





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   A							:



           @staticmethod
           def 						lowerCAmelCase__     (						*_lowerCAmelCase:    Optional[Any]		,	**_lowerCAmelCase:    int							)    ->     Tuple:
              '''simple docstring'''

              pass




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =hashlib.mda(image.tobytes()	)
   return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   A							(						unittest.TestCase    ):
      _snake_case				  =MODEL_FOR_DEPTH_ESTIMATION_MAPPING



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    int							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =DepthEstimationPipeline(model=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    Any							)    ->     List[str]:
         '''simple docstring'''

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

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



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

         pass



      @slow
      @require_torch
      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           ="Intel/dpt-large"
         UpperCAmelCase_           =pipeline("depth-estimation"		,	model=_lowerCAmelCase							)
         UpperCAmelCase_           =depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"							)
         UpperCAmelCase_           =hashimage(outputs["depth"]							)

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()							)		,	29.3_04							)
         self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()							)		,	2.6_62							)






      @require_torch
      def 						lowerCAmelCase__     (						self:    str							)    ->     List[str]:
         '''simple docstring'''

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








def     lowercase	(    a = 50 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[1] * (length + 1)

		for row_length in range(3     , length + 1 ):
				for block_length in range(3     , row_length + 1 ):
						for block_start in range(row_length - block_length ):
								ways_number[row_length] += ways_number[
								    row_length - block_start - block_length - 1
								]

						ways_number[row_length] += 1

		return ways_number[length]


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






