
"""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)
UpperCAmelCase__      :    List[Any]    							= logging.getLogger()

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """\n""".join(_snake_case						)
							Path(_snake_case						).open("""w"""						).writelines(_snake_case						)


UpperCAmelCase__      :    Union[str, Any]    							= 'patrickvonplaten/t5-tiny-random'
UpperCAmelCase__      :    Optional[int]    							= 'sshleifer/bart-tiny-random'
UpperCAmelCase__      :    Dict    							= 'sshleifer/tiny-mbart'

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



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




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

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



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([BART_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([T5_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
											SCREAMING_SNAKE_CASE__						:       int           =   input_file_name.parent / """utest_output.txt"""
											assert not output_file_name.exists()

											SCREAMING_SNAKE_CASE__						:       Any           =   {
											    """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__						:       List[str]           =   Path(self.get_auto_remove_tmp_dir()    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """val.target"""    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""en"""]    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""de"""]    )
											SCREAMING_SNAKE_CASE__						:       str           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   F'''
            run_eval_search.py
            {model}
            {str(SCREAMING_SNAKE_CASE__    )}
            {str(SCREAMING_SNAKE_CASE__    )}
            --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(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		with CaptureStdout() as cs:
																									run_search()
																		SCREAMING_SNAKE_CASE__						:       Optional[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(SCREAMING_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(SCREAMING_SNAKE_CASE__    ).exists()
																		os.remove(Path(SCREAMING_SNAKE_CASE__    )    )







def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

