

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
A_						:     str			  =				20

# Time between time steps in seconds
A_						:     List[str]			  =				INTERVAL / 1000
class    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__       , lowercase__       , lowercase__       , lowercase__ = 1.0       , lowercase__ = 1.0       , lowercase__ = "blue"       , )       -> None:
        __UpperCAmelCase			   =     position_x
        __UpperCAmelCase			   =     position_y
        __UpperCAmelCase			   =     velocity_x
        __UpperCAmelCase			   =     velocity_y
        __UpperCAmelCase			   =     mass
        __UpperCAmelCase			   =     size
        __UpperCAmelCase			   =     color


    @property
    def        lowerCAmelCase_  (self					)       -> tuple[float, float]:
        return self.position_x, self.position_y


    @property
    def        lowerCAmelCase_  (self					)       -> tuple[float, float]:
        return self.velocity_x, self.velocity_y


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> None:
        self.velocity_x += force_x * delta_time
        self.velocity_y += force_y * delta_time






    def        lowerCAmelCase_  (self       , lowercase__					)       -> None:
        self.position_x += self.velocity_x * delta_time
        self.position_y += self.velocity_y * delta_time






class    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__ = 1.0       , lowercase__ = 1.0       , lowercase__ = 0.0       , )       -> None:
        __UpperCAmelCase			   =     bodies
        __UpperCAmelCase			   =     gravitation_constant
        __UpperCAmelCase			   =     time_factor
        __UpperCAmelCase			   =     softening_factor


    def __len__(self					)       -> int:
        return len(self.bodies					)






    def        lowerCAmelCase_  (self       , lowercase__					)       -> None:






        for bodya in self.bodies:
            __UpperCAmelCase			   =     0.0
            __UpperCAmelCase			   =     0.0
            for bodya in self.bodies:
                if bodya != bodya:
                    __UpperCAmelCase			   =     bodya.position_x - bodya.position_x
                    __UpperCAmelCase			   =     bodya.position_y - bodya.position_y

                    # Calculation of the distance using Pythagoras's theorem
                    # Extra factor due to the softening technique
                    __UpperCAmelCase			   =     (dif_x**2 + dif_y**2 + self.softening_factor) ** (
                        1 / 2
                    )

                    # Newton's law of universal gravitation.
                    force_x += (
                        self.gravitation_constant * bodya.mass * dif_x / distance**3
                    )
                    force_y += (
                        self.gravitation_constant * bodya.mass * dif_y / distance**3
                    )

                      # Update the body's velocity once all the force components have been added
            bodya.update_velocity(UpperCamelCase__       , UpperCamelCase__       , delta_time * self.time_factor					)

        # Update the positions only after all the velocities have been updated
        for body in self.bodies:
            body.update_position(delta_time * self.time_factor					)






def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Dict:






    '''simple docstring'''
    body_system.update_system(A_ )

    # Update the positions of the patches
    for patch, body in zip(A_    ,				body_system.bodies ):
        __UpperCAmelCase			   =     (body.position_x, body.position_y)


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE = -1    ,				SCREAMING_SNAKE_CASE = 1    ,				SCREAMING_SNAKE_CASE = -1    ,				SCREAMING_SNAKE_CASE = 1    ,				) ->       List[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     plt.figure()
    fig.canvas.set_window_title(A_ )
    __UpperCAmelCase			   =     plt.axes(
        xlim=(x_start, x_end)    ,				ylim=(y_start, y_end) )  # Set section to be plotted
    plt.gca().set_aspect('''equal''' )  # Fix aspect ratio

    # Each body is drawn as a patch by the plt-function
    __UpperCAmelCase			   =     [
        plt.Circle((body.position_x, body.position_y)    ,				body.size    ,				fc=body.color )
        for body in body_system.bodies
    ]

    for patch in patches:
        ax.add_patch(A_ )

    # Function called at each step of the animation
    def update(SCREAMING_SNAKE_CASE ) -> list[plt.Circle]:
        update_step(A_    ,				A_    ,				A_ )
        return patches

    __UpperCAmelCase			   =     animation.FuncAnimation(  # noqa: F841
        A_    ,				A_    ,				interval=A_    ,				blit=A_ )

    plt.show()


def  __a    (				) ->       str:






    '''simple docstring'''
    __UpperCAmelCase			   =     0.9700436
    __UpperCAmelCase			   =     -0.24308753
    __UpperCAmelCase			   =     0.466203685
    __UpperCAmelCase			   =     0.43236573

    __UpperCAmelCase			   =     [
        Body(A_    ,				A_    ,				A_    ,				A_    ,				size=0.2    ,				color='''red''' ),
        Body(-position_x    ,				-position_y    ,				A_    ,				A_    ,				size=0.2    ,				color='''green''' ),
        Body(0    ,				0    ,				-2 * velocity_x    ,				-2 * velocity_y    ,				size=0.2    ,				color='''blue''' ),
    ]
    return BodySystem(A_    ,				time_factor=3 )


def  __a    (				) ->       Tuple:






