


'''simple docstring'''






import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

a_       :     Optional[Any] 							=     pathlib.Path.cwd().joinpath("project_euler")
a_       :     List[str] 							=     pathlib.Path.cwd().joinpath(
    "scripts", "project_euler_answers.json"
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
 a_       :     Dict 							=     json.load(file_handle)






def 						_A					(lowerCAmelCase__	:Optional[Any]		) ->	ModuleType:




     '''simple docstring'''



     _a         =				importlib.util.spec_from_file_location(file_path.name					,    str(lowercase_		)		)
     _a         =				importlib.util.module_from_spec(lowercase_		)  # type: ignore
     spec.loader.exec_module(lowercase_		)  # type: ignore
     return module






def 						_A					() ->	list[pathlib.Path]:




     '''simple docstring'''



     _a         =				[]
     for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
          if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'		):
               continue
          for file_path in problem_dir_path.iterdir():
               if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')		):
                    continue
               solution_file_paths.append(lowercase_		)
     return solution_file_paths






def 						_A					() ->	str:




     '''simple docstring'''



     with open(os.environ['GITHUB_EVENT_PATH']		) as file:
          _a         =				json.load(lowercase_		)
     return event["pull_request"]["url"] + "/files"






def 						_A					() ->	list[pathlib.Path]:




     '''simple docstring'''



     _a         =				[]
     _a         =				{
         'Accept': 'application/vnd.github.v3+json',
         'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
     }
     _a         =				requests.get(get_files_url()					,    headers=lowercase_		).json()
     for file in files:
          _a         =				pathlib.Path.cwd().joinpath(file['filename']		)
          if (
              filepath.suffix != ".py"
              or filepath.name.startswith(('_', 'test')		)
              or not filepath.name.startswith('sol'		)
          ):
               continue
          solution_file_paths.append(lowercase_		)
     return solution_file_paths






def 						_A					() ->	list[pathlib.Path]:




     '''simple docstring'''



     if os.environ.get('CI'		) and os.environ.get('GITHUB_EVENT_NAME'		) == "pull_request":
          # Return only if there are any, otherwise default to all solutions
          if filepaths := added_solution_file_path():
               return filepaths
     return all_solution_file_paths()






@pytest.mark.parametrize(
    'solution_path'					,    collect_solution_file_paths()					,    ids=lambda lowerCAmelCase__		: f'{path.parent.name}/{path.name}'					,    )
def 						_A					(lowerCAmelCase__	:List[str]		) ->	None:




     '''simple docstring'''



     _a         =				solution_path.parent.name[8:].zfill(3		)
     _a         =				PROBLEM_ANSWERS[problem_number]
     _a         =				convert_path_to_module(lowercase_		)
     _a         =				str(solution_module.solution()		)  # type: ignore
     _a         =				hashlib.shaaaa(answer.encode()		).hexdigest()
     assert (
         answer == expected
     ), f'Expected solution to {problem_number} to have hash {expected}, got {answer}'







"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor




class       UpperCAmelCase_    (		unittest.TestCase				):




    def __init__(       self		:			Union[str, Any]      ,					snake_case_		:			Tuple      ,					snake_case_		:			Dict=7      ,					snake_case_		:			List[Any]=3      ,					snake_case_		:			Optional[int]=18      ,					snake_case_		:			Tuple=30      ,					snake_case_		:			Dict=400      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			Optional[int]=None      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			Optional[Any]=[0.5, 0.5, 0.5]      ,					snake_case_		:			Optional[int]=[0.5, 0.5, 0.5]      ,					) ->				List[Any]:




     '''simple docstring'''

     A__			=							size if size is not None else {"height": 18, "width": 18}
     A__			=							parent
     A__			=							batch_size
     A__			=							num_channels
     A__			=							image_size
     A__			=							min_resolution
     A__			=							max_resolution
     A__			=							do_resize
     A__			=							size
     A__			=							do_normalize
     A__			=							image_mean
     A__			=							image_std




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Dict:




     '''simple docstring'''

     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }






@require_torch
@require_vision
class       UpperCAmelCase_    (		A_,  unittest.TestCase				):
    lowercase__		   = ViTImageProcessor if is_vision_available() else None




    def 				__magic_name__				(       self		:			int ) ->				Any:




     '''simple docstring'''

     A__			=							ViTImageProcessingTester(self )




    @property
    def 				__magic_name__				(       self		:			Optional[Any] ) ->				str:




     '''simple docstring'''

     return self.image_processor_tester.prepare_image_processor_dict()




    def 				__magic_name__				(       self		:			Tuple ) ->				str:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(snake_case_      ,					"image_mean" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_std" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_normalize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_resize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"size" ) )




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				List[str]:




     '''simple docstring'''

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size      ,					{"height": 18, "width": 18} )

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict      ,					size=42 )
     self.assertEqual(image_processor.size      ,					{"height": 42, "width": 42} )




    def 				__magic_name__				(       self		:			Any ) ->				Optional[Any]:




     '''simple docstring'''

     pass




    def 				__magic_name__				(       self		:			Optional[int] ) ->				Any:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     A__			=							prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					Image.Image )

     # Test not batched input
     A__			=							image_processing(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)

     # Test batched
     A__			=							image_processing(snake_case_      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)




    def 				__magic_name__				(       self		:			str ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     A__			=							prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_      ,					numpify=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					np.ndarray )

     # Test not batched input
     A__			=							image_processing(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)

     # Test batched
     A__			=							image_processing(snake_case_      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)




    def 				__magic_name__				(       self		:			Dict ) ->				Union[str, Any]:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     A__			=							prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_      ,					torchify=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					torch.Tensor )

     # Test not batched input
     A__			=							image_processing(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)

     # Test batched
     A__			=							image_processing(snake_case_      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)





