






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

import pytest
import requests

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

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


def     _a					(       UpperCamelCase_   :			Optional[Any]				)	->				Any:
						"""simple docstring"""







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


def     _a					(       )	->				Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__	 =  []
						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(A_				)
						return solution_file_paths


def     _a					(       )	->				Any:
						"""simple docstring"""







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


def     _a					(       )	->				Dict:
						"""simple docstring"""







						lowerCAmelCase__	 =  []
						lowerCAmelCase__	 =  {
						    "Accept": "application/vnd.github.v3+json",
						    "Authorization": "token " + os.environ["GITHUB_TOKEN"],
						}
						lowerCAmelCase__	 =  requests.get(get_files_url()      ,				headers=A_				).json()
						for file in files:
												lowerCAmelCase__	 =  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(A_				)
						return solution_file_paths


def     _a					(       )	->				Dict:
						"""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_   :			Any				)	->				Union[str, Any]:
						"""simple docstring"""







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














import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class        lowercase__       (     unittest.TestCase		):

	@property
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  8
							lowerCAmelCase__	 =  8

							lowerCAmelCase__	 =  torch.rand((batch_size, num_channels, height, width)    )

							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if isinstance(image[0]						,      PIL.Image.Image    ):
													return np.stack([np.array(__UpperCAmelCase    ) for i in image]						,      axis=0    )
							elif isinstance(__UpperCAmelCase						,      torch.Tensor    ):
													return image.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							return image

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







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(
													    image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase						,      )
													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )
							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )

													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							lowerCAmelCase__	 =  image_processor.numpy_to_pil(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )
													for i, o in zip(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  np.array(__UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    ) if output_type == "pil" else (self.to_np(__UpperCAmelCase    ) * 255).round()
																			assert (
																			    np.abs(in_np - out_np    ).max() < 1E-6
																			), F"decoded output does not match input for output_type {output_type}"

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







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  input_pt_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							lowerCAmelCase__	 =  input_np_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6


	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6






