



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					( __UpperCAmelCase      ):





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.config_class(**self.inputs_dict				)
						self.parent.assertTrue(hasattr(_lowerCamelCase							,    """tf_padding"""				)				)
						self.parent.assertTrue(hasattr(_lowerCamelCase							,    """depth_multiplier"""				)				)







class 				__A					:





			def __init__(self							,    __magic_name__							,    __magic_name__=13							,    __magic_name__=3							,    __magic_name__=32							,    __magic_name__=0.25							,    __magic_name__=8							,    __magic_name__=True							,    __magic_name__=1024							,    __magic_name__=32							,    __magic_name__="relu6"							,    __magic_name__=0.1							,    __magic_name__=0.02							,    __magic_name__=True							,    __magic_name__=True							,    __magic_name__=10							,    __magic_name__=None							,    ):
						lowerCamelCase__ :      List[Any]								=		parent
						lowerCamelCase__ :      Any								=		batch_size
						lowerCamelCase__ :      Any								=		num_channels
						lowerCamelCase__ :      str								=		image_size
						lowerCamelCase__ :      Union[str, Any]								=		depth_multiplier
						lowerCamelCase__ :      Optional[Any]								=		min_depth
						lowerCamelCase__ :      Union[str, Any]								=		tf_padding
						lowerCamelCase__ :      Any								=		int(last_hidden_size * depth_multiplier				)
						lowerCamelCase__ :      Optional[int]								=		output_stride
						lowerCamelCase__ :      List[Any]								=		hidden_act
						lowerCamelCase__ :      Optional[Any]								=		classifier_dropout_prob
						lowerCamelCase__ :      Union[str, Any]								=		use_labels
						lowerCamelCase__ :      str								=		is_training
						lowerCamelCase__ :      List[Any]								=		num_labels
						lowerCamelCase__ :      List[str]								=		initializer_range
						lowerCamelCase__ :      str								=		scope





			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowerCamelCase__ :      List[str]								=		None
						lowerCamelCase__ :      List[Any]								=		None
						if self.use_labels:
									lowerCamelCase__ :      List[Any]								=		ids_tensor([self.batch_size]							,    self.num_labels				)
									lowerCamelCase__ :      int								=		ids_tensor([self.batch_size, self.image_size, self.image_size]							,    self.num_labels				)

						lowerCamelCase__ :      List[str]								=		self.get_config()

						return config, pixel_values, labels, pixel_labels





			def        _snake_case    (self				):
						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        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      int								=		MobileNetVaModel(config=_lowerCamelCase				)
						model.to(_lowerCamelCase				)
						model.eval()
						lowerCamelCase__ :      Any								=		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        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		self.num_labels
						lowerCamelCase__ :      Union[str, Any]								=		MobileNetVaForImageClassification(_lowerCamelCase				)
						model.to(_lowerCamelCase				)
						model.eval()
						lowerCamelCase__ :      Dict								=		model(_lowerCamelCase							,    labels=_lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		self.prepare_config_and_inputs()
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		config_and_inputs
						lowerCamelCase__ :      Union[str, Any]								=		{"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class 				__A					( __UpperCAmelCase					, __UpperCAmelCase					, unittest.TestCase      ):
			UpperCamelCase					:int     =			(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
			UpperCamelCase					:int     =			(
			    {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase					:str     =			False
			UpperCamelCase					:int     =			False
			UpperCamelCase					:str     =			False
			UpperCamelCase					:Optional[Any]     =			False





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		MobileNetVaModelTester(self				)
						lowerCamelCase__ :      Tuple								=		MobileNetVaConfigTester(self							,    config_class=_lowerCamelCase							,    has_text_modality=_lowerCamelCase				)





			def        _snake_case    (self				):
						self.config_tester.run_common_tests()





			@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""				)
			def        _snake_case    (self				):
						pass





