






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								=						logging.getLogger()




def      lowercase__      (     __snake_case    :							Path     ,       __snake_case    :							list  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						'\n'.join(__snake_case  )
					Path(__snake_case  ).open('w'  ).writelines(__snake_case  )


__UpperCAmelCase								=						'patrickvonplaten/t5-tiny-random'
__UpperCAmelCase								=						'sshleifer/bart-tiny-random'
__UpperCAmelCase								=						'sshleifer/tiny-mbart'

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






class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



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

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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							self.run_eval_tester(_UpperCamelCase   )



		@parameterized.expand([BART_TINY, MBART_TINY]   )
		@slow
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							self.run_eval_tester(_UpperCamelCase   )



		@parameterized.expand([T5_TINY, MBART_TINY]   )
		@slow
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      str            =						Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
							UpperCAmelCase_    :      Tuple            =						input_file_name.parent / 'utest_output.txt'
							assert not output_file_name.exists()

							UpperCAmelCase_    :      List[Any]            =						{
							    '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!',
							    ],
							}

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










import os
import sys
import unittest


lowerCamelCase											=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase											=       os.path.join(git_repo_path, 'src', 'transformers')

lowerCamelCase											=       '\n{0} = None\n'

lowerCamelCase											=       '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


lowerCamelCase											=       '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'




class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
             self.assertIsNone(lowercase_ )

             _lowerCamelCase       :		List[str]		=find_backend('    if not is_tokenizers_available():' )
             self.assertEqual(lowercase_			,   'tokenizers' )

             _lowerCamelCase       :		List[Any]		=find_backend('    if not is_tensorflow_text_available():' )
             self.assertEqual(lowercase_			,   'tensorflow_text' )

             _lowerCamelCase       :		int		=find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers' )

             _lowerCamelCase       :		Dict		=find_backend(
                 '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tensorflow_text' )

             _lowerCamelCase       :		List[Any]		=find_backend(
                 '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers_and_vision' )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=read_init()
             # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
             self.assertIn('torch'			,   lowercase_ )
             self.assertIn('tensorflow_text'			,   lowercase_ )
             self.assertIn('sentencepiece_and_tokenizers'			,   lowercase_ )

             # Likewise, we can't assert on the exact content of a key
             self.assertIn('BertModel'			,   objects['torch'] )
             self.assertIn('TFBertModel'			,   objects['tf'] )
             self.assertIn('FlaxBertModel'			,   objects['flax'] )
             self.assertIn('BertModel'			,   objects['torch'] )
             self.assertIn('TFBertTokenizer'			,   objects['tensorflow_text'] )
             self.assertIn('convert_slow_tokenizer'			,   objects['sentencepiece_and_tokenizers'] )






      def        lowerCamelCase       (					self						:     int )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=create_dummy_object('CONSTANT'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   '\nCONSTANT = None\n' )

             _lowerCamelCase       :		Dict		=create_dummy_object('function'			,   '\'torch\'' )
             self.assertEqual(
                 lowercase_			,   '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

             _lowerCamelCase       :		Union[str, Any]		='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
             _lowerCamelCase       :		Tuple		=create_dummy_object('FakeClass'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Dict		='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
             _lowerCamelCase       :		Optional[int]		=create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
             self.assertEqual(dummy_files['torch']			,   lowercase_ )


