



'''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)
snake_case_			=     logging.getLogger()






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Path    ,	_SCREAMING_SNAKE_CASE      :list       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''\n'''.join(_SCREAMING_SNAKE_CASE       )
				Path(_SCREAMING_SNAKE_CASE       ).open('''w'''       ).writelines(_SCREAMING_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 	a__     (				_lowercase	):


	def 				lowercase__  (self : str,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= 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  :						List[str]      					= [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
					_dump_articles(__UpperCAmelCase,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= str(Path(self.get_auto_remove_tmp_dir()      ) / '''scores.json'''      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''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(__UpperCAmelCase,					'''argv''',					__UpperCAmelCase      ):
									run_generate()
									assert Path(__UpperCAmelCase      ).exists()
									# os.remove(Path(output_file_name))


	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					self.run_eval_tester(__UpperCAmelCase      )


	@parameterized.expand([BART_TINY, MBART_TINY]      )
	@slow
	def 				lowercase__  (self : List[str],					__UpperCAmelCase : Dict      )     ->			str:

					"""simple docstring"""

					self.run_eval_tester(__UpperCAmelCase      )






	@parameterized.expand([T5_TINY, MBART_TINY]      )
	@slow
	def 				lowercase__  (self : Dict,					__UpperCAmelCase : List[Any]      )     ->			str:

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









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




