
class 				lowerCamelCase__							:




 '''simple docstring'''




 def __init__(      self	:Optional[Any]		)	->				List[Any]:
        __UpperCamelCase			:     Any         =      {}  # Mapping from char to TrieNode
        __UpperCamelCase			:     List[Any]         =      False







 def      _lowerCamelCase       (      self	:int  ,     a	:List[str]		)	->				List[str]:
        for word in words:
               self.insert(UpperCAmelCase_		)







 def      _lowerCamelCase       (      self	:int  ,     a	:Dict		)	->				Optional[Any]:
        __UpperCamelCase			:     Any         =      self
        for char in word:
               if char not in curr.nodes:
                      __UpperCamelCase			:     Any         =      TrieNode()
               __UpperCamelCase			:     Optional[int]         =      curr.nodes[char]
        __UpperCamelCase			:     Optional[int]         =      True







 def      _lowerCamelCase       (      self	:Any  ,     a	:List[str]		)	->				Dict:
        __UpperCamelCase			:     Optional[int]         =      self
        for char in word:
               if char not in curr.nodes:
                      return False
               __UpperCamelCase			:     Tuple         =      curr.nodes[char]
        return curr.is_leaf







 def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Tuple		)	->				List[Any]:

        def _delete(a	:Tuple  ,     a	:Union[str, Any]  ,     a	:int		) -> bool:
               if index == len(UpperCAmelCase_		):
                      # If word does not exist
                      if not curr.is_leaf:
                             return False
                      __UpperCamelCase			:     Optional[Any]         =      False
                      return len(curr.nodes		) == 0
               __UpperCamelCase			:     Union[str, Any]         =      word[index]
               __UpperCamelCase			:     Optional[int]         =      curr.nodes.get(UpperCAmelCase_		)
               # If char not in current trie node
               if not char_node:
                      return False
               # Flag to check if node can be deleted
               __UpperCamelCase			:     Tuple         =      _delete(UpperCAmelCase_  ,     UpperCAmelCase_  ,     index + 1		)
               if delete_curr:
                      del curr.nodes[char]
                      return len(curr.nodes		) == 0
               return delete_curr

        _delete(self  ,     UpperCAmelCase_  ,     0		)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Optional[Any])      ->					str:






       '''simple docstring'''





       if node.is_leaf:
              print(_snake_case		,						end=" ")

       for key, value in node.nodes.items():
              print_words(_snake_case		,						word + key)




def 			_SCREAMING_SNAKE_CASE      (	)      ->					List[Any]:






       '''simple docstring'''





       __UpperCamelCase			:     Optional[int]         =      "banana bananas bandana band apple all beast".split()
       __UpperCamelCase			:     Union[str, Any]         =      TrieNode()
       root.insert_many(_snake_case)
       # print_words(root, "")
       assert all(root.find(_snake_case) for word in words)
       assert root.find("banana")
       assert not root.find("bandanas")
       assert not root.find("apps")
       assert root.find("apple")
       assert root.find("all")
       root.delete("all")
       assert not root.find("all")
       root.delete("banana")
       assert not root.find("banana")
       assert root.find("bananas")
       return True




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Any)      ->					Tuple:






       '''simple docstring'''





       print(str(_snake_case)		,						"works!" if passes else "doesn\'t work :(")




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Dict:






       '''simple docstring'''





       assert test_trie()




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[Any]:






       '''simple docstring'''





       print_results("Testing trie functionality"		,						test_trie())


if __name__ == "__main__":
  main()

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 				lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




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




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







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







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     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		)},
								    ]  ,     a  ,     )







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







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      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      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)