




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







def       _UpperCamelCase			(_lowerCamelCase				:  Path ,						_lowerCamelCase				:  list						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	'''\n'''.join(_lowerCamelCase						)
     Path(_lowerCamelCase						).open('''w'''						).writelines(_lowerCamelCase						)


UpperCAmelCase_							:	str	     =		'''patrickvonplaten/t5-tiny-random'''
UpperCAmelCase_							:	int	     =		'''sshleifer/bart-tiny-random'''
UpperCAmelCase_							:	Optional[Any]	     =		'''sshleifer/tiny-mbart'''

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







class      lowerCAmelCase (			__lowerCAmelCase):


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




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

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


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



            '''simple docstring'''




            self.run_eval_tester(__SCREAMING_SNAKE_CASE	)


       @parameterized.expand([BART_TINY, MBART_TINY]	)
       @slow
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            self.run_eval_tester(__SCREAMING_SNAKE_CASE	)




       @parameterized.expand([T5_TINY, MBART_TINY]	)
       @slow
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




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

            __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!''',
                ],
            }

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










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




