmentation, MobileNetVaModel
					from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor






class 			A_						(			_a							):
							def        lowerCAmelCase	(						self						:     Tuple):
								__lowerCamelCase							:							Optional[int]   			=					self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,'tf_padding'))
								self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,'depth_multiplier'))






class 			A_						:
							def __init__(						self						:     Optional[int]  ,SCREAMING_SNAKE_CASE__						:     Optional[Any]  ,SCREAMING_SNAKE_CASE__						:     str=1_3  ,SCREAMING_SNAKE_CASE__						:     Dict=3  ,SCREAMING_SNAKE_CASE__						:     Tuple=3_2  ,SCREAMING_SNAKE_CASE__						:     Dict=0.25  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=8  ,SCREAMING_SNAKE_CASE__						:     int=8  ,SCREAMING_SNAKE_CASE__						:     Dict=6  ,SCREAMING_SNAKE_CASE__						:     Optional[Any]=3_2  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]=True  ,SCREAMING_SNAKE_CASE__						:     Tuple=True  ,SCREAMING_SNAKE_CASE__						:     List[str]=True  ,SCREAMING_SNAKE_CASE__						:     Optional[Any]="relu6"  ,SCREAMING_SNAKE_CASE__						:     Dict=1_2_8_0  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]=0.1  ,SCREAMING_SNAKE_CASE__						:     List[str]=0.02  ,SCREAMING_SNAKE_CASE__						:     Tuple=True  ,SCREAMING_SNAKE_CASE__						:     Tuple=True  ,SCREAMING_SNAKE_CASE__						:     Tuple=1_0  ,SCREAMING_SNAKE_CASE__						:     Tuple=None  ,):
								__lowerCamelCase							:							Union[str, Any]   			=					parent
								__lowerCamelCase							:							List[str]   			=					batch_size
								__lowerCamelCase							:							str   			=					num_channels
								__lowerCamelCase							:							List[str]   			=					image_size
								__lowerCamelCase							:							Optional[int]   			=					depth_multiplier
								__lowerCamelCase							:							str   			=					depth_divisible_by
								__lowerCamelCase							:							List[Any]   			=					min_depth
								__lowerCamelCase							:							str   			=					expand_ratio
								__lowerCamelCase							:							Any   			=					tf_padding
								__lowerCamelCase							:							int   			=					output_stride
								__lowerCamelCase							:							Optional[Any]   			=					first_layer_is_expansion
								__lowerCamelCase							:							str   			=					finegrained_output
								__lowerCamelCase							:							List[str]   			=					hidden_act
								__lowerCamelCase							:							List[Any]   			=					last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier)
								__lowerCamelCase							:							Tuple   			=					classifier_dropout_prob
								__lowerCamelCase							:							Union[str, Any]   			=					use_labels
								__lowerCamelCase							:							int   			=					is_training
								__lowerCamelCase							:							List[Any]   			=					num_labels
								__lowerCamelCase							:							List[Any]   			=					initializer_range
								__lowerCamelCase							:							Any   			=					scope
							def        lowerCAmelCase	(						self						:     Optional[int]):
								__lowerCamelCase							:							List[str]   			=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								__lowerCamelCase							:							str   			=					None
								__lowerCamelCase							:							Tuple   			=					None
								if self.use_labels:
									__lowerCamelCase							:							Any   			=					ids_tensor([self.batch_size]  ,self.num_labels)
									__lowerCamelCase							:							Optional[int]   			=					ids_tensor([self.batch_size, self.image_size, self.image_size]  ,self.num_labels)

								__lowerCamelCase							:							Dict   			=					self.get_config()

								return config, pixel_values, labels, pixel_labels
							def        lowerCAmelCase	(						self						:     Optional[Any]):
								return MobileNetVaConfig(
								    num_channels=self.num_channels  ,image_size=self.image_size  ,depth_multiplier=self.depth_multiplier  ,depth_divisible_by=self.depth_divisible_by  ,min_depth=self.min_depth  ,expand_ratio=self.expand_ratio  ,output_stride=self.output_stride  ,first_layer_is_expansion=self.first_layer_is_expansion  ,finegrained_output=self.finegrained_output  ,hidden_act=self.hidden_act  ,tf_padding=self.tf_padding  ,classifier_dropout_prob=self.classifier_dropout_prob  ,initializer_range=self.initializer_range  ,)
							def        lowerCAmelCase	(						self						:     Any  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     Dict):
								__lowerCamelCase							:							int   			=					MobileNetVaModel(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase							:							Optional[int]   			=					model(SCREAMING_SNAKE_CASE__)
								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,
								    )  ,)
								self.parent.assertEqual(
								    result.pooler_output.shape  ,(self.batch_size, self.last_hidden_size)  ,)
							def        lowerCAmelCase	(						self						:     List[Any]  ,SCREAMING_SNAKE_CASE__						:     int  ,SCREAMING_SNAKE_CASE__						:     str  ,SCREAMING_SNAKE_CASE__						:     str  ,SCREAMING_SNAKE_CASE__						:     int):
								__lowerCamelCase							:							Dict   			=					self.num_labels
								__lowerCamelCase							:							Dict   			=					MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase							:							str   			=					model(SCREAMING_SNAKE_CASE__  ,labels=SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels))
							def        lowerCAmelCase	(						self						:     List[Any]  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     Dict  ,SCREAMING_SNAKE_CASE__						:     List[Any]  ,SCREAMING_SNAKE_CASE__						:     List[str]):
								__lowerCamelCase							:							str   			=					self.num_labels
								__lowerCamelCase							:							List[str]   			=					MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase							:							Any   			=					model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(
								    result.logits.shape  ,(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )  ,)
								__lowerCamelCase							:							List[Any]   			=					model(SCREAMING_SNAKE_CASE__  ,labels=SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(
								    result.logits.shape  ,(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )  ,)


							def        lowerCAmelCase	(						self						:     Union[str, Any]):
								__lowerCamelCase							:							Tuple   			=					self.prepare_config_and_inputs()
								__lowerCamelCase							:							Dict   			=					config_and_inputs
								__lowerCamelCase							:							str   			=					{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 			A_						(			_a     ,				_a     ,				unittest.TestCase							):
							_UpperCAmelCase       :    Union[str, Any]		 = (
							    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase       :    Optional[Any]		 = (
							    {
							        """feature-extraction""": MobileNetVaModel,
							        """image-classification""": MobileNetVaForImageClassification,
							        """image-segmentation""": MobileNetVaForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase       :    Optional[Any]		 = False
							_UpperCAmelCase       :    List[Any]		 = False
							_UpperCAmelCase       :    Dict		 = False
							_UpperCAmelCase       :    List[str]		 = False
							def        lowerCAmelCase	(						self						:     Union[str, Any]):
								__lowerCamelCase							:							int   			=					MobileNetVaModelTester(self)
								__lowerCamelCase							:							Tuple   			=					MobileNetVaConfigTester(self  ,config_class=SCREAMING_SNAKE_CASE__  ,has_text_modality=SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Any):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='MobileNetV2 does not use inputs_embeds')
							def        lowerCAmelCase	(						self						:     Any):
								pass
							@unittest.skip(reason='MobileNetV2 does not support input and output embeddings')
							def        lowerCAmelCase	(						self						:     int):
								pass
							@unittest.skip(reason='MobileNetV2 does not output attentions')
							def        lowerCAmelCase	(						self						:     List[str]):
								pass
							def        lowerCAmelCase	(						self						:     int):
								__lowerCamelCase							:							int   			=					self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase							:							List[str]   			=					['pixel_values']
									self.assertListEqual(arg_names[:1]  ,SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Tuple):
								__lowerCamelCase							:							Optional[Any]   			=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Any):

								def check_hidden_states_output(SCREAMING_SNAKE_CASE__						:     Dict  ,SCREAMING_SNAKE_CASE__						:     int  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]):
									__lowerCamelCase							:							Dict   			=					model_class(SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()

									with torch.no_grad():
										__lowerCamelCase							:							str   			=					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__))

									__lowerCamelCase							:							str   			=					outputs.hidden_states

									__lowerCamelCase							:							Union[str, Any]   			=					1_6
									self.assertEqual(len(SCREAMING_SNAKE_CASE__)  ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase							:							Optional[int]   			=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase							:							str   			=					True
									check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase							:							int   			=					True

									check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Optional[Any]):
								__lowerCamelCase							:							Tuple   			=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Optional[Any]):
								__lowerCamelCase							:							Optional[Any]   			=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE__)


							@slow
							def        lowerCAmelCase	(						self						:     int):

								for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase							:							List[str]   			=					MobileNetVaModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)






def   SCREAMING_SNAKE_CASE__			(    )    ->	Optional[int]:
	__lowerCamelCase							:							Union[str, Any]   			=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
	return image






@require_torch
@require_vision
class 			A_						(			unittest.TestCase							):
							@cached_property
							def        lowerCAmelCase	(						self						:     Union[str, Any]):
								return (
								    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224') if is_vision_available() else None
								)
							@slow
							def        lowerCAmelCase	(						self						:     Any):
								__lowerCamelCase							:							str   			=					MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224').to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase							:							Union[str, Any]   			=					self.default_image_processor
								__lowerCamelCase							:							Tuple   			=					prepare_img()
								__lowerCamelCase							:							Union[str, Any]   			=					image_processor(images=SCREAMING_SNAKE_CASE__  ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

								# forward pass
								with torch.no_grad():
									__lowerCamelCase							:							List[Any]   			=					model(**SCREAMING_SNAKE_CASE__)

								# verify the logits
								__lowerCamelCase							:							Union[str, Any]   			=					torch.Size((1, 1_0_0_1))
								self.assertEqual(outputs.logits.shape  ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase							:							str   			=					torch.tensor([0.2445, -1.1993, 0.1905]).to(SCREAMING_SNAKE_CASE__)

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


							@slow
							def        lowerCAmelCase	(						self						:     List[str]):
								__lowerCamelCase							:							Optional[Any]   			=					MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513')
								__lowerCamelCase							:							Union[str, Any]   			=					model.to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase							:							int   			=					MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513')

								__lowerCamelCase							:							Tuple   			=					prepare_img()
								__lowerCamelCase							:							Any   			=					image_processor(images=SCREAMING_SNAKE_CASE__  ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

								# forward pass
								with torch.no_grad():
									__lowerCamelCase							:							Tuple   			=					model(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase							:							List[str]   			=					outputs.logits

								# verify the logits
								__lowerCamelCase							:							Any   			=					torch.Size((1, 2_1, 6_5, 6_5))
								self.assertEqual(logits.shape  ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase							:							Optional[int]   			=					torch.tensor(
								    [
								        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
								        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
								        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
								    ]  ,device=SCREAMING_SNAKE_CASE__  ,)

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








from maths.prime_factors import prime_factors



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        _lowercase			:				str 						=						F'''Input value of [number={number}] must be an integer'''
        raise TypeError(lowerCamelCase_					)
    if number < 1:
        raise ValueError('Input must be a positive integer'					)
    return -1 if len(prime_factors(lowerCamelCase_					)					) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()


