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 OwlViTImageProcessor







class __snake_case							(		unittest.TestCase						):




   def __init__(       self     :							int			,      _lowercase     :							Optional[Any]			,      _lowercase     :							List[str]=7			,      _lowercase     :							str=3			,      _lowercase     :							str=18			,      _lowercase     :							Union[str, Any]=30			,      _lowercase     :							str=4_00			,      _lowercase     :							List[Any]=True			,      _lowercase     :							Union[str, Any]=None			,      _lowercase     :							Union[str, Any]=True			,      _lowercase     :							Dict=None			,      _lowercase     :							Tuple=True			,      _lowercase     :							Optional[int]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,      _lowercase     :							List[Any]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,      _lowercase     :							List[str]=True			,      ):



     """simple docstring"""






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




   def       __a		(       self     :							int ):



     """simple docstring"""






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







@require_torch
@require_vision
class __snake_case							(		lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				OwlViTImageProcessor if is_vision_available() else None




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= OwlViTImageProcessingTester(self )




   @property
   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(_lowercase			,      """do_resize""" ) )
     self.assertTrue(hasattr(_lowercase			,      """size""" ) )
     self.assertTrue(hasattr(_lowercase			,      """do_center_crop""" ) )
     self.assertTrue(hasattr(_lowercase			,      """center_crop""" ) )
     self.assertTrue(hasattr(_lowercase			,      """do_normalize""" ) )
     self.assertTrue(hasattr(_lowercase			,      """image_mean""" ) )
     self.assertTrue(hasattr(_lowercase			,      """image_std""" ) )
     self.assertTrue(hasattr(_lowercase			,      """do_convert_rgb""" ) )




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






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

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




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     SCREAMING_SNAKE_CASE__     				= prepare_image_inputs(self.image_processor_tester			,      equal_resolution=_lowercase )

     for image in image_inputs:
       self.assertIsInstance(_lowercase			,      Image.Image )

     # Test not batched input
     SCREAMING_SNAKE_CASE__     				= 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
     SCREAMING_SNAKE_CASE__     				= image_processing(_lowercase			,      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       __a		(       self     :							Optional[int] ):



     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE__     				= 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
     SCREAMING_SNAKE_CASE__     				= image_processing(_lowercase			,      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       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE__     				= 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
     SCREAMING_SNAKE_CASE__     				= image_processing(_lowercase			,      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"""],
         )			,      )

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

import pytest
import requests

__lowerCamelCase     :					Optional[Any]				  =						pathlib.Path.cwd().joinpath('''project_euler''')
__lowerCamelCase     :					Dict				  =						pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
   __lowerCamelCase     :					dict[str, str]				  =						json.load(file_handle)



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   ModuleType:







  """simple docstring"""





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



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  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       __SCREAMING_SNAKE_CASE    (   )				->   str:







  """simple docstring"""





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



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  SCREAMING_SNAKE_CASE__     				= {
      """Accept""": """application/vnd.github.v3+json""",
      """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
  }
  SCREAMING_SNAKE_CASE__     				= requests.get(get_files_url()					,	headers=__UpperCamelCase      ).json()
  for file in files:
    SCREAMING_SNAKE_CASE__     				= 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       __SCREAMING_SNAKE_CASE    (   )				->   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 __UpperCamelCase      : f"""{path.parent.name}/{path.name}"""					,	)
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   None:







  """simple docstring"""





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