


"""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 _lowerCAmelCase				:







     @staticmethod
     def  lowerCamelCase		(	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Tuple:

         '''simple docstring'''




         pass







def   __lowerCAmelCase						(						lowercase  :  Image )	->						str:


    """simple docstring"""





    snake_case    :      str      					=    hashlib.mda(image.tobytes() )
    return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowerCAmelCase				(      unittest.TestCase	):
   __UpperCAmelCase :       Any							  =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    DepthEstimationPipeline(model=UpperCamelCase__						,	image_processor=UpperCamelCase__						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




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

       snake_case    :      int      					=    datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,	"image"						,	split="test"						)
       snake_case    :      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						)},
           ]						,	UpperCamelCase__						,	)







   @require_tf
   @unittest.skip("Depth estimation is not implemented in TF"						)
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       pass







   @slow
   @require_torch
   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    "Intel/dpt-large"
       snake_case    :      Union[str, Any]      					=    pipeline("depth-estimation"						,	model=UpperCamelCase__						)
       snake_case    :      Optional[Any]      					=    depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"						)
       snake_case    :      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						)	->		Optional[int]:

       '''simple docstring'''




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









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  list )	->						int:


    """simple docstring"""





    if not grid or not grid[0]:
        raise TypeError("The grid does not contain the appropriate information" )

    for cell_n in range(1						,							len(grid[0] ) ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    snake_case    :      int      					=    grid[0]

    for row_n in range(1						,							len(lowercase ) ):
        snake_case    :      Optional[Any]      					=    grid[row_n]
        snake_case    :      List[str]      					=    fill_row(lowercase						,							lowercase )
        snake_case    :      Optional[int]      					=    grid[row_n]

    return grid[-1][-1]







def   __lowerCAmelCase						(						lowercase  :  list						,							lowercase  :  list )	->						list:


    """simple docstring"""





    current_row[0] += row_above[0]
    for cell_n in range(1						,							len(lowercase ) ):
        current_row[cell_n] += min(current_row[cell_n - 1]						,							row_above[cell_n] )

    return current_row


if __name__ == "__main__":
  import doctest

  doctest.testmod()





