



'''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)
__lowerCAmelCase									=   logging.getLogger()



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		_a     :      Any        =							'\n'.join(lowerCAmelCase_  )
		Path(lowerCAmelCase_  ).open('w'  ).writelines(lowerCAmelCase_  )


__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 	__magic_name__						(    _UpperCamelCase   ):







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

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







							def 							__lowercase			(							self					:       Optional[int]		):
									self.run_eval_tester(_UpperCAmelCase		)







							@parameterized.expand([BART_TINY, MBART_TINY]		)
							@slow
							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       Union[str, Any]		):
									self.run_eval_tester(_UpperCAmelCase		)







							@parameterized.expand([T5_TINY, MBART_TINY]		)
							@slow
							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Any		):
									_a     :      List[str]        =							Path(self.get_auto_remove_tmp_dir()		) / 'utest_input.source'
									_a     :      Tuple        =							input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_a     :      Optional[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!',
									    ],
									}

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







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel






