



"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




@slow
@require_torch_gpu
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							str:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
									_UpperCAmelCase							 =		sd_pipe.to(snake_case						)
									sd_pipe.set_progress_bar_config(disable=snake_case						)

									sd_pipe.set_scheduler('sample_euler'						)

									_UpperCAmelCase							 =		'A painting of a squirrel eating a burger'
									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		sd_pipe([prompt]	,       generator=snake_case	,       guidance_scale=9.0	,       num_inference_steps=20	,       output_type='np'						)

									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase							 =		np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]						)

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






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
									_UpperCAmelCase							 =		sd_pipe.to(snake_case						)
									sd_pipe.set_progress_bar_config(disable=snake_case						)

									sd_pipe.set_scheduler('sample_euler'						)

									_UpperCAmelCase							 =		'A painting of a squirrel eating a burger'
									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		sd_pipe([prompt]	,       generator=snake_case	,       guidance_scale=9.0	,       num_inference_steps=20	,       output_type='np'						)

									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase							 =		np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-1






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
									_UpperCAmelCase							 =		sd_pipe.to(snake_case						)
									sd_pipe.set_progress_bar_config(disable=snake_case						)

									sd_pipe.set_scheduler('sample_dpmpp_2m'						)

									_UpperCAmelCase							 =		'A painting of a squirrel eating a burger'
									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		sd_pipe(
									    [prompt]	,       generator=snake_case	,       guidance_scale=7.5	,       num_inference_steps=15	,       output_type='np'	,       use_karras_sigmas=snake_case	,       )

									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase							 =		np.array(
									    [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]						)

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







"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

lowercase  							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()
									_UpperCAmelCase							 =		[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
									_dump_articles(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([T5_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_UpperCAmelCase							 =		{
									    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
									    'de': [
									        'Maschinelles Lernen ist großartig, oder?',
									        'Ich esse gerne Bananen',
									        'Morgen ist wieder ein toller Tag!',
									    ],
									}

									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(snake_case						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
									testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']						)

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(snake_case						)
											for w in expected_strings:
													assert w in cs.out
											for w in un_expected_strings:
													assert w not in cs.out
											assert Path(snake_case						).exists()
											os.remove(Path(snake_case						)						)


