

'''simple docstring'''






import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin






@require_phonemizer
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			WavaVecaPhonemeCTCTokenizer
  SCREAMING_SNAKE_CASE    :  List[str]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   super().setUp()

   __lowercase							  = (
       '''<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '''
       '''ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '''
       '''ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '''
       '''oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '''
       '''pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '''
       '''yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '''
       '''əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '''
       '''ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '''
       '''ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '''
       '''uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '''
       '''ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '''
       '''ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '''
       '''ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'''
   ).split(''' '''			)
   __lowercase							  = dict(zip(lowercase__					,range(len(lowercase__			)			)			)			)

   __lowercase							  = {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     Optional[Any]					,lowercase__   :     List[str]=False					,lowercase__   :     Optional[int]=2_0					,lowercase__   :     Dict=5			):
   __lowercase							  = [(i, tokenizer.decode([i]					,clean_up_tokenization_spaces=lowercase__			)) for i in range(len(lowercase__			)			)]
   __lowercase							  = list(filter(lambda lowercase__			: [t[0]] == tokenizer.encode(t[1]					,do_phonemize=lowercase__			)					,lowercase__			)			)
   if max_length is not None and len(lowercase__			) > max_length:
    __lowercase							  = toks[:max_length]
   if min_length is not None and len(lowercase__			) < min_length and len(lowercase__			) > 0:
    while len(lowercase__			) < min_length:
     __lowercase							  = toks + toks
        # toks_str = [t[1] for t in toks]
   __lowercase							  = [t[0] for t in toks]

   # Ensure consistency
   __lowercase							  = tokenizer.decode(lowercase__					,clean_up_tokenization_spaces=lowercase__			)
   if " " not in output_txt and len(lowercase__			) > 1:
    __lowercase							  = (
        tokenizer.decode([toks_ids[0]]					,clean_up_tokenization_spaces=lowercase__			)
        + ''' '''
        + tokenizer.decode(toks_ids[1:]					,clean_up_tokenization_spaces=lowercase__			)
    )
   if with_prefix_space:
    __lowercase							  = ''' ''' + output_txt
   __lowercase							  = tokenizer.encode(lowercase__					,add_special_tokens=lowercase__			)
   return output_txt, output_ids






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,**lowercase__   :     Optional[int]			):
   kwargs.update(self.special_tokens_map			)
   return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

   # check adding a single token
   tokenizer.add_tokens('''xxx'''			)
   __lowercase							  = tokenizer('''m xxx ɪ'''					,do_phonemize=lowercase__			).input_ids
   self.assertEqual(lowercase__					,[1_3, 3_9_2, 1_7]			)  # xxx should be last token

   tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc''']			)
   __lowercase							  = tokenizer('''m aaa ɪ ccc'''					,do_phonemize=lowercase__			).input_ids
   self.assertEqual(lowercase__					,[1_3, 3_9_3, 1_7, 3_9_5]			)  # aaa and ccc should be after xxx and 2 after aaa

   __lowercase							  = tokenizer('''maɪ c'''					,do_phonemize=lowercase__			).input_ids
   self.assertEqual(lowercase__					,[3, 2_0_0]			)  # mai should be <unk> (=3)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

   __lowercase							  = '''Hello how are you'''
   __lowercase							  = tokenizer.phonemize(lowercase__					,phonemizer_lang='''en-us'''			)
   self.assertEqual(lowercase__					,'''h ə l oʊ h aʊ ɑːɹ j uː'''			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

   __lowercase							  = '''Hello how are you'''
   __lowercase							  = tokenizer.phonemize(lowercase__					,phonemizer_lang='''en-us'''			)
   self.assertEqual(tokenizer(lowercase__			).input_ids					,tokenizer(lowercase__					,do_phonemize=lowercase__			).input_ids			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)
   __lowercase							  = '''Hello how are you'''
   __lowercase							  = tokenizer.phonemize(lowercase__					,phonemizer_lang='''en-us'''			)

