

'''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 :      List[Any]					 =     logging.getLogger()


def        _lowerCAmelCase					(						_UpperCamelCase						:     Path							, _UpperCamelCase						:     list				)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ='\n'.join(_UpperCamelCase				)
     Path(_UpperCamelCase				).open('w'				).writelines(_UpperCamelCase				)


lowerCamelCase :      Tuple					 =     "patrickvonplaten/t5-tiny-random"
lowerCamelCase :      Tuple					 =     "sshleifer/bart-tiny-random"
lowerCamelCase :      List[Any]					 =     "sshleifer/tiny-mbart"

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







class 							A__    (       A__  ):





  def 						A  (		self      :	Any							,					_a      :	Dict )	->    Dict:





       '''simple docstring'''






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

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





  def 						A  (		self      :	List[str] )	->    str:





       '''simple docstring'''






       self.run_eval_tester(_a )





  @parameterized.expand([BART_TINY, MBART_TINY] )
  @slow
  def 						A  (		self      :	Optional[Any]							,					_a      :	Tuple )	->    List[str]:





       '''simple docstring'''






       self.run_eval_tester(_a )





  @parameterized.expand([T5_TINY, MBART_TINY] )
  @slow
  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    Union[str, Any]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    ={
           '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							    =Path(self.get_auto_remove_tmp_dir() )
       _SCREAMING_SNAKE_CASE							    =str(tmp_dir / 'scores.json' )
       _SCREAMING_SNAKE_CASE							    =str(tmp_dir / 'val.target' )
       _dump_articles(_a							,					text['en'] )
       _dump_articles(_a							,					text['de'] )
       _SCREAMING_SNAKE_CASE							    ='translation_en_to_de' if model == T5_TINY else 'summarization'
       _SCREAMING_SNAKE_CASE							    =f"\n            run_eval_search.py\n            {model}\n            {str(_a )}\n            {str(_a )}\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(_a							,					'argv'							,					_a ):
            with CaptureStdout() as cs:
                 run_search()
            _SCREAMING_SNAKE_CASE							    =[' num_beams | length_penalty', model, 'Best score args']
            _SCREAMING_SNAKE_CASE							    =['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"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase						=      {
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlipModel''',
	    '''BlipPreTrainedModel''',
	    '''BlipForConditionalGeneration''',
	    '''BlipForQuestionAnswering''',
	    '''BlipVisionModel''',
	    '''BlipTextModel''',
	    '''BlipForImageTextRetrieval''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFBlipModel''',
	    '''TFBlipPreTrainedModel''',
	    '''TFBlipForConditionalGeneration''',
	    '''TFBlipForQuestionAnswering''',
	    '''TFBlipVisionModel''',
	    '''TFBlipTextModel''',
	    '''TFBlipForImageTextRetrieval''',
	]

if TYPE_CHECKING:
	from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
	from .processing_blip import BlipProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_blip import BlipImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blip import (
		    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlipForConditionalGeneration,
		    BlipForImageTextRetrieval,
		    BlipForQuestionAnswering,
		    BlipModel,
		    BlipPreTrainedModel,
		    BlipTextModel,
		    BlipVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blip import (
		    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBlipForConditionalGeneration,
		    TFBlipForImageTextRetrieval,
		    TFBlipForQuestionAnswering,
		    TFBlipModel,
		    TFBlipPreTrainedModel,
		    TFBlipTextModel,
		    TFBlipVisionModel,
		)

else:
	import sys

	__UpperCamelCase						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


