




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        a__				(       *A_      :						Any		,							**A_      :						str						)     ->			Tuple:

              """simple docstring"""






              pass

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Image  ):


      '''simple docstring'''



      lowerCamelCase_	  =					hashlib.mda(image.tobytes()  )
      return m.hexdigest()







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



 '''simple docstring'''


 UpperCamelCase		     =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING

 def        a__				(       self      :						Optional[int]		,							A_      :						Union[str, Any]		,							A_      :						Any		,							A_      :						Any						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					DepthEstimationPipeline(model=A_		,							image_processor=A_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]

 def        a__				(       self      :						List[Any]		,							A_      :						List[str]		,							A_      :						List[Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}		,							A_						)
       import datasets

       lowerCamelCase_	  =					datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,							'image'		,							split='test'						)
       lowerCamelCase_	  =					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        a__				(       self      :						str						)     ->			Union[str, Any]:

       """simple docstring"""






       pass

 @slow
 @require_torch
 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					'Intel/dpt-large'
       lowerCamelCase_	  =					pipeline('depth-estimation'		,							model=A_						)
       lowerCamelCase_	  =					depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       lowerCamelCase_	  =					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        a__				(       self      :						Dict						)     ->			List[str]:

       """simple docstring"""






       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(lowercase  )  )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(lowercase  )
            elif i == ")":
                  # RULE 4
                  lowerCamelCase_	  =					operator_stack.peek()
                  operator_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
 lowerCamelCase       :	Any									=     "(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