   __lowercase							  = tokenizer.decode(tokenizer(lowercase__			).input_ids			)

   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

   __lowercase							  = [
       [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
       [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
   ]
   __lowercase							  = tokenizer.decode(sample_ids[0]			)
   __lowercase							  = tokenizer.batch_decode(lowercase__			)
   self.assertEqual(lowercase__					,batch_tokens[0]			)
   self.assertEqual(lowercase__					,['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.tokenizer_class.from_pretrained(
       '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,word_delimiter_token='''|'''			)
   tokenizer.add_tokens('''|'''			)

   __lowercase							  = '''Hello how are you'''
   __lowercase							  = tokenizer.phonemize(lowercase__					,phonemizer_lang='''en-us'''			)
   self.assertEqual(lowercase__					,'''h ə l oʊ | h aʊ | ɑːɹ | j uː |'''			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = self.tokenizer_class.from_pretrained(
       '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,word_delimiter_token='''|'''			)
   tokenizer.add_tokens('''|'''			)

   __lowercase							  = '''Hello how are you'''
   __lowercase							  = tokenizer.phonemize(lowercase__					,phonemizer_lang='''en-us'''			)
   self.assertEqual(tokenizer(lowercase__			).input_ids					,tokenizer(lowercase__					,do_phonemize=lowercase__			).input_ids			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.tokenizer_class.from_pretrained(
       '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,word_delimiter_token='''|'''			)
   tokenizer.add_tokens('''|'''			)

   # fmt: off
   __lowercase							  = [
       [1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
       [tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
   ]
   # fmt: on

   # decode with word_del_token filter
   __lowercase							  = tokenizer.decode(sample_ids[0]			)
   __lowercase							  = tokenizer.batch_decode(lowercase__			)
   self.assertEqual(lowercase__					,batch_tokens[0]			)
   self.assertEqual(lowercase__					,['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']			)

   # decode with no word_del_token filter
   __lowercase							  = tokenizer.decode(sample_ids[0]					,filter_word_delimiter_token=lowercase__			)
   __lowercase							  = tokenizer.batch_decode(lowercase__					,filter_word_delimiter_token=lowercase__			)
   self.assertEqual(lowercase__					,batch_tokens[0]			)
   self.assertEqual(lowercase__					,['''k s ɾ | ɾ l | ɭʲ''', '''| j ð | s j ð s oːɹ''']			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.tokenizer_class.from_pretrained(
       '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,word_delimiter_token='''|'''			)
   tokenizer.add_tokens('''|'''			)

   __lowercase							  = '''Hello how are you'''
   __lowercase							  = tokenizer.phonemize(lowercase__					,phonemizer_lang='''en-us'''			)

   __lowercase							  = tokenizer.decode(tokenizer(lowercase__			).input_ids					,filter_word_delimiter_token=lowercase__			)

   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = self.tokenizer_class.from_pretrained(
       '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,word_delimiter_token='''|'''			)
   tokenizer.add_tokens('''|'''			)

   __lowercase							  = '''Hello how are you'''
   __lowercase							  = tokenizer.phonemize(lowercase__					,phonemizer_lang='''en-us'''			)

   __lowercase							  = tokenizer.decode(tokenizer(lowercase__			).input_ids					,filter_word_delimiter_token=lowercase__			)

   self.assertEqual(''' '''.join([p.strip() for p in phonemes.split(''' |'''			)]			).strip()					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = self.tokenizer_class.from_pretrained(
       '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,word_delimiter_token=lowercase__			)
   __lowercase							  = '''Hello how are you'''

   __lowercase							  = tokenizer(lowercase__					,phonemizer_lang='''en-us'''			).input_ids
   __lowercase							  = tokenizer(lowercase__					,phonemizer_lang='''fr-fr'''			).input_ids

   self.assertNotEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokenizer.decode(lowercase__			)
   __lowercase							  = tokenizer.decode(lowercase__			)

   self.assertEqual(lowercase__					,'''h ə l oʊ h aʊ ɑːɹ j uː'''			)
   self.assertEqual(lowercase__					,'''ɛ l o h aʊ a ʁ j u'''			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)
   __lowercase							  = '''Hello how Are you'''
   __lowercase							  = '''hello how are you'''

   __lowercase							  = tokenizer(lowercase__			).input_ids
   __lowercase							  = tokenizer(lowercase__			).input_ids

   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)
   tokenizer.add_tokens(['''!''', '''?''']			)
   tokenizer.add_special_tokens({'''cls_token''': '''$$$'''}			)

