


"""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 	lowercase__				:





    @staticmethod
    def 		UpperCamelCase_  (							*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
       pass

def 				_snake_case      (   __snake_case							:      Image   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		hashlib.mda(image.tobytes()   )
   return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	lowercase__				(					unittest.TestCase				):
   __UpperCAmelCase							       =					MODEL_FOR_DEPTH_ESTIMATION_MAPPING





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      _lowerCamelCase	:					int   =		DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Tuple   =		depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
      self.assertEqual({"""predicted_depth""": ANY(torch.Tensor), """depth""": ANY(Image.Image)}      ,   SCREAMING_SNAKE_CASE)
      import datasets

      _lowerCamelCase	:					str   =		datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""      ,   """image"""      ,   split="""test""")
      _lowerCamelCase	:					int   =		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)},
          ]      ,   SCREAMING_SNAKE_CASE      ,   )





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





   @slow
   @require_torch
   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Dict   =		"""Intel/dpt-large"""
      _lowerCamelCase	:					str   =		pipeline("""depth-estimation"""      ,   model=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg""")
      _lowerCamelCase	:					Any   =		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 		UpperCamelCase_  (							self)  ->  List[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""")





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      list[int]   ):




   """simple docstring"""







   if graph[path[curr_ind - 1]][next_ver] == 0:
      return False

   # 2. Validate that next vertex is not already in path
   return not any(vertex == next_ver for vertex in path   )

def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      list[int]						, __snake_case							:      int   ):




   """simple docstring"""







   if curr_ind == len(__snake_case   ):
      # return whether path exists between current and starting vertices
      return graph[path[curr_ind - 1]][path[0]] == 1

   # Recursive Step
   for next_ver in range(0						, len(__snake_case   )   ):
      if valid_connection(__snake_case						, __snake_case						, __snake_case						, __snake_case   ):
         # Insert current vertex  into path as next transition
         _lowerCamelCase	:					List[str]   =		next_ver
         # Validate created path
         if util_hamilton_cycle(__snake_case						, __snake_case						, curr_ind + 1   ):
            return True
         # Backtrack
         _lowerCamelCase	:					Tuple   =		-1
   return False





def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int = 0   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		[-1] * (len(__snake_case   ) + 1)
   # initialize start and end of path with starting index
   _lowerCamelCase	:					Optional[int]   =		start_index
   # evaluate and if we find answer return path either return empty array
   return path if util_hamilton_cycle(__snake_case						, __snake_case						, 1   ) else []

