





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_    :






        @staticmethod
        def 				UpperCAmelCase__						(							*_snake_case			:					Any       ,  **_snake_case			:					Optional[Any]      )->Union[str, Any]:






         '''simple docstring'''

         pass






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Image   )      ->   str:
 __lowerCAmelCase					:	int      =       hashlib.mda(image.tobytes()   )
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        snake_case_    (		unittest.TestCase						):
  A_      							=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Any       ,  _snake_case			:					List[Any]      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       DepthEstimationPipeline(model=_snake_case       ,  image_processor=_snake_case      )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  _snake_case			:					List[Any]       ,  _snake_case			:					int      )->Dict:






   '''simple docstring'''

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

   __lowerCAmelCase					:	Any      =       datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,  """image"""       ,  split="""test"""      )
   __lowerCAmelCase					:	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      )},
       ]       ,  _snake_case       ,  )






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






   '''simple docstring'''

   pass






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






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       """Intel/dpt-large"""
   __lowerCAmelCase					:	Any      =       pipeline("""depth-estimation"""       ,  model=_snake_case      )
   __lowerCAmelCase					:	List[str]      =       depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""      )
   __lowerCAmelCase					:	Optional[int]      =       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 				UpperCAmelCase__						(							self			:					Tuple      )->Any:






   '''simple docstring'''

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[int]   )      ->   int:
 if not numbers:
  return 0

 if not isinstance(SCREAMING_SNAKE_CASE   ,   (list, tuple)   ) or not all(
     isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ) for number in numbers   ):
  raise ValueError("""numbers must be an iterable of integers"""   )

 __lowerCAmelCase					:	int      =       numbers[0]

 for i in range(1   ,   len(SCREAMING_SNAKE_CASE   )   ):
  # update the maximum and minimum subarray products
  __lowerCAmelCase					:	List[str]      =       numbers[i]
  if number < 0:
   __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       min_till_now, max_till_now
  __lowerCAmelCase					:	Optional[int]      =       max(SCREAMING_SNAKE_CASE   ,   max_till_now * number   )
  __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   min_till_now * number   )

  # update the maximum product found till now
  __lowerCAmelCase					:	List[str]      =       max(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 return max_prod