





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 ViTImageProcessor



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  def __init__(  self	,  A	,  A=7	,  A=3	,  A=18	,  A=30	,  A=4_00	,  A=True	,  A=None	,  A=True	,  A=[0.5, 0.5, 0.5]	,  A=[0.5, 0.5, 0.5]	,  )		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       size if size is not None else {'''height''': 18, '''width''': 18}
       __magic_name__				=       parent
       __magic_name__				=       batch_size
       __magic_name__				=       num_channels
       __magic_name__				=       image_size
       __magic_name__				=       min_resolution
       __magic_name__				=       max_resolution
       __magic_name__				=       do_resize
       __magic_name__				=       size
       __magic_name__				=       do_normalize
       __magic_name__				=       image_mean
       __magic_name__				=       image_std







  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__   ,							unittest.TestCase			):


  """simple docstring"""





  _a								=							ViTImageProcessor if is_vision_available() else None





  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__				=       ViTImageProcessingTester(self						)





  @property
  def 			__A      (  self						)		->   Any:





       '''simple docstring'''







       return self.image_processor_tester.prepare_image_processor_dict()





  def 			__A      (  self						)		->   Union[str, Any]:





       '''simple docstring'''







       __magic_name__				=       self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(A	,  '''image_mean'''						)						)
       self.assertTrue(hasattr(A	,  '''image_std'''						)						)
       self.assertTrue(hasattr(A	,  '''do_normalize'''						)						)
       self.assertTrue(hasattr(A	,  '''do_resize'''						)						)
       self.assertTrue(hasattr(A	,  '''size'''						)						)





  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       __magic_name__				=       self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size	,  {'''height''': 18, '''width''': 18}						)

       __magic_name__				=       self.image_processing_class.from_dict(self.image_processor_dict	,  size=42						)
       self.assertEqual(image_processor.size	,  {'''height''': 42, '''width''': 42}						)





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       pass





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       __magic_name__				=       self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       __magic_name__				=       prepare_image_inputs(self.image_processor_tester	,  equal_resolution=A						)
       for image in image_inputs:
            self.assertIsInstance(A	,  Image.Image						)

       # Test not batched input
       __magic_name__				=       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['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,  )

       # Test batched
       __magic_name__				=       image_processing(A	,  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['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,  )





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       __magic_name__				=       self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       __magic_name__				=       prepare_image_inputs(self.image_processor_tester	,  equal_resolution=A	,  numpify=A						)
       for image in image_inputs:
            self.assertIsInstance(A	,  np.ndarray						)

       # Test not batched input
       __magic_name__				=       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['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,  )

       # Test batched
       __magic_name__				=       image_processing(A	,  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['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,  )







  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       __magic_name__				=       self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       __magic_name__				=       prepare_image_inputs(self.image_processor_tester	,  equal_resolution=A	,  torchify=A						)
       for image in image_inputs:
            self.assertIsInstance(A	,  torch.Tensor						)

       # Test not batched input
       __magic_name__				=       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['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,  )

       # Test batched
       __magic_name__				=       image_processing(A	,  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['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,  )






import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       __magic_name__				=       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''						)
       __magic_name__				=       dict(zip(A	,  range(len(A						)						)						)						)

       __magic_name__				=       {
           '''pad_token''': '''<pad>''',
           '''unk_token''': '''<unk>''',
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
       }
       __magic_name__				=       {
           '''feature_size''': 1,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_60_00,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }

       __magic_name__				=       tempfile.mkdtemp()
       __magic_name__				=       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file''']						)
       __magic_name__				=       os.path.join(self.tmpdirname	,  A						)
       with open(self.vocab_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)

       with open(self.feature_extraction_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)





  def 			__A      (  self	,  **A						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.add_kwargs_tokens_map.copy()
       kwargs.update(A						)
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self	,  **A						)		->   List[Any]:





       '''simple docstring'''







       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       shutil.rmtree(self.tmpdirname						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_tokenizer()
       __magic_name__				=       self.get_feature_extractor()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       processor.save_pretrained(self.tmpdirname						)
       __magic_name__				=       WavaVecaProcessor.from_pretrained(self.tmpdirname						)

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

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





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       WavaVecaProcessor(tokenizer=self.get_tokenizer()	,  feature_extractor=self.get_feature_extractor()						)
       processor.save_pretrained(self.tmpdirname						)

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

       __magic_name__				=       WavaVecaProcessor.from_pretrained(
           self.tmpdirname	,  bos_token='''(BOS)'''	,  eos_token='''(EOS)'''	,  do_normalize=A	,  padding_value=1.0						)

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

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





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       floats_list((3, 10_00)						)

       __magic_name__				=       feature_extractor(A	,  return_tensors='''np'''						)
       __magic_name__				=       processor(A	,  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 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       '''This is a test string'''

       __magic_name__				=       processor(text=A						)

       __magic_name__				=       tokenizer(A						)

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





  def 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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

       __magic_name__				=       processor.batch_decode(A						)
       __magic_name__				=       tokenizer.batch_decode(A						)

       self.assertListEqual(A	,  A						)







  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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