




'''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)
lowercase      :   Optional[int]      =       logging.getLogger()



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     snake_case			:      Optional[int]        =			"""\n""".join(__lowercase				)
     Path(__lowercase				).open("""w"""				).writelines(__lowercase				)


lowercase      :   int      =       """patrickvonplaten/t5-tiny-random"""
lowercase      :   int      =       """sshleifer/bart-tiny-random"""
lowercase      :   Optional[int]      =       """sshleifer/tiny-mbart"""

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







class 					_a (a__	):





     '''simple docstring'''



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

          snake_case			:      Tuple        =			str(Path(self.get_auto_remove_tmp_dir()							) / """scores.json"""							)
          snake_case			:      int        =			"""translation_en_to_de""" if model == T5_TINY else """summarization"""
          snake_case			:      Dict        =			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 			snake_case_      (				self							)      ->				List[str]:
          self.run_eval_tester(__a							)







     @parameterized.expand([BART_TINY, MBART_TINY]							)
     @slow
     def 			snake_case_      (				self							,__a							)      ->				Tuple:
          self.run_eval_tester(__a							)







     @parameterized.expand([T5_TINY, MBART_TINY]							)
     @slow
     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Optional[Any]        =			Path(self.get_auto_remove_tmp_dir()							) / """utest_input.source"""
          snake_case			:      Optional[int]        =			input_file_name.parent / """utest_output.txt"""
          assert not output_file_name.exists()

          snake_case			:      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!""",
              ],
          }

          snake_case			:      List[str]        =			Path(self.get_auto_remove_tmp_dir()							)
          snake_case			:      List[Any]        =			str(tmp_dir / """scores.json"""							)
          snake_case			:      str        =			str(tmp_dir / """val.target"""							)
          _dump_articles(__a							,text["""en"""]							)
          _dump_articles(__a							,text["""de"""]							)
          snake_case			:      List[str]        =			"""translation_en_to_de""" if model == T5_TINY else """summarization"""
          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()
               snake_case			:      List[str]        =			[""" num_beams | length_penalty""", model, """Best score args"""]
               snake_case			:      Optional[int]        =			["""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							)							)









'''simple docstring'''







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



