






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      snake_case		(							UpperCAmelCase	:       Path,						UpperCAmelCase	:       list   ):
				A	 =      '\n'.join(UpperCAmelCase   )
				Path(UpperCAmelCase   ).open('w'   ).writelines(UpperCAmelCase   )


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      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   List[str]:


						'''simple docstring'''
						A	 =      Path(self.get_auto_remove_tmp_dir()		) / 'utest_input.source'
						A	 =      input_file_name.parent / 'utest_output.txt'
						assert not output_file_name.exists()
						A	 =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
						_dump_articles(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

						A	 =      str(Path(self.get_auto_remove_tmp_dir()		) / 'scores.json'		)
						A	 =      'translation_en_to_de' if model == T5_TINY else 'summarization'
						A	 =      f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.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       A(  self		:       List[str]		)    ->   Union[str, Any]:


						'''simple docstring'''
						self.run_eval_tester(_SCREAMING_SNAKE_CASE		)
		@parameterized.expand([BART_TINY, MBART_TINY]		)
		@slow
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str		)    ->   Union[str, Any]:


						'''simple docstring'''
						self.run_eval_tester(_SCREAMING_SNAKE_CASE		)







		@parameterized.expand([T5_TINY, MBART_TINY]		)
		@slow
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      Path(self.get_auto_remove_tmp_dir()		) / 'utest_input.source'
						A	 =      input_file_name.parent / 'utest_output.txt'
						assert not output_file_name.exists()

						A	 =      {
						    '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!',
						    ],
						}

						A	 =      Path(self.get_auto_remove_tmp_dir()		)
						A	 =      str(tmp_dir / 'scores.json'		)
						A	 =      str(tmp_dir / 'val.target'		)
						_dump_articles(_SCREAMING_SNAKE_CASE						,text['en']		)
						_dump_articles(_SCREAMING_SNAKE_CASE						,text['de']		)
						A	 =      'translation_en_to_de' if model == T5_TINY else 'summarization'
						A	 =      f'\n            run_eval_search.py\n            {model}\n            {str(_SCREAMING_SNAKE_CASE		)}\n            {str(_SCREAMING_SNAKE_CASE		)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.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()
										A	 =      [' num_beams | length_penalty', model, 'Best score args']
										A	 =      ['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		)		)













from collections import deque

from .hash_table import HashTable
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Any						,*_SCREAMING_SNAKE_CASE		:       Optional[int]						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						A	 =      deque([]		) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(_SCREAMING_SNAKE_CASE		)
						A	 =      self.values[key]
		def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return (
						    sum(self.charge_factor - len(_SCREAMING_SNAKE_CASE		) for slot in self.values		)
						    / self.size_table
						    * self.charge_factor
						)







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str=None		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not (
						    len(self.values[key]		) == self.charge_factor and self.values.count(_SCREAMING_SNAKE_CASE		) == 0
						):
										return key
						return super()._collision_resolution(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)





