
'''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 MobileNetVaImageProcessor

class   _snake_case( unittest.TestCase			):






 def __init__(self					:     Optional[Any]						,		a					:     str						,		a					:     Tuple=7						,		a					:     Dict=3						,		a					:     Tuple=18						,		a					:     int=30						,		a					:     Tuple=4_00						,		a					:     Tuple=True						,		a					:     Dict=None						,		a					:     Tuple=True						,		a					:     List[str]=None						,		)					->      Union[str, Any]:






    """simple docstring"""


    A__         =    size if size is not None else {"shortest_edge": 20}
    A__         =    crop_size if crop_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_center_crop
    A__         =    crop_size






 def 	_UpperCamelCase   (self					:     str						)					->      str:






    """simple docstring"""


    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class   _snake_case( _a					,   unittest.TestCase			):
 __snake_case:       str									=				MobileNetVaImageProcessor if is_vision_available() else None






 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Tuple:






    """simple docstring"""


    A__         =    MobileNetVaImageProcessingTester(self						)






 @property
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      List[Any]:






    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      int:






    """simple docstring"""


    A__         =    self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__						,		'do_resize'						)						)
    self.assertTrue(hasattr(lowerCamelCase__						,		'size'						)						)
    self.assertTrue(hasattr(lowerCamelCase__						,		'do_center_crop'						)						)
    self.assertTrue(hasattr(lowerCamelCase__						,		'center_crop'						)						)






 def 	_UpperCamelCase   (self					:     List[Any]						)					->      List[str]:






    """simple docstring"""


    A__         =    self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size						,		{'shortest_edge': 20}						)
    self.assertEqual(image_processor.crop_size						,		{'height': 18, 'width': 18}						)

    A__         =    self.image_processing_class.from_dict(self.image_processor_dict						,		size=42						,		crop_size=84						)
    self.assertEqual(image_processor.size						,		{'shortest_edge': 42}						)
    self.assertEqual(image_processor.crop_size						,		{'height': 84, 'width': 84}						)






 def 	_UpperCamelCase   (self					:     Any						)					->      str:






    """simple docstring"""


    pass






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






    """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=lowerCamelCase__						)
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase__						,		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.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )						,		)

    # Test batched
    A__         =    image_processing(lowerCamelCase__						,		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.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )						,		)






 def 	_UpperCamelCase   (self					:     Dict						)					->      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=lowerCamelCase__						,		numpify=lowerCamelCase__						)
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase__						,		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.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )						,		)

    # Test batched
    A__         =    image_processing(lowerCamelCase__						,		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.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )						,		)






 def 	_UpperCamelCase   (self					:     str						)					->      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=lowerCamelCase__						,		torchify=lowerCamelCase__						)
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase__						,		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.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )						,		)

    # Test batched
    A__         =    image_processing(lowerCamelCase__						,		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.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )						,		)








'''simple docstring'''







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

import pytest
import requests

lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath('''project_euler''')
lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

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







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







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







def 	_A			(   ):






   '''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(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''simple docstring'''







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







def 	_A			(   ):






   '''simple docstring'''







   A__         =    []
   A__         =    {
       'Accept': 'application/vnd.github.v3+json',
       'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
   }
   A__         =    requests.get(get_files_url()			,headers=UpperCAmelCase	).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(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''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 UpperCAmelCase	: F"""{path.parent.name}/{path.name}"""			,)
def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    solution_path.parent.name[8:].zfill(3	)
   A__         =    PROBLEM_ANSWERS[problem_number]
   A__         =    convert_path_to_module(UpperCAmelCase	)
   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}"""


