


'''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)
__lowercase	:						Dict       =	logging.getLogger()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Path						,      _SCREAMING_SNAKE_CASE      :   list   ):
					__a			:       Union[str, Any]					    =  '\n'.join(_SCREAMING_SNAKE_CASE   )
					Path(_SCREAMING_SNAKE_CASE   ).open('w'   ).writelines(_SCREAMING_SNAKE_CASE   )


__lowercase	:						Any       =	'patrickvonplaten/t5-tiny-random'
__lowercase	:						List[Any]       =	'sshleifer/bart-tiny-random'
__lowercase	:						Dict       =	'sshleifer/tiny-mbart'

__lowercase	:						Optional[int]       =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





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

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.run_eval_tester(__a							)




	@parameterized.expand([BART_TINY, MBART_TINY]							)
	@slow
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						self.run_eval_tester(__a							)





	@parameterized.expand([T5_TINY, MBART_TINY]							)
	@slow
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  Path(self.get_auto_remove_tmp_dir()							) / 'utest_input.source'
						__a			:       Union[str, Any]					    =  input_file_name.parent / 'utest_output.txt'
						assert not output_file_name.exists()

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWForCTC',
       'SEWForSequenceClassification',
       'SEWModel',
       'SEWPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew import (
          SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWForCTC,
          SEWForSequenceClassification,
          SEWModel,
          SEWPreTrainedModel,
      )


else:
   import sys

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





