



"""simple docstring"""






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    :



        @staticmethod
        def    __UpperCAmelCase    (       *__lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Optional[Any]				):



          """simple docstring"""
          pass




def 			snake_case		(		lowerCAmelCase_	)						->				str:
  _snake_case   	=      hashlib.mda(image.tobytes()	)
  return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	List[str]           =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      DepthEstimationPipeline(model=__lowerCamelCase							,       image_processor=__lowerCamelCase				)
     return depth_estimator, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     self.assertEqual({'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)}							,       __lowerCamelCase				)
     import datasets

     _snake_case   	=      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''							,       '''image'''							,       split='''test'''				)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     pass



   @slow
   @require_torch
   def    __UpperCAmelCase    (       self    :	List[Any]				):



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

     # This seems flaky.
     # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
     self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()				)							,       2_9.3_0_4				)
     self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()				)							,       2.6_6_2				)





   @require_torch
   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # 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"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      root / filename
      for filename in [
          '''vision.cpp''',
          os.path.join('''cpu'''  ,						'''ms_deform_attn_cpu.cpp'''	),
          os.path.join('''cuda'''  ,						'''ms_deform_attn_cuda.cu'''	),
      ]
  ]

  load(
      '''MultiScaleDeformableAttention'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						extra_cflags=['''-DWITH_CUDA=1''']  ,						extra_cuda_cflags=[
          '''-DCUDA_HAS_FP16=1''',
          '''-D__CUDA_NO_HALF_OPERATORS__''',
          '''-D__CUDA_NO_HALF_CONVERSIONS__''',
          '''-D__CUDA_NO_HALF2_OPERATORS__''',
      ]  ,						)

  import MultiScaleDeformableAttention as MSDA

  return MSDA





