



"""simple docstring"""




import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					Tuple     				=	get_tests_dir('fixtures/test_sentencepiece.model')


@require_torch
@require_torchaudio
@require_sentencepiece
class 	a__		(    unittest.TestCase  ):


   def 							lowercase					(				self   :				int      )     ->							int:
         lowercase	:   str											= tempfile.mkdtemp()

         lowercase	:   Optional[int]											= ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         lowercase	:   int											= dict(zip(lowerCAmelCase,  range(len(lowerCAmelCase      )      )      )      )
         lowercase	:   List[Any]											= Path(self.tmpdirname      )
         save_json(lowerCAmelCase,  save_dir / VOCAB_FILES_NAMES['vocab_file']      )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
               copyfile(lowerCAmelCase,  save_dir / VOCAB_FILES_NAMES['spm_file']      )

         lowercase	:   Any											= SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
         tokenizer.save_pretrained(self.tmpdirname      )

         lowercase	:   List[Any]											= {
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 16000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(lowerCAmelCase,  save_dir / FEATURE_EXTRACTOR_NAME      )


   def 							lowercase					(				self   :				Dict,  **lowerCAmelCase   :				Tuple      )     ->							Tuple:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname,  **lowerCAmelCase      )


   def 							lowercase					(				self   :				Optional[int],  **lowerCAmelCase   :				List[str]      )     ->							Optional[Any]:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname,  **lowerCAmelCase      )


   def 							lowercase					(				self   :				List[Any]      )     ->							Union[str, Any]:
         shutil.rmtree(self.tmpdirname      )


   def 							lowercase					(				self   :				str      )     ->							Optional[int]:
         lowercase	:   Any											= self.get_tokenizer()
         lowercase	:   Optional[int]											= self.get_feature_extractor()

         lowercase	:   Tuple											= SpeechaTextProcessor(tokenizer=lowerCAmelCase,  feature_extractor=lowerCAmelCase      )

         processor.save_pretrained(self.tmpdirname      )
         lowercase	:   List[Any]											= SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

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

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


   def 							lowercase					(				self   :				int      )     ->							int:
         lowercase	:   Optional[int]											= SpeechaTextProcessor(
             tokenizer=self.get_tokenizer(),  feature_extractor=self.get_feature_extractor()      )
         processor.save_pretrained(self.tmpdirname      )

         lowercase	:   Optional[Any]											= self.get_tokenizer(bos_token='(BOS)',  eos_token='(EOS)'      )
         lowercase	:   Optional[Any]											= self.get_feature_extractor(do_normalize=lowerCAmelCase,  padding_value=1.0      )

         lowercase	:   Union[str, Any]											= SpeechaTextProcessor.from_pretrained(
             self.tmpdirname,  bos_token='(BOS)',  eos_token='(EOS)',  do_normalize=lowerCAmelCase,  padding_value=1.0      )

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

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


   def 							lowercase					(				self   :				Any      )     ->							Optional[Any]:
         lowercase	:   Optional[int]											= self.get_feature_extractor()
         lowercase	:   Any											= self.get_tokenizer()

         lowercase	:   Dict											= SpeechaTextProcessor(tokenizer=lowerCAmelCase,  feature_extractor=lowerCAmelCase      )

         lowercase	:   List[Any]											= floats_list((3, 1000)      )

         lowercase	:   Any											= feature_extractor(lowerCAmelCase,  return_tensors='np'      )
         lowercase	:   Union[str, Any]											= processor(lowerCAmelCase,  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 							lowercase					(				self   :				Tuple      )     ->							Union[str, Any]:
         lowercase	:   Optional[Any]											= self.get_feature_extractor()
         lowercase	:   Dict											= self.get_tokenizer()

         lowercase	:   Tuple											= SpeechaTextProcessor(tokenizer=lowerCAmelCase,  feature_extractor=lowerCAmelCase      )

         lowercase	:   str											= 'This is a test string'

         lowercase	:   Tuple											= processor(text=lowerCAmelCase      )

         lowercase	:   Optional[Any]											= tokenizer(lowerCAmelCase      )

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


   def 							lowercase					(				self   :				Union[str, Any]      )     ->							List[str]:
         lowercase	:   Optional[int]											= self.get_feature_extractor()
         lowercase	:   str											= self.get_tokenizer()

         lowercase	:   str											= SpeechaTextProcessor(tokenizer=lowerCAmelCase,  feature_extractor=lowerCAmelCase      )

         lowercase	:   Any											= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowercase	:   Dict											= processor.batch_decode(lowerCAmelCase      )
         lowercase	:   Dict											= tokenizer.batch_decode(lowerCAmelCase      )

         self.assertListEqual(lowerCAmelCase,  lowerCAmelCase      )






   def 							lowercase					(				self   :				Tuple      )     ->							Any:
         lowercase	:   List[Any]											= self.get_feature_extractor()
         lowercase	:   Dict											= self.get_tokenizer()

         lowercase	:   Tuple											= SpeechaTextProcessor(tokenizer=lowerCAmelCase,  feature_extractor=lowerCAmelCase      )

         self.assertListEqual(
             processor.model_input_names,  feature_extractor.model_input_names,  msg='`processor` and `feature_extractor` model input names do not match',  )





"""simple docstring"""




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image


class 	a__		(    unittest.TestCase,	SCREAMING_SNAKE_CASE__  ):


   def 							lowercase					(				self   :				Dict      )     ->							List[str]:
         lowercase	:   str											= load_tool('image-captioning'      )
         self.tool.setup()
         lowercase	:   List[str]											= load_tool('image-captioning',  remote=lowerCAmelCase      )


   def 							lowercase					(				self   :				str      )     ->							int:
         lowercase	:   Union[str, Any]											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   List[str]											= self.tool(lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )


   def 							lowercase					(				self   :				str      )     ->							List[str]:
         lowercase	:   str											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   Tuple											= self.remote_tool(lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )


   def 							lowercase					(				self   :				Tuple      )     ->							Union[str, Any]:
         lowercase	:   Tuple											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   Union[str, Any]											= self.tool(image=lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )






   def 							lowercase					(				self   :				str      )     ->							Optional[int]:
         lowercase	:   List[str]											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   str											= self.remote_tool(image=lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )
