


"""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)
_lowerCamelCase     =  logging.getLogger()


def __lowercase     (      lowerCamelCase_  :      Path						,					lowerCamelCase_  :      list  ):
				SCREAMING_SNAKE_CASE__     							=				"\n".join(lowerCamelCase_  )
				Path(lowerCamelCase_  ).open("w"  ).writelines(lowerCamelCase_  )


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

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


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







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

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

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



				def   lowerCAmelCase__		(						self     ):
								self.run_eval_tester(UpperCAmelCase__     )



				@parameterized.expand([BART_TINY, MBART_TINY]     )
				@slow
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								self.run_eval_tester(UpperCAmelCase__     )






				@parameterized.expand([T5_TINY, MBART_TINY]     )
				@slow
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				Path(self.get_auto_remove_tmp_dir()     ) / "utest_input.source"
								SCREAMING_SNAKE_CASE__     							=				input_file_name.parent / "utest_output.txt"
								assert not output_file_name.exists()

								SCREAMING_SNAKE_CASE__     							=				{
								    "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!",
								    ],
								}

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

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




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase     =  0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_lowerCamelCase     =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

								self.encoder.set_watermark("bits"       ,		self.watermark     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

								SCREAMING_SNAKE_CASE__     							=				(255 * (images / 2 + 0.5)).cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

								SCREAMING_SNAKE_CASE__     							=				[self.encoder.encode(UpperCAmelCase__       ,		"dwtDct"     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

								SCREAMING_SNAKE_CASE__     							=				torch.clamp(2 * (images / 255 - 0.5)       ,		min=-1.0       ,		max=1.0     )
								return images
