'''simple docstring'''
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 			_UpperCamelCase			(  _a						:						pathlib.Path      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	importlib.util.spec_from_file_location(file_path.name					,  str(_a      )      )
    __UpperCamelCase						:						Optional[Any]		     =	importlib.util.module_from_spec(_a      )  # type: ignore
    spec.loader.exec_module(_a      )  # type: ignore
    return module







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	[]
    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 			_UpperCamelCase			(  ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						str		     =	[]
    __UpperCamelCase						:						List[Any]		     =	{
        'Accept': 'application/vnd.github.v3+json',
        'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
    }
    __UpperCamelCase						:						str		     =	requests.get(get_files_url()					,  headers=_a      ).json()
    for file in files:
        __UpperCamelCase						:						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(_a      )
    return solution_file_paths







def 			_UpperCamelCase			(  ):




    """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 _a      : f"""{path.parent.name}/{path.name}"""					,  )
def 			_UpperCamelCase			(  _a						:						pathlib.Path      ):




    """simple docstring"""



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




'''simple docstring'''
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @property
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	1
         __UpperCamelCase						:						Union[str, Any]		     =	3
         __UpperCamelCase						:						Tuple		     =	(3_2, 3_2)

         __UpperCamelCase						:						List[Any]		     =	floats_tensor((batch_size, num_channels) + sizes  ,      rng=random.Random(0     )     ).to(_lowerCamelCase     )
         return image







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	UNetaDModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=6  ,      out_channels=3  ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,      )
         return model







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						List[str]		     =	VQModel(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=3  ,      )
         return model







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	'cpu'
         __UpperCamelCase						:						Union[str, Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						List[str]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         __UpperCamelCase						:						Tuple		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						int		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.Generator(device=_lowerCamelCase     ).manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

         __UpperCamelCase						:						str		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         __UpperCamelCase						:						str		     =	np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test requires a GPU'     )
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						Optional[int]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         # put models in fp16
         __UpperCamelCase						:						str		     =	unet.half()
         __UpperCamelCase						:						Optional[Any]		     =	vqvae.half()

         __UpperCamelCase						:						Optional[int]		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[Any]		     =	ldm(_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

         assert image.shape == (1, 6_4, 6_4, 3)






@slow
@require_torch
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool.png'     )
         __UpperCamelCase						:						List[Any]		     =	init_image.resize((6_4, 6_4)  ,      resample=PIL_INTERPOLATION['lanczos']     )

         __UpperCamelCase						:						List[str]		     =	LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'  ,      device_map='auto'     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2_0  ,      output_type='numpy'     ).images

         __UpperCamelCase						:						List[Any]		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         __UpperCamelCase						:						str		     =	np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2



