


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      UpperCamelCase					:


      '''simple docstring'''







      @staticmethod
      def 		UpperCamelCase  (     *UpperCamelCase_   ,							**UpperCamelCase_		):
           pass




def     UpperCamelCase    (      _a		)					->							str:
     '''simple docstring'''





     lowercase_       :List[Any]     =       hashlib.mda(image.tobytes()		)
     return m.hexdigest()






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


   '''simple docstring'''







   lowercase					:	Union[str, Any]									=MODEL_FOR_DEPTH_ESTIMATION_MAPPING
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Optional[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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Optional[int]     =       depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)}   ,							__UpperCamelCase		)
        import datasets

        lowercase_       :List[str]     =       datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,							'''image'''   ,							split='''test'''		)
        lowercase_       :Tuple     =       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 		UpperCamelCase  (     self		):
        pass
   @slow
   @require_torch
   def 		UpperCamelCase  (     self		):
        lowercase_       :Tuple     =       '''Intel/dpt-large'''
        lowercase_       :Tuple     =       pipeline('''depth-estimation'''   ,							model=__UpperCamelCase		)
        lowercase_       :List[Any]     =       depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''		)
        lowercase_       :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 		UpperCamelCase  (     self		):
        # This is highly irregular to have no small tests.
        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''		)





from __future__ import annotations




def     UpperCamelCase    (      _a       ,		_a = None       ,		_a = None		)					->							None:
     '''simple docstring'''





     if start is None:
          lowercase_       :str     =       0

     if end is None:
          lowercase_       :str     =       len(_a		) - 1

     if start >= end:
          return

     lowercase_       :Dict     =       (start + end) // 2

     slowsort(_a       ,		_a       ,		_a		)
     slowsort(_a       ,		mid + 1       ,		_a		)

     if sequence[end] < sequence[mid]:
          lowercase_    ,      lowercase_       :List[Any]     =       sequence[mid], sequence[end]

     slowsort(_a       ,		_a       ,		end - 1		)


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

   testmod()

