



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_						:
           @staticmethod
           def    snake_case_			(     *SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						Any:
              pass
def 						UpperCamelCase      (  snake_case__			:       Image      )      ->	Tuple:
   UpperCamelCase       :    str		      =      hashlib.mda(image.tobytes()      )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							lowerCAmelCase_						(    unittest.TestCase				):
       UpperCAmelCase__							:   List[Any]  	=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    Any		      =      DepthEstimationPipeline(model=__lowerCamelCase,     image_processor=__lowerCamelCase			)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    Union[str, Any]		      =      depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor			), 'depth': ANY(Image.Image			)},     __lowerCamelCase			)
          import datasets

          UpperCamelCase       :    str		      =      datasets.load_dataset('hf-internal-testing/fixtures_image_utils',     'image',     split='test'			)
          UpperCamelCase       :    List[str]		      =      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    snake_case_			(     self			)	->						Optional[int]:
          pass
       @slow
       @require_torch
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Tuple		      =      '''Intel/dpt-large'''
          UpperCamelCase       :    str		      =      pipeline('depth-estimation',     model=__lowerCamelCase			)
          UpperCamelCase       :    str		      =      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()			),     29.3_04			)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()			),     2.6_62			)






       @require_torch
       def    snake_case_			(     self			)	->						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'			)







'''simple docstring'''





class 				UpperCAmelCase			:


 def __init__(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:dict[str, TrieNode]      =      {}  # Mapping from char to TrieNode
    UpperCAmelCase__	:Union[str, Any]      =      False


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	list[str] ):
    for word in words:
       self.insert(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Tuple      =      self
    for char in word:
       if char not in curr.nodes:
          UpperCAmelCase__	:Optional[int]      =      TrieNode()
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    UpperCAmelCase__	:Optional[int]      =      True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:str      =      self
    for char in word:
       if char not in curr.nodes:
          return False
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    return curr.is_leaf





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):

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

    _delete(self			,       __lowerCamelCase			,       0 )


def        a__     (					UpperCamelCase_						:      TrieNode,   UpperCamelCase_						:      str						):
   if node.is_leaf:
      print(UpperCamelCase_,   end=''' '''						)

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


def        a__     (					):
   UpperCAmelCase__	:Union[str, Any]      =      '''banana bananas bandana band apple all beast'''.split()
   UpperCAmelCase__	:Union[str, Any]      =      TrieNode()
   root.insert_many(UpperCamelCase_						)
   # print_words(root, "")
   assert all(root.find(UpperCamelCase_						) 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        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      bool						):
   print(str(UpperCamelCase_						),   '''works!''' if passes else '''doesn\'t work :('''						)


def        a__     (					):
   assert test_trie()


def        a__     (					):
   print_results('''Testing trie functionality''',   test_trie()						)


if __name__ == "__main__":
    main()