   # fmt: off
   __lowercase							  = [
       [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
       [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
   ]
   # fmt: on

   __lowercase							  = tokenizer.batch_decode(lowercase__			)
   self.assertEqual(lowercase__					,['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$''']			)






  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     Union[str, Any]					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = [d[key] for d in offsets]
   return retrieved_list






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.get_tokenizer(word_delimiter_token='''|'''			)
   tokenizer.add_tokens('''|'''			)

   # fmt: off
   # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
   __lowercase							  = [1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
   # fmt: on

   __lowercase							  = tokenizer.decode(lowercase__					,output_char_offsets=lowercase__					,filter_word_delimiter_token=lowercase__			)
   # check Wav2Vec2CTCTokenizerOutput keys for char
   self.assertEqual(len(outputs.keys()			)					,2			)
   self.assertTrue('''text''' in outputs			)
   self.assertTrue('''char_offsets''' in outputs			)
   self.assertTrue(isinstance(lowercase__					,lowercase__			)			)

   # check that order of chars is correct and identical for both outputs
   self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''char_offsets''']					,'''char'''			)			)					,outputs.text			)
   self.assertListEqual(
       self.get_from_offsets(outputs['''char_offsets''']					,'''char'''			)					,['''k''', '''s''', '''ɾ''', '''ɾ''', '''|''', '''ɾ''', '''l''', '''|''', '''ɭʲ''']			)

   # check that offsets are actually correct for char
   # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
   # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
   self.assertListEqual(
       self.get_from_offsets(outputs['''char_offsets''']					,'''start_offset'''			)					,[0, 1, 4, 7, 9, 1_1, 1_2, 1_5, 1_6]			)
   self.assertListEqual(
       self.get_from_offsets(outputs['''char_offsets''']					,'''end_offset'''			)					,[1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_tokenizer(word_delimiter_token='''|'''			)

   def check_list_tuples_equal(lowercase__   :     List[Any]					,lowercase__   :     Tuple			):
    self.assertTrue(isinstance(lowercase__					,lowercase__			)			)
    self.assertTrue(isinstance(outputs_list[0]					,lowercase__			)			)

    # transform list to ModelOutput
    __lowercase							  = WavaVecaPhonemeCTCTokenizerOutput(
        {k: [d[k] for d in outputs_list] for k in outputs_list[0]}			)

    self.assertListEqual(outputs_batch['''text''']					,outputs_batch_a['''text''']			)

    def recursive_check(lowercase__   :     Dict					,lowercase__   :     Tuple			):
     if isinstance(lowercase__					,lowercase__			):
      [recursive_check(lowercase__					,lowercase__			) for la, la in zip(lowercase__					,lowercase__			)]
     self.assertEqual(lowercase__					,lowercase__			)

    if "char_offsets" in outputs_batch:
     recursive_check(outputs_batch['''char_offsets''']					,outputs_batch_a['''char_offsets''']			)

        # fmt: off
   __lowercase							  = [
       [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
       [2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
   ]
   # fmt: on

   # We assume that `decode` works as expected. All we will check now is
   # the output type is correct and the output is identical to `decode`

   # char
   __lowercase							  = tokenizer.batch_decode(lowercase__					,output_char_offsets=lowercase__			)
   __lowercase							  = [tokenizer.decode(lowercase__					,output_char_offsets=lowercase__			) for ids in sample_ids]
   check_list_tuples_equal(lowercase__					,lowercase__			)






  @unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   pass






  @unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   pass






  @unittest.skip('''encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   pass






  @unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_tokenizers(do_lower_case=lowercase__			)
   for tokenizer in tokenizers:
    with self.subTest(F"{tokenizer.__class__.__name__}"			):
     __lowercase							  = tokenizer.vocab_size
     __lowercase							  = len(lowercase__			)

     self.assertNotEqual(lowercase__					,0			)

     # We usually have added tokens from the start in tests because our vocab fixtures are
     # smaller than the original vocabs - let's not assert this
     # self.assertEqual(vocab_size, all_size)