    '''simple docstring'''
    __UpperCAmelCase			   =     7.3476e22
    __UpperCAmelCase			   =     5.972e24
    __UpperCAmelCase			   =     1_0_2_2
    __UpperCAmelCase			   =     3_8_4_3_9_9_0_0_0
    __UpperCAmelCase			   =     6.674e-11

    # Calculation of the respective velocities so that total impulse is zero,
    # i.e. the two bodies together don't move
    __UpperCAmelCase			   =     earth_mass * velocity_dif / (earth_mass + moon_mass)
    __UpperCAmelCase			   =     moon_velocity - velocity_dif

    __UpperCAmelCase			   =     Body(-earth_moon_distance    ,				0    ,				0    ,				A_    ,				A_    ,				1_0_0_0_0_0_0_0    ,				'''grey''' )
    __UpperCAmelCase			   =     Body(0    ,				0    ,				0    ,				A_    ,				A_    ,				5_0_0_0_0_0_0_0    ,				'''blue''' )
    return BodySystem([earth, moon]    ,				A_    ,				time_factor=1_0_0_0_0_0_0 )


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






    '''simple docstring'''
    __UpperCAmelCase			   =     []
    for _ in range(1_0 ):
        __UpperCAmelCase			   =     random.uniform(-0.5    ,				0.5 )
        __UpperCAmelCase			   =     random.uniform(-0.5    ,				0.5 )

        # Bodies are created pairwise with opposite velocities so that the
        # total impulse remains zero
        bodies.append(
            Body(
                random.uniform(-0.5    ,				0.5 )    ,				random.uniform(-0.5    ,				0.5 )    ,				A_    ,				A_    ,				size=0.05    ,				) )
        bodies.append(
            Body(
                random.uniform(-0.5    ,				0.5 )    ,				random.uniform(-0.5    ,				0.5 )    ,				-velocity_x    ,				-velocity_y    ,				size=0.05    ,				) )
    return BodySystem(A_    ,				0.01    ,				1_0    ,				0.1 )


if __name__ == "__main__":
       plot('Figure-8 solution to the 3-body-problem', example_a(), -2, 2, -2, 2)
       plot(
           'Moon\'s orbit around the earth',
           example_a(),
           -430000000,
           430000000,
           -430000000,
           430000000,
       )
       plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)







import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
       from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DPTImageProcessor
class    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__=2       , lowercase__=32       , lowercase__=16       , lowercase__=3       , lowercase__=True       , lowercase__=True       , lowercase__=32       , lowercase__=4       , lowercase__=[0, 1, 2, 3]       , lowercase__=4       , lowercase__=37       , lowercase__="gelu"       , lowercase__=0.1       , lowercase__=0.1       , lowercase__=0.02       , lowercase__=3       , lowercase__=False       , lowercase__=None       , )       -> Any:
        __UpperCAmelCase			   =     parent
        __UpperCAmelCase			   =     batch_size
        __UpperCAmelCase			   =     image_size
        __UpperCAmelCase			   =     patch_size
        __UpperCAmelCase			   =     num_channels
        __UpperCAmelCase			   =     is_training
        __UpperCAmelCase			   =     use_labels
        __UpperCAmelCase			   =     hidden_size
        __UpperCAmelCase			   =     num_hidden_layers
        __UpperCAmelCase			   =     backbone_out_indices
        __UpperCAmelCase			   =     num_attention_heads
        __UpperCAmelCase			   =     intermediate_size
        __UpperCAmelCase			   =     hidden_act
        __UpperCAmelCase			   =     hidden_dropout_prob
        __UpperCAmelCase			   =     attention_probs_dropout_prob
        __UpperCAmelCase			   =     initializer_range
        __UpperCAmelCase			   =     num_labels
        __UpperCAmelCase			   =     scope
        __UpperCAmelCase			   =     is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        __UpperCAmelCase			   =     (image_size // patch_size) ** 2
        __UpperCAmelCase			   =     num_patches + 1


    def        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        __UpperCAmelCase			   =     self.get_config()

        return config, pixel_values, labels


    def        lowerCAmelCase_  (self					)       -> str:
        return DPTConfig(
            image_size=self.image_size       , patch_size=self.patch_size       , num_channels=self.num_channels       , hidden_size=self.hidden_size       , num_hidden_layers=self.num_hidden_layers       , backbone_out_indices=self.backbone_out_indices       , num_attention_heads=self.num_attention_heads       , intermediate_size=self.intermediate_size       , hidden_act=self.hidden_act       , hidden_dropout_prob=self.hidden_dropout_prob       , attention_probs_dropout_prob=self.attention_probs_dropout_prob       , is_decoder=lowercase__       , initializer_range=self.initializer_range       , is_hybrid=self.is_hybrid       , )


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> Any:
        __UpperCAmelCase			   =     DPTModel(config=lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__					)
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> List[Any]:
        __UpperCAmelCase			   =     self.num_labels
        __UpperCAmelCase			   =     DPTForDepthEstimation(lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__					)
        self.parent.assertEqual(result.predicted_depth.shape       , (self.batch_size, self.image_size, self.image_size)					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> str:
        __UpperCAmelCase			   =     self.num_labels
        __UpperCAmelCase			   =     DPTForSemanticSegmentation(lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__       , labels=lowercase__					)
        self.parent.assertEqual(
            result.logits.shape       , (self.batch_size, self.num_labels, self.image_size, self.image_size)					)






