





"""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)
_snake_case     :		List[str]       						= logging.getLogger()






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  "\n".join(UpperCamelCase       )
							Path(UpperCamelCase       ).open("w"       ).writelines(UpperCamelCase       )


_snake_case     :		Union[str, Any]       						= "patrickvonplaten/t5-tiny-random"
_snake_case     :		Optional[int]       						= "sshleifer/bart-tiny-random"
_snake_case     :		List[Any]       						= "sshleifer/tiny-mbart"

_snake_case     :		Optional[Any]       						= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 					_UpperCAmelCase   (	_SCREAMING_SNAKE_CASE ):


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:Tuple		):
												A		=  Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
												A		=  input_file_name.parent / "utest_output.txt"
												assert not output_file_name.exists()
												A		=  [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
												_dump_articles(A_     ,       A_		)

												A		=  str(Path(self.get_auto_remove_tmp_dir()		) / "scores.json"		)
												A		=  "translation_en_to_de" if model == T5_TINY else "summarization"
												A		=  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(A_     ,       "argv"     ,       A_		):
																			run_generate()
																			assert Path(A_		).exists()
																			# os.remove(Path(output_file_name))


					def 	lowerCamelCase       (					self			:Tuple		):
												self.run_eval_tester(A_		)


					@parameterized.expand([BART_TINY, MBART_TINY]		)
					@slow
					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:str		):
												self.run_eval_tester(A_		)


					@parameterized.expand([T5_TINY, MBART_TINY]		)
					@slow
					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:Optional[int]		):
												A		=  Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
												A		=  input_file_name.parent / "utest_output.txt"
												assert not output_file_name.exists()

												A		=  {
												    "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!",
												    ],
												}

												A		=  Path(self.get_auto_remove_tmp_dir()		)
												A		=  str(tmp_dir / "scores.json"		)
												A		=  str(tmp_dir / "val.target"		)
												_dump_articles(A_     ,       text["en"]		)
												_dump_articles(A_     ,       text["de"]		)
												A		=  "translation_en_to_de" if model == T5_TINY else "summarization"
												A		=  f"\n            run_eval_search.py\n            {model}\n            {str(A_		)}\n            {str(A_		)}\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(A_     ,       "argv"     ,       A_		):
																			with CaptureStdout() as cs:
																										run_search()
																			A		=  [" num_beams | length_penalty", model, "Best score args"]
																			A		=  ["Info"]
																			if "translation" in task:
																										expected_strings.append("bleu"		)
																			else:
																										expected_strings.extend(A_		)
																			for w in expected_strings:
																										assert w in cs.out
																			for w in un_expected_strings:
																										assert w not in cs.out
																			assert Path(A_		).exists()
																			os.remove(Path(A_		)		)




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   ):



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				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=A_				,      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 __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				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=A_				,      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 __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				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=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

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

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

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