     __lowercase							  = ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
     __lowercase							  = tokenizer.add_tokens(lowercase__			)
     __lowercase							  = tokenizer.vocab_size
     __lowercase							  = len(lowercase__			)

     self.assertNotEqual(lowercase__					,0			)
     self.assertEqual(lowercase__					,lowercase__			)
     self.assertEqual(lowercase__					,len(lowercase__			)			)
     self.assertEqual(lowercase__					,all_size + len(lowercase__			)			)

     __lowercase							  = tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''					,add_special_tokens=lowercase__			)

     self.assertGreaterEqual(len(lowercase__			)					,4			)
     self.assertGreater(tokens[0]					,tokenizer.vocab_size - 1			)
     self.assertGreater(tokens[-3]					,tokenizer.vocab_size - 1			)

     __lowercase							  = {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
     __lowercase							  = tokenizer.add_special_tokens(lowercase__			)
     __lowercase							  = tokenizer.vocab_size
     __lowercase							  = len(lowercase__			)

     self.assertNotEqual(lowercase__					,0			)
     self.assertEqual(lowercase__					,lowercase__			)
     self.assertEqual(lowercase__					,len(lowercase__			)			)
     self.assertEqual(lowercase__					,all_size_a + len(lowercase__			)			)

     __lowercase							  = tokenizer.encode(
         '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''					,add_special_tokens=lowercase__			)

     self.assertGreaterEqual(len(lowercase__			)					,6			)
     self.assertGreater(tokens[0]					,tokenizer.vocab_size - 1			)
     self.assertGreater(tokens[0]					,tokens[1]			)
     self.assertGreater(tokens[-3]					,tokenizer.vocab_size - 1			)
     self.assertGreater(tokens[-3]					,tokens[-4]			)
     self.assertEqual(tokens[0]					,tokenizer.eos_token_id			)
     self.assertEqual(tokens[-3]					,tokenizer.pad_token_id			)






  @unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   pass






  @unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
   # is not the case for Wav2Vec2PhonemeCTCTokenizer.
   __lowercase							  = self.get_tokenizers(fast=lowercase__					,do_lower_case=lowercase__			)
   for tokenizer in tokenizers:
    with self.subTest(F"{tokenizer.__class__.__name__}"			):
     __lowercase							  = ['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
     __lowercase							  = tokenizer.convert_tokens_to_string(lowercase__			)

     self.assertIsInstance(output['''text''']					,lowercase__			)




'''simple docstring'''






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMAEForPreTraining, ViTMAEModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     str					,lowercase__   :     Tuple					,lowercase__   :     Dict=1_3					,lowercase__   :     List[str]=3_0					,lowercase__   :     Tuple=2					,lowercase__   :     Optional[int]=3					,lowercase__   :     List[str]=True					,lowercase__   :     Tuple=True					,lowercase__   :     int=3_2					,lowercase__   :     List[str]=5					,lowercase__   :     Tuple=4					,lowercase__   :     Any=3_7					,lowercase__   :     Any="gelu"					,lowercase__   :     Union[str, Any]=0.1					,lowercase__   :     Optional[int]=0.1					,lowercase__   :     str=1_0					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Union[str, Any]=3					,lowercase__   :     Optional[int]=0.6					,lowercase__   :     List[Any]=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = image_size
   __lowercase							  = patch_size
   __lowercase							  = num_channels
   __lowercase							  = is_training
   __lowercase							  = use_labels
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = mask_ratio
   __lowercase							  = scope

   # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
   # (we add 1 for the [CLS] token)
   __lowercase							  = (image_size // patch_size) ** 2
   __lowercase							  = int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

   __lowercase							  = None
   if self.use_labels:
    __lowercase							  = ids_tensor([self.batch_size]					,self.type_sequence_label_size			)

