


import inspect
import unittest

from transformers import MobileViTConfig
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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
     from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor


class 			A						(							snake_case__			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       self.config_class(**self.inputs_dict		)
        self.parent.assertTrue(hasattr(_A,'hidden_sizes'		)		)
        self.parent.assertTrue(hasattr(_A,'neck_hidden_sizes'		)		)
        self.parent.assertTrue(hasattr(_A,'num_attention_heads'		)		)




class 			A						:







    """simple docstring"""





    def __init__(				self			:	Optional[int],lowercase_			:	Optional[int],lowercase_			:	Tuple=1_3,lowercase_			:	Optional[Any]=3_2,lowercase_			:	List[Any]=2,lowercase_			:	Any=3,lowercase_			:	str=6_4_0,lowercase_			:	List[Any]=4,lowercase_			:	Any="silu",lowercase_			:	Optional[Any]=3,lowercase_			:	Dict=3_2,lowercase_			:	Union[str, Any]=0.1,lowercase_			:	Optional[int]=0.1,lowercase_			:	int=0.1,lowercase_			:	Any=0.02,lowercase_			:	Dict=True,lowercase_			:	Optional[int]=True,lowercase_			:	Union[str, Any]=1_0,lowercase_			:	Optional[int]=None,)->   Dict:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       batch_size
        A__   				=       image_size
        A__   				=       patch_size
        A__   				=       num_channels
        A__   				=       last_hidden_size
        A__   				=       num_attention_heads
        A__   				=       hidden_act
        A__   				=       conv_kernel_size
        A__   				=       output_stride
        A__   				=       hidden_dropout_prob
        A__   				=       attention_probs_dropout_prob
        A__   				=       classifier_dropout_prob
        A__   				=       use_labels
        A__   				=       is_training
        A__   				=       num_labels
        A__   				=       initializer_range
        A__   				=       scope


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





        '''simple docstring'''







        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 						snake_case__					(				self			:	List[str]		)->   int:





        '''simple docstring'''







        return MobileViTConfig(
            image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,num_attention_heads=self.num_attention_heads,hidden_act=self.hidden_act,conv_kernel_size=self.conv_kernel_size,output_stride=self.output_stride,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,classifier_dropout_prob=self.classifier_dropout_prob,initializer_range=self.initializer_range,)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Optional[int],lowercase_			:	Tuple,lowercase_			:	List[Any],lowercase_			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       MobileViTModel(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 						snake_case__					(				self			:	Dict,lowercase_			:	List[str],lowercase_			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	Union[str, Any]		)->   int:





        '''simple docstring'''







        A__   				=       self.num_labels
        A__   				=       MobileViTForImageClassification(_A		)
        model.to(_A		)
        model.eval()
        A__   				=       model(_A,labels=_A		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Union[str, Any],lowercase_			:	List[Any],lowercase_			:	Union[str, Any],lowercase_			:	List[str]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self.num_labels
        A__   				=       MobileViTForSemanticSegmentation(_A		)
        model.to(_A		)
        model.eval()
        A__   				=       model(_A		)
        self.parent.assertEqual(
            result.logits.shape,(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            ),)
        A__   				=       model(_A,labels=_A		)
        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 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        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 			A						(							snake_case__   ,							snake_case__   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							(
        (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    lowerCamelCase		     =							(
        {
            """feature-extraction""": MobileViTModel,
            """image-classification""": MobileViTForImageClassification,
            """image-segmentation""": MobileViTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False


    def 						snake_case__					(				self			:	str		)->   Any:





        '''simple docstring'''







        A__   				=       MobileViTModelTester(self		)
        A__   				=       MobileViTConfigTester(self,config_class=_A,has_text_modality=_A		)


    def 						snake_case__					(				self			:	Optional[int]		)->   str:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    @unittest.skip(reason='MobileViT does not use inputs_embeds'		)
    def 						snake_case__					(				self			:	Union[str, Any]		)->   List[str]:





        '''simple docstring'''







        pass


    @unittest.skip(reason='MobileViT does not support input and output embeddings'		)
    def 						snake_case__					(				self			:	Dict		)->   int:





        '''simple docstring'''







        pass


    @unittest.skip(reason='MobileViT does not output attentions'		)
    def 						snake_case__					(				self			:	Any		)->   List[Any]:





        '''simple docstring'''







        pass


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





        '''simple docstring'''







        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		)


    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	Any		)->   str:





        '''simple docstring'''







        A__   				=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_A		)


    def 						snake_case__					(				self			:	Tuple		)->   Optional[Any]:





        '''simple docstring'''







        def check_hidden_states_output(lowercase_			:	List[Any],lowercase_			:	int,lowercase_			:	List[Any]		):
            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__   				=       5
            self.assertEqual(len(_A		),_A		)

            # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
            # with the width and height being successively divided by 2.
            A__   				=       2
            for i in range(len(_A		)		):
                self.assertListEqual(
                    list(hidden_states[i].shape[-2:]		),[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],)
                divisor *= 2

            self.assertEqual(self.model_tester.output_stride,divisor // 2		)

        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 						snake_case__					(				self			:	Union[str, Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_A		)


    def 						snake_case__					(				self			:	str		)->   int:





        '''simple docstring'''







        A__   				=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*_A		)


    @slow
    def 						snake_case__					(				self			:	str		)->   Any:





        '''simple docstring'''







        for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A__   				=       MobileViTModel.from_pretrained(_A		)
            self.assertIsNotNone(_A		)




def 						_snake_case(   )							->				int:





    '''simple docstring'''






    A__   				=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image


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







    """simple docstring"""





    @cached_property
    def 						snake_case__					(				self			:	Tuple		)->   Tuple:





        '''simple docstring'''







        return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'		) if is_vision_available() else None


    @slow
    def 						snake_case__					(				self			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'		).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, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,_A		)

        A__   				=       torch.tensor([-1.9_364, -1.2_327, -0.4_653]		).to(_A		)

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


    @slow
    def 						snake_case__					(				self			:	Optional[int]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)
        A__   				=       model.to(_A		)

        A__   				=       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)

        A__   				=       prepare_img()
        A__   				=       image_processor(images=_A,return_tensors='pt'		).to(_A		)

        # forward pass
        with torch.no_grad():
            A__   				=       model(**_A		)
        A__   				=       outputs.logits

        # verify the logits
        A__   				=       torch.Size((1, 2_1, 3_2, 3_2)		)
        self.assertEqual(logits.shape,_A		)

        A__   				=       torch.tensor(
            [
                [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
                [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
                [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
            ],device=_A,)

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


    @slow
    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)
        A__   				=       model.to(_A		)

        A__   				=       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)

        A__   				=       prepare_img()
        A__   				=       image_processor(images=_A,return_tensors='pt'		).to(_A		)

        # forward pass
        with torch.no_grad():
            A__   				=       model(**_A		)

        A__   				=       outputs.logits.detach().cpu()

        A__   				=       image_processor.post_process_semantic_segmentation(outputs=_A,target_sizes=[(5_0, 6_0)]		)
        A__   				=       torch.Size((5_0, 6_0)		)
        self.assertEqual(segmentation[0].shape,_A		)

        A__   				=       image_processor.post_process_semantic_segmentation(outputs=_A		)
        A__   				=       torch.Size((3_2, 3_2)		)
        self.assertEqual(segmentation[0].shape,_A		)




from __future__ import annotations

from scipy.special import comb  # type: ignore


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	list[tuple[float, float]]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       list_of_points
        # Degree determines the flexibility of the curve.
        # Degree = 1 will produce a straight line.
        A__   				=       len(lowercase_		) - 1


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	float		)->   list[float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        A__   				=       []
        for i in range(len(self.list_of_points		)		):
            # basis function for each i
            output_values.append(
                comb(self.degree,lowercase_		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
        # the basis must sum up to 1 for it to produce a valid Bezier curve.
        assert round(sum(lowercase_		),5		) == 1
        return output_values


    def 						snake_case__					(				self			:	str,lowercase_			:	float		)->   tuple[float, float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."

        A__   				=       self.basis_function(lowercase_		)
        A__   				=       0.0
        A__   				=       0.0
        for i in range(len(self.list_of_points		)		):
            # For all points, sum up the product of i-th basis function and i-th point.
            x += basis_function[i] * self.list_of_points[i][0]
            y += basis_function[i] * self.list_of_points[i][1]
        return (x, y)


    def 						snake_case__					(				self			:	str,lowercase_			:	float = 0.01		)->   str:





        '''simple docstring'''







        from matplotlib import pyplot as plt  # type: ignore

        A__   				=       []  # x coordinates of points to plot
        A__   				=       []  # y coordinates of points to plot

        A__   				=       0.0
        while t <= 1:
            A__   				=       self.bezier_curve_function(lowercase_		)
            to_plot_x.append(value[0]		)
            to_plot_y.append(value[1]		)
            t += step_size

        A__   				=       [i[0] for i in self.list_of_points]
        A__   				=       [i[1] for i in self.list_of_points]

        plt.plot(
            lowercase_,lowercase_,color='blue',label='Curve of Degree ' + str(self.degree		),)
        plt.scatter(lowercase_,lowercase_,color='red',label='Control Points'		)
        plt.legend()
        plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
     BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
     BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

