

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)
A_	:			Dict  	=      logging.getLogger()

def 						UpperCamelCase (lowercase_:			Path   ,				lowercase_:			list )					->				Union[str, Any]:
 A__							:   Union[str, Any]					       =		'''\n'''.join(lowercase_ )
 Path(lowercase_ ).open("""w""" ).writelines(lowercase_ )


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

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



class    _a						(__lowerCAmelCase    ):


     '''simple docstring'''






     def   __A      (			self   ,	A__      ):
      A__							:   Optional[Any]					       =		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__							:   Any					       =		[''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
      _dump_articles(lowerCamelCase__   ,	lowerCamelCase__      )

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






     def   __A      (			self      ):
      self.run_eval_tester(lowerCamelCase__      )






     @parameterized.expand([BART_TINY, MBART_TINY]      )
     @slow
     def   __A      (			self   ,	A__      ):
      self.run_eval_tester(lowerCamelCase__      )






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

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






from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	help="""Path to location to store the models"""      )
						download_parser.add_argument(
						    """--force"""   ,	action="""store_true"""   ,	help="""Force the model to be download even if already in cache-dir"""      )
						download_parser.add_argument(
						    """--trust-remote-code"""   ,	action="""store_true"""   ,	help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,	)
						download_parser.add_argument("""model"""   ,	type=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		trust_remote_code






					def   __A      (			self      ):
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )
						AutoTokenizer.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )



