



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    				=		logging.getLogger()


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Any							=    "\n".join(__snake_case				)
  Path(__snake_case				).open("w"				).writelines(__snake_case				)


snake_case    				=		"""patrickvonplaten/t5-tiny-random"""
snake_case    				=		"""sshleifer/bart-tiny-random"""
snake_case    				=		"""sshleifer/tiny-mbart"""

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


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    Path(self.get_auto_remove_tmp_dir()      ) / "utest_input.source"
       SCREAMING_SNAKE_CASE       :      Dict							=    input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()
       SCREAMING_SNAKE_CASE       :      Dict							=    [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
       _dump_articles(__a					, __a      )

       SCREAMING_SNAKE_CASE       :      int							=    str(Path(self.get_auto_remove_tmp_dir()      ) / "scores.json"      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "translation_en_to_de" if model == T5_TINY else "summarization"
       SCREAMING_SNAKE_CASE       :      Tuple							=    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(__a					, "argv"					, __a      ):
         run_generate()
         assert Path(__a      ).exists()
         # os.remove(Path(output_file_name))


     def 				_A       (				self       :	Dict      ):
       self.run_eval_tester(__a      )


     @parameterized.expand([BART_TINY, MBART_TINY]      )
     @slow
     def 				_A       (				self       :	str					, UpperCAmelCase_       :	Union[str, Any]      ):
       self.run_eval_tester(__a      )






     @parameterized.expand([T5_TINY, MBART_TINY]      )
     @slow
     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      str							=    Path(self.get_auto_remove_tmp_dir()      ) / "utest_input.source"
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    {
           "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       :      Dict							=    Path(self.get_auto_remove_tmp_dir()      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    str(tmp_dir / "scores.json"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    str(tmp_dir / "val.target"      )
       _dump_articles(__a					, text["en"]      )
       _dump_articles(__a					, text["de"]      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    "translation_en_to_de" if model == T5_TINY else "summarization"
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    f'''
            run_eval_search.py
            {model}
            {str(__a      )}
            {str(__a      )}
            --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(__a					, "argv"					, __a      ):
         with CaptureStdout() as cs:
           run_search()
         SCREAMING_SNAKE_CASE       :      Any							=    [" num_beams | length_penalty", model, "Best score args"]
         SCREAMING_SNAKE_CASE       :      Any							=    ["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 numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	CLIPConfig

     UpperCamelCase_							:		Optional[int]			=	['''CLIPEncoderLayer''']


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	CLIPConfig      ):
       super().__init__(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    CLIPVisionModelWithProjection(config.vision_config      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    nn.Linear(config.vision_config.projection_dim					, 1      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    nn.Linear(config.vision_config.projection_dim					, 1      )






     @torch.no_grad()
     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[Any]=0.5					, UpperCAmelCase_       :	Optional[Any]=0.5      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.vision_model(UpperCAmelCase_      )[0]

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.p_head(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    nsfw_detected.flatten()
       SCREAMING_SNAKE_CASE       :      List[str]							=    nsfw_detected > p_threshold
       SCREAMING_SNAKE_CASE       :      List[Any]							=    nsfw_detected.tolist()

       if any(UpperCAmelCase_      ):
         logger.warning(
             "Potential NSFW content was detected in one or more images. A black image will be returned instead."
             " Try again with a different prompt and/or seed."      )

       for idx, nsfw_detected_ in enumerate(UpperCAmelCase_      ):
         if nsfw_detected_:
           SCREAMING_SNAKE_CASE       :      Dict							=    np.zeros(images[idx].shape      )

       SCREAMING_SNAKE_CASE       :      Any							=    self.w_head(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    watermark_detected.flatten()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    watermark_detected > w_threshold
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    watermark_detected.tolist()

       if any(UpperCAmelCase_      ):
         logger.warning(
             "Potential watermarked content was detected in one or more images. A black image will be returned instead."
             " Try again with a different prompt and/or seed."      )

       for idx, watermark_detected_ in enumerate(UpperCAmelCase_      ):
         if watermark_detected_:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.zeros(images[idx].shape      )

       return images, nsfw_detected, watermark_detected