   __lowercase							  = self.get_config()

   return config, pixel_values, labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return ViTMAEConfig(
       image_size=self.image_size					,patch_size=self.patch_size					,num_channels=self.num_channels					,hidden_size=self.hidden_size					,num_hidden_layers=self.num_hidden_layers					,num_attention_heads=self.num_attention_heads					,intermediate_size=self.intermediate_size					,hidden_act=self.hidden_act					,hidden_dropout_prob=self.hidden_dropout_prob					,attention_probs_dropout_prob=self.attention_probs_dropout_prob					,is_decoder=lowercase__					,initializer_range=self.initializer_range					,mask_ratio=self.mask_ratio					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     str					,lowercase__   :     Optional[int]					,lowercase__   :     List[str]			):
   __lowercase							  = ViTMAEModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__			)
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     List[Any]					,lowercase__   :     int					,lowercase__   :     Optional[Any]			):
   __lowercase							  = ViTMAEForPreTraining(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__			)
   __lowercase							  = (self.image_size // self.patch_size) ** 2
   __lowercase							  = self.patch_size**2 * self.num_channels
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, num_patches, expected_num_channels)			)

   # test greyscale images
   __lowercase							  = 1
   __lowercase							  = ViTMAEForPreTraining(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
   __lowercase							  = model(lowercase__			)
   __lowercase							  = self.patch_size**2
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, num_patches, expected_num_channels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.prepare_config_and_inputs()
   __lowercase  ,	__lowercase  ,	__lowercase							  = config_and_inputs
   __lowercase							  = {'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE    :  Dict							=			{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			False
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = ViTMAEModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,has_text_modality=lowercase__					,hidden_size=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   self.config_tester.run_common_tests()






  @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    self.assertIsInstance(model.get_input_embeddings()					,(nn.Module)			)
    __lowercase							  = model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(lowercase__					,nn.Linear			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    __lowercase							  = inspect.signature(model.forward			)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowercase							  = [*signature.parameters.keys()]

    __lowercase							  = ['''pixel_values''']
    self.assertListEqual(arg_names[:1]					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Optional[int]					,lowercase__   :     List[str]					,lowercase__   :     Optional[Any]			):
   # make masks reproducible
   np.random.seed(2			)

   __lowercase							  = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2			)
   __lowercase							  = np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
   __lowercase							  = torch.from_numpy(lowercase__			)

   # Add `noise` argument.
   # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
   __lowercase							  = pt_noise

   super().check_pt_tf_models(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    model.to(lowercase__			)
    model.eval()
    # make random mask reproducible
    torch.manual_seed(2			)
    with torch.no_grad():
     __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)

    __lowercase							  = outputs[0].cpu().numpy()
    __lowercase							  = 0

    with tempfile.TemporaryDirectory() as tmpdirname:
     model.save_pretrained(lowercase__			)
     __lowercase							  = model_class.from_pretrained(lowercase__			)
     model.to(lowercase__			)
     # make random mask reproducible
     torch.manual_seed(2			)
     with torch.no_grad():
      __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)

     # Make sure we don't have nans
     __lowercase							  = after_outputs[0].cpu().numpy()
     __lowercase							  = 0
     __lowercase							  = np.amax(np.abs(out_a - out_a			)			)
     self.assertLessEqual(lowercase__					,1e-5			)






  @unittest.skip(
      reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   pass






  @unittest.skip(
      reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   pass






  @unittest.skip(
      reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   pass






  @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   pass






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   pass






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = ViTMAEModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
 return image






@require_torch
@require_vision
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @cached_property
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''			) if is_vision_available() else None






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # make random mask reproducible across the PT and TF model
   np.random.seed(2			)

   __lowercase							  = ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''			).to(lowercase__			)

   __lowercase							  = self.default_image_processor
   __lowercase							  = prepare_img()
   __lowercase							  = image_processor(images=lowercase__					,return_tensors='''pt'''			).to(lowercase__			)

   # prepare a noise vector that will be also used for testing the TF model
   # (this way we can ensure that the PT and TF models operate on the same inputs)
   __lowercase							  = ViTMAEConfig()
   __lowercase							  = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
   __lowercase							  = np.random.uniform(size=(1, num_patches)			)

   # forward pass
   with torch.no_grad():
    __lowercase							  = model(**lowercase__					,noise=torch.from_numpy(lowercase__			).to(device=lowercase__			)			)

   # verify the logits
   __lowercase							  = torch.Size((1, 1_9_6, 7_6_8)			)
   self.assertEqual(outputs.logits.shape					,lowercase__			)

   __lowercase							  = torch.tensor(
       [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]			)

   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]					,expected_slice.to(lowercase__			)					,atol=1e-4			)			)

