





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_				:







             """simple docstring"""





             @staticmethod
             def    __UpperCAmelCase (    *__A	:	Optional[int]						,**__A	:	Optional[int]  )						->					str:
               pass

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Image     )      ->						str:
  _lowercase							= hashlib.mda(image.tobytes()     )
  return m.hexdigest()







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







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	str						,__A	:	List[str]						,__A	:	Dict  )						->					Dict:
         _lowercase							= DepthEstimationPipeline(model=__A						,image_processor=__A  )
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]







       def    __UpperCAmelCase (    self	:	int						,__A	:	Any						,__A	:	List[str]  )						->					Dict:
         _lowercase							= depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
         self.assertEqual({'predicted_depth': ANY(torch.Tensor  ), 'depth': ANY(Image.Image  )}						,__A  )
         import datasets

         _lowercase							= datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,'image'						,split='test'  )
         _lowercase							= 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  )},
             ]						,__A						,)







       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'  )
       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         pass







       @slow
       @require_torch
       def    __UpperCAmelCase (    self	:	Dict  )						->					str:
         _lowercase							= 'Intel/dpt-large'
         _lowercase							= pipeline('depth-estimation'						,model=__A  )
         _lowercase							= depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'  )
         _lowercase							= 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	:	List[str]  )						->					Any:
         # 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 ....utils import logging


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Union[str, Any]						,__A	:	int						,__A	:	List[Any]=None						,__A	:	List[str]=2048  )						->					List[Any]:
         _lowercase							= config.__dict__
         _lowercase							= modal_hidden_size
         if num_labels:
           _lowercase							= num_labels