
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)
__magic_name__  							=	logging.getLogger()





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					"\n".join(__lowerCAmelCase				)
    Path(__lowerCAmelCase				).open("w"				).writelines(__lowerCAmelCase				)


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

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





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

          snake_case__    =					str(Path(self.get_auto_remove_tmp_dir()			) / "scores.json"			)
          snake_case__    =					"translation_en_to_de" if model == T5_TINY else "summarization"
          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(lowerCamelCase		,						"argv"		,						lowerCamelCase			):
              run_generate()
              assert Path(lowerCamelCase			).exists()
              # os.remove(Path(output_file_name))
      def A_      (      self			):
          self.run_eval_tester(lowerCamelCase			)
      @parameterized.expand([BART_TINY, MBART_TINY]			)
      @slow
      def A_      (      self		,						lowerCamelCase			):
          self.run_eval_tester(lowerCamelCase			)



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

          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!",
              ],
          }

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





import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):

    if initial_intensity < 0:
        raise ValueError("The value of intensity cannot be negative"				)
        # handling of negative values of initial intensity
    if angle < 0 or angle > 360:
        raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"				)
        # handling of values out of allowed range
    return initial_intensity * (math.cos(math.radians(__lowerCAmelCase				)				) ** 2)


if __name__ == "__main__":
     import doctest

     doctest.testmod(name='''malus_law''')



