


"""simple docstring"""
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					int   =		"""laion/clap-htsat-unfused"""
      _lowerCamelCase	:					Union[str, Any]   =		tempfile.mkdtemp()





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Any:
      return RobertaTokenizer.from_pretrained(self.checkpoint      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  int:
      return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      shutil.rmtree(self.tmpdirname)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		self.get_tokenizer()
      _lowerCamelCase	:					List[Any]   =		self.get_feature_extractor()

      _lowerCamelCase	:					str   =		ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   feature_extractor=SCREAMING_SNAKE_CASE)

      processor.save_pretrained(self.tmpdirname)
      _lowerCamelCase	:					Union[str, Any]   =		ClapProcessor.from_pretrained(self.tmpdirname)

      self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer.get_vocab())
      self.assertIsInstance(processor.tokenizer      ,   SCREAMING_SNAKE_CASE)

      self.assertEqual(processor.feature_extractor.to_json_string()      ,   feature_extractor.to_json_string())
      self.assertIsInstance(processor.feature_extractor      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					List[Any]   =		ClapProcessor(tokenizer=self.get_tokenizer()      ,   feature_extractor=self.get_feature_extractor())
      processor.save_pretrained(self.tmpdirname)

      _lowerCamelCase	:					Optional[int]   =		self.get_tokenizer(bos_token="""(BOS)"""      ,   eos_token="""(EOS)""")
      _lowerCamelCase	:					List[str]   =		self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

      _lowerCamelCase	:					str   =		ClapProcessor.from_pretrained(
          self.tmpdirname      ,   bos_token="""(BOS)"""      ,   eos_token="""(EOS)"""      ,   do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

      self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab())
      self.assertIsInstance(processor.tokenizer      ,   SCREAMING_SNAKE_CASE)

      self.assertEqual(processor.feature_extractor.to_json_string()      ,   feature_extractor_add_kwargs.to_json_string())
      self.assertIsInstance(processor.feature_extractor      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		self.get_feature_extractor()
      _lowerCamelCase	:					Any   =		self.get_tokenizer()

      _lowerCamelCase	:					Optional[Any]   =		ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   feature_extractor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		floats_list((3, 1000))

      _lowerCamelCase	:					Tuple   =		feature_extractor(SCREAMING_SNAKE_CASE      ,   return_tensors="""np""")
      _lowerCamelCase	:					Any   =		processor(audios=SCREAMING_SNAKE_CASE      ,   return_tensors="""np""")

      for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					Union[str, Any]   =		self.get_feature_extractor()
      _lowerCamelCase	:					Optional[Any]   =		self.get_tokenizer()

      _lowerCamelCase	:					Tuple   =		ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   feature_extractor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		"""This is a test string"""

      _lowerCamelCase	:					Union[str, Any]   =		processor(text=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		tokenizer(SCREAMING_SNAKE_CASE)

      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key])





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[Any]   =		self.get_feature_extractor()
      _lowerCamelCase	:					Tuple   =		self.get_tokenizer()

      _lowerCamelCase	:					Any   =		ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   feature_extractor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _lowerCamelCase	:					int   =		processor.batch_decode(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE)

      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Any   =		self.get_feature_extractor()
      _lowerCamelCase	:					List[str]   =		self.get_tokenizer()

      _lowerCamelCase	:					str   =		ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   feature_extractor=SCREAMING_SNAKE_CASE)

      self.assertListEqual(
          processor.model_input_names[2:]      ,   feature_extractor.model_input_names      ,   msg="""`processor` and `feature_extractor` model input names do not match"""      ,   )





"""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__				:str			   =	logging.getLogger()





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	'''\n'''.join(snake_case__		)
			Path(snake_case__		).open('''w'''		).writelines(snake_case__		)


UpperCamelCase__				:Dict			   =	"""patrickvonplaten/t5-tiny-random"""
UpperCamelCase__				:List[Any]			   =	"""sshleifer/bart-tiny-random"""
UpperCamelCase__				:str			   =	"""sshleifer/tiny-mbart"""

UpperCamelCase__				: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   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



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

					_UpperCamelCase					:str					     =	str(Path(self.get_auto_remove_tmp_dir()   ) / '''scores.json'''   )
					_UpperCamelCase					:Any					     =	'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					_UpperCamelCase					:Union[str, 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(SCREAMING_SNAKE_CASE__					,				'''argv'''					,				SCREAMING_SNAKE_CASE__   ):
								run_generate()
								assert Path(SCREAMING_SNAKE_CASE__   ).exists()
								# os.remove(Path(output_file_name))
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )
		@parameterized.expand([BART_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )





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

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	Path(self.get_auto_remove_tmp_dir()   ) / '''utest_input.source'''
					_UpperCamelCase					:Any					     =	input_file_name.parent / '''utest_output.txt'''
					assert not output_file_name.exists()

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