    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase			   =     self.prepare_config_and_inputs()
        __UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase			   =     config_and_inputs
        __UpperCAmelCase			   =     {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class    A_				( _a	,       _a	,       unittest.TestCase  ):


    '''simple docstring'''






    a__       							=    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    a__       							=    (
        {
            "depth-estimation": DPTForDepthEstimation,
            "feature-extraction": DPTModel,
            "image-segmentation": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    a__       							=    False
    a__       							=    False
    a__       							=    False


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     DPTModelTester(self					)
        __UpperCAmelCase			   =     ConfigTester(self       , config_class=lowercase__       , has_text_modality=lowercase__       , hidden_size=37					)


    def        lowerCAmelCase_  (self					)       -> Dict:
        self.config_tester.run_common_tests()


    @unittest.skip(reason='''DPT does not use inputs_embeds'''					)
    def        lowerCAmelCase_  (self					)       -> int:
        pass


    def        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCAmelCase			   =     model_class(lowercase__					)
            self.assertIsInstance(model.get_input_embeddings()       , (nn.Module)					)
            __UpperCAmelCase			   =     model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowercase__       , nn.Linear					)					)


    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCAmelCase			   =     ['''pixel_values''']
            self.assertListEqual(arg_names[:1]       , lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Any:
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCAmelCase			   =     True

            if model_class in get_values(lowercase__					):
                continue

            __UpperCAmelCase			   =     model_class(lowercase__					)
            model.to(lowercase__					)
            model.train()
            __UpperCAmelCase			   =     self._prepare_for_class(lowercase__       , lowercase__       , return_labels=lowercase__					)
            __UpperCAmelCase			   =     model(**lowercase__					).loss
            loss.backward()


    def        lowerCAmelCase_  (self					)       -> Tuple:
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCAmelCase			   =     False
            __UpperCAmelCase			   =     True

            if model_class in get_values(lowercase__					) or not model_class.supports_gradient_checkpointing:
                continue
            __UpperCAmelCase			   =     model_class(lowercase__					)
            model.to(lowercase__					)
            model.gradient_checkpointing_enable()
            model.train()
            __UpperCAmelCase			   =     self._prepare_for_class(lowercase__       , lowercase__       , return_labels=lowercase__					)
            __UpperCAmelCase			   =     model(**lowercase__					).loss
            loss.backward()


    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase			   =     _config_zero_init(lowercase__					)
        for model_class in self.all_model_classes:
            __UpperCAmelCase			   =     model_class(config=lowercase__					)
            # Skip the check for the backbone
            __UpperCAmelCase			   =     []
            for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                    __UpperCAmelCase			   =     [F'''{name}.{key}''' for key in module.state_dict().keys()]
                    break

            for name, param in model.named_parameters():
                if param.requires_grad:
                    if name in backbone_params:
                        continue
                    self.assertIn(
                        ((param.data.mean() * 1E9).round() / 1E9).item()       , [0.0, 1.0]       , msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''       , )


    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
    def        lowerCAmelCase_  (self					)       -> int:
        pass






    @slow
    def        lowerCAmelCase_  (self					)       -> List[str]:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCAmelCase			   =     DPTModel.from_pretrained(lowercase__					)
            self.assertIsNotNone(lowercase__					)






def  __a    (				) ->       Any:






    '''simple docstring'''
    __UpperCAmelCase			   =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    return image
@require_torch
@require_vision
@slow
class    A_				( unittest.TestCase  ):


    '''simple docstring'''






    def        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large'''					)
        __UpperCAmelCase			   =     DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)
            __UpperCAmelCase			   =     outputs.predicted_depth

        # verify the predicted depth
        __UpperCAmelCase			   =     torch.Size((1, 384, 384)					)
        self.assertEqual(predicted_depth.shape       , lowercase__					)

        __UpperCAmelCase			   =     torch.tensor(
            [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(lowercase__					)

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


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
        __UpperCAmelCase			   =     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)

        # verify the logits
        __UpperCAmelCase			   =     torch.Size((1, 150, 480, 480)					)
        self.assertEqual(outputs.logits.shape       , lowercase__					)

        __UpperCAmelCase			   =     torch.tensor(
            [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(lowercase__					)

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






    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
        __UpperCAmelCase			   =     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)

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

        __UpperCAmelCase			   =     image_processor.post_process_semantic_segmentation(outputs=lowercase__       , target_sizes=[(500, 300)]					)
        __UpperCAmelCase			   =     torch.Size((500, 300)					)
        self.assertEqual(segmentation[0].shape       , lowercase__					)

        __UpperCAmelCase			   =     image_processor.post_process_semantic_segmentation(outputs=lowercase__					)
        __UpperCAmelCase			   =     torch.Size((480, 480)					)
        self.assertEqual(segmentation[0].shape       , lowercase__					)




