





'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image
else:


					class 					__a      :

												@staticmethod
												def     UpperCAmelCase__	(							*__magic_name__							:     Any       ,     **__magic_name__							:     Dict   )					->       Dict:







																		"""simple docstring"""
																		pass





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Image   ) ->	str:
						UpperCAmelCase_			:      int     						=  hashlib.mda(image.tobytes()   )
						return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__a      (unittest.TestCase     ):
							__a					:   str									=       MODEL_FOR_DEPTH_ESTIMATION_MAPPING

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Any       ,     __magic_name__							:     List[Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  DepthEstimationPipeline(model=__magic_name__       ,     image_processor=__magic_name__   )
													return depth_estimator, [
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													]

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Tuple   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
													self.assertEqual({'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )}       ,     __magic_name__   )
													import datasets

													UpperCAmelCase_			:      int     						=  datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''       ,     '''image'''       ,     split='''test'''   )
													UpperCAmelCase_			:      List[str]     						=  depth_estimator(
													    [
													        Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   ),
													        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
													        # RGBA
													        dataset[0]['''file'''],
													        # LA
													        dataset[1]['''file'''],
													        # L
													        dataset[2]['''file'''],
													    ]   )
													self.assertEqual(
													    [
													        {'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )},
													        {'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )},
													        {'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )},
													        {'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )},
													        {'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )},
													    ]       ,     __magic_name__       ,     )

							@require_tf
							@unittest.skip('''Depth estimation is not implemented in TF'''   )
							def     UpperCAmelCase__	(							self							:     Tuple   )					->       str:







													"""simple docstring"""
													pass

							@slow
							@require_torch
							def     UpperCAmelCase__	(							self							:     int   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  '''Intel/dpt-large'''
													UpperCAmelCase_			:      Tuple     						=  pipeline('''depth-estimation'''       ,     model=__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''   )
													UpperCAmelCase_			:      int     						=  hashimage(outputs['''depth''']   )

													# This seems flaky.
													# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
													self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()   )       ,     2_9.3_0_4   )
													self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()   )       ,     2.6_6_2   )



							@require_torch
							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Tuple:







													"""simple docstring"""
													# This is highly irregular to have no small tests.
													self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''   )












'''simple docstring'''







import sys
import turtle





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float]   ) ->	tuple[float, float]:
						return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			int,			) ->	None:
						my_pen.up()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.down()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )

						if depth == 0:
												return

						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )


if __name__ == "__main__":
					if len(sys.argv) != 2:
										raise ValueError(
										    "Correct format for using this script: "
										    "python fractals.py <int:depth_for_fractal>"
										)
					snake_case_     :       Any						=  turtle.Turtle()
					my_pen.ht()
					my_pen.speed(5)
					my_pen.pencolor("red")

					snake_case_     :       Tuple						=  [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
					triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))





