




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






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





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							1
								lowercase__  :	Tuple        =							3
								lowercase__  :	str        =							8
								lowercase__  :	List[Any]        =							8

								lowercase__  :	Union[str, Any]        =							torch.rand((batch_size, num_channels, height, width)    )

								return sample

	def    __a     (					self	,				lowerCamelCase    ) ->      int:





								"""simple docstring"""


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

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





								"""simple docstring"""


								lowercase__  :	Any        =							VaeImageProcessor(do_resize=lowerCamelCase	,				do_normalize=lowerCamelCase    )

								lowercase__  :	Any        =							self.dummy_sample
								lowercase__  :	str        =							self.to_np(lowerCamelCase    )

								for output_type in ["pt", "np", "pil"]:
															lowercase__  :	int        =							image_processor.postprocess(
															    image_processor.preprocess(lowerCamelCase    )	,				output_type=lowerCamelCase	,				)
															lowercase__  :	Union[str, Any]        =							self.to_np(lowerCamelCase    )
															lowercase__  :	Tuple        =							(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    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	int        =							VaeImageProcessor(do_resize=lowerCamelCase	,				do_normalize=lowerCamelCase    )
								lowercase__  :	int        =							self.dummy_sample.cpu().numpy().transpose(0	,				2	,				3	,				1    )

								for output_type in ["pt", "np", "pil"]:
															lowercase__  :	Union[str, Any]        =							image_processor.postprocess(image_processor.preprocess(lowerCamelCase    )	,				output_type=lowerCamelCase    )

															lowercase__  :	Dict        =							self.to_np(lowerCamelCase    )
															lowercase__  :	Optional[Any]        =							(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    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	List[str]        =							VaeImageProcessor(do_resize=lowerCamelCase	,				do_normalize=lowerCamelCase    )

								lowercase__  :	Any        =							self.dummy_sample.cpu().numpy().transpose(0	,				2	,				3	,				1    )
								lowercase__  :	List[Any]        =							image_processor.numpy_to_pil(lowerCamelCase    )

								for output_type in ["pt", "np", "pil"]:
															lowercase__  :	str        =							image_processor.postprocess(image_processor.preprocess(lowerCamelCase    )	,				output_type=lowerCamelCase    )
															for i, o in zip(lowerCamelCase	,				lowerCamelCase    ):
																						lowercase__  :	Optional[Any]        =							np.array(lowerCamelCase    )
																						lowercase__  :	Tuple        =							self.to_np(lowerCamelCase    ) if output_type == "pil" else (self.to_np(lowerCamelCase    ) * 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    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							VaeImageProcessor(do_resize=lowerCamelCase	,				do_normalize=lowerCamelCase    )

								lowercase__  :	List[Any]        =							self.dummy_sample
								lowercase__  :	Any        =							input_pt_ad.squeeze(0    )

								lowercase__  :	Optional[int]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				output_type="np"	,				)
								lowercase__  :	List[str]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				output_type="np"	,				)

								lowercase__  :	Union[str, Any]        =							self.to_np(self.dummy_sample    )
								lowercase__  :	Dict        =							input_np_ad.squeeze(0    )

								lowercase__  :	Optional[int]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				output_type="np"	,				)
								lowercase__  :	List[str]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				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    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							VaeImageProcessor(do_resize=lowerCamelCase	,				do_normalize=lowerCamelCase    )

								lowercase__  :	Dict        =							self.dummy_sample
								lowercase__  :	str        =							list(lowerCamelCase    )

								lowercase__  :	Optional[int]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				output_type="np"	,				)

								lowercase__  :	Optional[Any]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				output_type="np"	,				)

								lowercase__  :	Union[str, Any]        =							self.to_np(self.dummy_sample    )
								list(lowerCamelCase    )

								lowercase__  :	List[str]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				output_type="np"	,				)

								lowercase__  :	List[Any]        =							image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase    )	,				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





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

import pytest
import requests

__a  :		str		     =   pathlib.Path.cwd().joinpath('''project_euler''')
__a  :		Tuple		     =   pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
						__a  :		dict[str, str]		     =   json.load(file_handle)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			ModuleType:
							lowercase__  :	List[str]        =							importlib.util.spec_from_file_location(file_path.name    ,str(SCREAMING_SNAKE_CASE_    )    )
							lowercase__  :	int        =							importlib.util.module_from_spec(SCREAMING_SNAKE_CASE_    )  # type: ignore
							spec.loader.exec_module(SCREAMING_SNAKE_CASE_    )  # type: ignore
							return module
def  snake_case_					(		)      ->			list[pathlib.Path]:
							lowercase__  :	List[str]        =							[]
							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(SCREAMING_SNAKE_CASE_    )
							return solution_file_paths
def  snake_case_					(		)      ->			str:
							with open(os.environ["GITHUB_EVENT_PATH"]    ) as file:
														lowercase__  :	str        =							json.load(SCREAMING_SNAKE_CASE_    )
							return event["pull_request"]["url"] + "/files"
def  snake_case_					(		)      ->			list[pathlib.Path]:
							lowercase__  :	Optional[Any]        =							[]
							lowercase__  :	Dict        =							{
							    "Accept": "application/vnd.github.v3+json",
							    "Authorization": "token " + os.environ["GITHUB_TOKEN"],
							}
							lowercase__  :	Union[str, Any]        =							requests.get(get_files_url()    ,headers=SCREAMING_SNAKE_CASE_    ).json()
							for file in files:
														lowercase__  :	Optional[Any]        =							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(SCREAMING_SNAKE_CASE_    )
							return solution_file_paths
def  snake_case_					(		)      ->			list[pathlib.Path]:
							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 SCREAMING_SNAKE_CASE_    : F"""{path.parent.name}/{path.name}"""    ,)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			None:
							lowercase__  :	str        =							solution_path.parent.name[8:].zfill(3    )
							lowercase__  :	str        =							PROBLEM_ANSWERS[problem_number]
							lowercase__  :	Optional[Any]        =							convert_path_to_module(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	str        =							str(solution_module.solution()    )  # type: ignore
							lowercase__  :	List[str]        =							hashlib.shaaaa(answer.encode()    ).hexdigest()
							assert (
							    answer == expected
							), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""