


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 							lowercase  :







         @staticmethod
         def 							lowercase__       (						*_lowercase :						Optional[Any]   ,   **_lowercase :						str      ):
               pass




def    a   (							A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		hashlib.mda(image.tobytes()							)
      return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							lowercase  (						unittest.TestCase			):
     lowerCamelCase			:			int      = MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						Tuple   ,   _lowercase :						Any   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		DepthEstimationPipeline(model=_lowercase   ,   image_processor=_lowercase      )
           return depth_estimator, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]







     def 							lowercase__       (						self :						Union[str, Any]   ,   _lowercase :						int   ,   _lowercase :						int      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
           self.assertEqual({'''predicted_depth''': ANY(torch.Tensor      ), '''depth''': ANY(Image.Image      )}   ,   _lowercase      )
           import datasets

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,   '''image'''   ,   split='''test'''      )
           SCREAMING_SNAKE_CASE__       :  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      )},
               ]   ,   _lowercase   ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''      )
     def 							lowercase__       (						self :						Optional[int]      ):
           pass







     @slow
     @require_torch
     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		'''Intel/dpt-large'''
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		pipeline('''depth-estimation'''   ,   model=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''      )
           SCREAMING_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 							lowercase__       (						self :						str      ):
           # 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 math import sqrt




def 			snake_case		(		lowerCAmelCase_ = 1000000	)						->				int:
  _snake_case   	=      0
  _snake_case   	=      0
  _snake_case   	=      42

  while num_cuboids <= limit:
    max_cuboid_size += 1
    for sum_shortest_sides in range(2  ,						2 * max_cuboid_size + 1	):
      if sqrt(sum_shortest_sides**2 + max_cuboid_size**2	).is_integer():
        num_cuboids += (
            min(lowerCAmelCase_  ,						sum_shortest_sides // 2	)
            - max(1  ,						sum_shortest_sides - max_cuboid_size	)
            + 1
        )

  return max_cuboid_size


if __name__ == "__main__":
     print(F"{solution() = }")





