


'''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      snake_case    :





       """simple docstring"""




       @staticmethod
       def   snake_case		(    *UpperCamelCase			,  **UpperCamelCase     ):







        """simple docstring"""
        pass





def __snake_case					(      UpperCAmelCase_						:						Image		):
 lowerCamelCase_      =  hashlib.mda(image.tobytes()		)
 return m.hexdigest()







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





  """simple docstring"""




  _lowerCamelCase		    =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  DepthEstimationPipeline(model=UpperCamelCase			,  image_processor=UpperCamelCase     )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







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

   lowerCamelCase_      =  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,  "image"			,  split="test"     )
   lowerCamelCase_      =  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     )},
       ]			,  UpperCamelCase			,  )






  @require_tf
  @unittest.skip("Depth estimation is not implemented in TF"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @slow
  @require_torch
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "Intel/dpt-large"
   lowerCamelCase_      =  pipeline("depth-estimation"			,  model=UpperCamelCase     )
   lowerCamelCase_      =  depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"     )
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """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'''





from __future__ import annotations





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : int | None = None					,    snake_case_       : int | None = None			)    ->      None:


     '''simple docstring'''




     if start is None:
          UpperCAmelCase_           =   0

     if end is None:
          UpperCAmelCase_           =   len(snake_case_			) - 1

     if start >= end:
          return

     UpperCAmelCase_           =   (start + end) // 2

     slowsort(snake_case_					,    snake_case_					,    snake_case_			)
     slowsort(snake_case_					,    mid + 1					,    snake_case_			)

     if sequence[end] < sequence[mid]:
          UpperCAmelCase_    , UpperCAmelCase_           =   sequence[mid], sequence[end]

     slowsort(snake_case_					,    snake_case_					,    end - 1			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()