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





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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									lowerCamelCase__ :      Union[str, Any]								=		model_class(_lowerCamelCase				)
									lowerCamelCase__ :      str								=		inspect.signature(model.forward				)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowerCamelCase__ :      Union[str, Any]								=		[*signature.parameters.keys()]

									lowerCamelCase__ :      Any								=		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]							,    _lowerCamelCase				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_lowerCamelCase				)





			def        _snake_case    (self				):
						def check_hidden_states_output(__magic_name__							,    __magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Optional[int]								=		model_class(_lowerCamelCase				)
									model.to(_lowerCamelCase				)
									model.eval()

									with torch.no_grad():
												lowerCamelCase__ :      int								=		model(**self._prepare_for_class(_lowerCamelCase							,    _lowerCamelCase				)				)

									lowerCamelCase__ :      Optional[Any]								=		outputs.hidden_states

									lowerCamelCase__ :      Union[str, Any]								=		26
									self.assertEqual(len(_lowerCamelCase				)							,    _lowerCamelCase				)

						lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									lowerCamelCase__ :      Optional[int]								=		True
									check_hidden_states_output(_lowerCamelCase							,    _lowerCamelCase							,    _lowerCamelCase				)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									lowerCamelCase__ :      List[str]								=		True

									check_hidden_states_output(_lowerCamelCase							,    _lowerCamelCase							,    _lowerCamelCase				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase				)





			@slow
			def        _snake_case    (self				):
						for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase__ :      Optional[Any]								=		MobileNetVaModel.from_pretrained(_lowerCamelCase				)
									self.assertIsNotNone(_lowerCamelCase				)







def      _A				()  ->Dict:



			'''simple docstring'''







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





			@slow
			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""				).to(_lowerCamelCase				)

						lowerCamelCase__ :      Optional[int]								=		self.default_image_processor
						lowerCamelCase__ :      Any								=		prepare_img()
						lowerCamelCase__ :      Tuple								=		image_processor(images=_lowerCamelCase							,    return_tensors="""pt"""				).to(_lowerCamelCase				)

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

						# verify the logits
						lowerCamelCase__ :      Optional[int]								=		torch.Size((1, 1001)				)
						self.assertEqual(outputs.logits.shape							,    _lowerCamelCase				)

						lowerCamelCase__ :      List[str]								=		torch.tensor([-4.17_39, -1.12_33, 3.12_05]				).to(_lowerCamelCase				)

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








from math import factorial, pi






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      int = 30 )  ->float:



			'''simple docstring'''







			if not isinstance(UpperCamelCase      ,			(int, float) ):
						raise ValueError("""maclaurin_sin() requires either an int or float for theta""" )

			if not isinstance(UpperCamelCase      ,			UpperCamelCase ) or accuracy <= 0:
						raise ValueError("""maclaurin_sin() requires a positive int for accuracy""" )

			lowerCamelCase__ :      Dict								=		float(UpperCamelCase )
			lowerCamelCase__ :      int								=		theta // (2 * pi)
			theta -= 2 * div * pi
			return sum(
			    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(UpperCamelCase ) )






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      int = 30 )  ->float:



			'''simple docstring'''







			if not isinstance(UpperCamelCase      ,			(int, float) ):
						raise ValueError("""maclaurin_cos() requires either an int or float for theta""" )

			if not isinstance(UpperCamelCase      ,			UpperCamelCase ) or accuracy <= 0:
						raise ValueError("""maclaurin_cos() requires a positive int for accuracy""" )

			lowerCamelCase__ :      int								=		float(UpperCamelCase )
			lowerCamelCase__ :      Optional[int]								=		theta // (2 * pi)
			theta -= 2 * div * pi
			return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(UpperCamelCase ) )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				print(maclaurin_sin(10))
				print(maclaurin_sin(-10))
				print(maclaurin_sin(10, 15))
				print(maclaurin_sin(-10, 15))

				print(maclaurin_cos(5))
				print(maclaurin_cos(-5))
				print(maclaurin_cos(10, 15))
				print(maclaurin_cos(-10, 15))



