



"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConvNextImageProcessor


class 	a__		(    unittest.TestCase  ):


   def __init__(				self   :				Tuple,  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				Tuple=7,  lowerCAmelCase   :				List[Any]=3,  lowerCAmelCase   :				int=18,  lowerCAmelCase   :				List[Any]=30,  lowerCAmelCase   :				Optional[int]=400,  lowerCAmelCase   :				int=True,  lowerCAmelCase   :				str=None,  lowerCAmelCase   :				str=0.875,  lowerCAmelCase   :				int=True,  lowerCAmelCase   :				List[Any]=[0.5, 0.5, 0.5],  lowerCAmelCase   :				str=[0.5, 0.5, 0.5],  )     ->							int:
         lowercase	:   int											= size if size is not None else {'shortest_edge': 20}
         lowercase	:   List[str]											= parent
         lowercase	:   Dict											= batch_size
         lowercase	:   Optional[int]											= num_channels
         lowercase	:   int											= image_size
         lowercase	:   int											= min_resolution
         lowercase	:   List[str]											= max_resolution
         lowercase	:   Dict											= do_resize
         lowercase	:   str											= size
         lowercase	:   Any											= crop_pct
         lowercase	:   Dict											= do_normalize
         lowercase	:   Union[str, Any]											= image_mean
         lowercase	:   Union[str, Any]											= image_std






   def 							lowercase					(				self   :				int      )     ->							Dict:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }




@require_torch
@require_vision
class 	a__		(    SCREAMING_SNAKE_CASE__,	unittest.TestCase  ):
   _lowerCamelCase       				=					ConvNextImageProcessor if is_vision_available() else None


   def 							lowercase					(				self   :				Dict      )     ->							List[Any]:
         lowercase	:   Dict											= ConvNextImageProcessingTester(self      )


   @property
   def 							lowercase					(				self   :				List[Any]      )     ->							Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()


   def 							lowercase					(				self   :				List[str]      )     ->							Tuple:
         lowercase	:   Tuple											= self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(_lowerCAmelCase,  'do_resize'      )      )
         self.assertTrue(hasattr(_lowerCAmelCase,  'size'      )      )
         self.assertTrue(hasattr(_lowerCAmelCase,  'crop_pct'      )      )
         self.assertTrue(hasattr(_lowerCAmelCase,  'do_normalize'      )      )
         self.assertTrue(hasattr(_lowerCAmelCase,  'image_mean'      )      )
         self.assertTrue(hasattr(_lowerCAmelCase,  'image_std'      )      )


   def 							lowercase					(				self   :				List[str]      )     ->							List[Any]:
         lowercase	:   Any											= self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size,  {'shortest_edge': 20}      )

         lowercase	:   Optional[Any]											= self.image_processing_class.from_dict(self.image_processor_dict,  size=42      )
         self.assertEqual(image_processor.size,  {'shortest_edge': 42}      )


   def 							lowercase					(				self   :				List[Any]      )     ->							Dict:
         pass


   def 							lowercase					(				self   :				List[Any]      )     ->							List[Any]:
         # Initialize image_processing
         lowercase	:   str											= self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         lowercase	:   str											= prepare_image_inputs(self.image_processor_tester,  equal_resolution=_lowerCAmelCase      )
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase,  Image.Image      )

         # Test not batched input
         lowercase	:   Tuple											= image_processing(image_inputs[0],  return_tensors='pt'      ).pixel_values
         self.assertEqual(
             encoded_images.shape,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),  )

         # Test batched
         lowercase	:   int											= image_processing(_lowerCAmelCase,  return_tensors='pt'      ).pixel_values
         self.assertEqual(
             encoded_images.shape,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),  )


   def 							lowercase					(				self   :				Optional[Any]      )     ->							Tuple:
         # Initialize image_processing
         lowercase	:   List[Any]											= self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         lowercase	:   Any											= prepare_image_inputs(self.image_processor_tester,  equal_resolution=_lowerCAmelCase,  numpify=_lowerCAmelCase      )
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase,  np.ndarray      )

         # Test not batched input
         lowercase	:   Dict											= image_processing(image_inputs[0],  return_tensors='pt'      ).pixel_values
         self.assertEqual(
             encoded_images.shape,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),  )

         # Test batched
         lowercase	:   Optional[int]											= image_processing(_lowerCAmelCase,  return_tensors='pt'      ).pixel_values
         self.assertEqual(
             encoded_images.shape,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),  )






   def 							lowercase					(				self   :				str      )     ->							Optional[Any]:
         # Initialize image_processing
         lowercase	:   Union[str, Any]											= self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         lowercase	:   str											= prepare_image_inputs(self.image_processor_tester,  equal_resolution=_lowerCAmelCase,  torchify=_lowerCAmelCase      )
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase,  torch.Tensor      )

         # Test not batched input
         lowercase	:   Optional[int]											= image_processing(image_inputs[0],  return_tensors='pt'      ).pixel_values
         self.assertEqual(
             encoded_images.shape,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),  )

         # Test batched
         lowercase	:   Any											= image_processing(_lowerCAmelCase,  return_tensors='pt'      ).pixel_values
         self.assertEqual(
             encoded_images.shape,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),  )


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:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





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


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		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']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



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

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

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

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		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'  ,      )
