
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  lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def    _lowerCamelCase				(					*a		:Optional[Any]     ,   **a		:int						)		-> Dict:
										pass




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Image)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	_A								=     MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[str]     ,   a		:Union[str, Any]     ,   a		:Union[str, Any]						)		-> Tuple:
								__UpperCamelCase	:   List[Any]			   =					DepthEstimationPipeline(model=a     ,   image_processor=a						)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:int     ,   a		:str						)		-> Any:
								__UpperCamelCase	:   int			   =					depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor						), "depth": ANY(Image.Image						)}     ,   a						)
								import datasets

								__UpperCamelCase	:   List[Any]			   =					datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     ,   "image"     ,   split="test"						)
								__UpperCamelCase	:   Dict			   =					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						)},
								    ]     ,   a     ,   )







	@require_tf
	@unittest.skip("Depth estimation is not implemented in TF"						)
	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								pass







	@slow
	@require_torch
	def    _lowerCamelCase				(					self		:List[str]						)		-> Tuple:
								__UpperCamelCase	:   List[str]			   =					"Intel/dpt-large"
								__UpperCamelCase	:   List[str]			   =					pipeline("depth-estimation"     ,   model=a						)
								__UpperCamelCase	:   str			   =					depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"						)
								__UpperCamelCase	:   Optional[Any]			   =					hashimage(outputs["depth"]						)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()						)     ,   29.304						)
								self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()						)     ,   2.662						)







	@require_torch
	def    _lowerCamelCase				(					self		:int						)		-> Tuple:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"						)

from __future__ import annotations

import math

lowercase  :					Any													=       '2020.9.26'
lowercase  :					Union[str, Any]													=       'xcodz-dot, cclaus, dhruvmanila'




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float)					->    tuple[float, float]:






							'''simple docstring'''





							if not all(isinstance(_lowerCamelCase						,  (float, int)) for val in locals().values()):
														__UpperCamelCase	:   str			   =					F'Input values must either be float or int: {list(locals().values())}'
														raise TypeError(_lowerCamelCase)
							__UpperCamelCase	:   List[str]			   =					((x * distance) / (z + distance)) * scale
							__UpperCamelCase	:   List[Any]			   =					((y * distance) / (z + distance)) * scale
							return projected_x, projected_y




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			str						,  _lowerCamelCase :			float)					->    tuple[float, float, float]:






							'''simple docstring'''





							if not isinstance(_lowerCamelCase						,  _lowerCamelCase):
														raise TypeError("Axis must be a str")
							__UpperCamelCase	:   str			   =					locals()
							del input_variables["axis"]
							if not all(isinstance(_lowerCamelCase						,  (float, int)) for val in input_variables.values()):
														__UpperCamelCase	:   Dict			   =					(
														    "Input values except axis must either be float or int: "
														    F'{list(input_variables.values())}'
														)
														raise TypeError(_lowerCamelCase)
							__UpperCamelCase	:   Optional[Any]			   =					(angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__UpperCamelCase	:   Tuple			   =					x * math.cos(_lowerCamelCase) - y * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Union[str, Any]			   =					y * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z
							elif axis == "x":
														__UpperCamelCase	:   Dict			   =					y * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z * math.cos(_lowerCamelCase) + y * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   List[str]			   =					x
							elif axis == "y":
														__UpperCamelCase	:   Any			   =					x * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Dict			   =					y
							else:
														raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")

							return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }")
		print(f"{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }")