






'''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 							_UpperCAmelCase    :




     @staticmethod
     def 					lowerCAmelCase__					(      *a	:							int	,  **a	:							Tuple      ):






        '''simple docstring'''






        pass




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




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





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							_UpperCAmelCase    (		unittest.TestCase      ):
    __lowerCamelCase: List[str]									=     MODEL_FOR_DEPTH_ESTIMATION_MAPPING




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							Dict	,  a	:							Dict	,  a	:							str      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  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	:							int	,  a	:							Any	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}	,  a      )
       import datasets

       lowercase_						:     List[Any]							     =  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"	,  "image"	,  split="test"      )
       lowercase_						:     Optional[int]							     =  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]      ):






       '''simple docstring'''






       pass




    @slow
    @require_torch
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  "Intel/dpt-large"
       lowercase_						:     Union[str, Any]							     =  pipeline("depth-estimation"	,  model=a      )
       lowercase_						:     Union[str, Any]							     =  depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"      )
       lowercase_						:     List[str]							     =  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	:							Tuple      ):






       '''simple docstring'''






       self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )












'''simple docstring'''
UpperCamelCase__			=      {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowercase_						:     Union[str, Any]							     =  (
          F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
          F"""Valid values are: {", ".join(_UpperCamelCase				)}"""
      )
      raise ValueError(_UpperCamelCase				)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




