
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 		UpperCamelCase__  (       __lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      WavaVecaPhonemeCTCTokenizer
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      False
   def       lowerCAmelCase			(self :					Any   ):
    super().setUp()

    __a :  Dict						=  (
        '''<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(''' '''   )
    __a :  Any						=  dict(zip(snake_case_					,				range(len(snake_case_   )   )   )   )

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

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

    # Ensure consistency
    __a :  int						=  tokenizer.decode(snake_case_					,				clean_up_tokenization_spaces=snake_case_   )
    if " " not in output_txt and len(snake_case_   ) > 1:
     __a :  str						=  (
         tokenizer.decode([toks_ids[0]]					,				clean_up_tokenization_spaces=snake_case_   )
         + ''' '''
         + tokenizer.decode(toks_ids[1:]					,				clean_up_tokenization_spaces=snake_case_   )
     )
    if with_prefix_space:
     __a :  Union[str, Any]						=  ''' ''' + output_txt
    __a :  Dict						=  tokenizer.encode(snake_case_					,				add_special_tokens=snake_case_   )
    return output_txt, output_ids
   def       lowerCAmelCase			(self :					Optional[int]					,				**snake_case_ :					Optional[int]   ):
    kwargs.update(self.special_tokens_map   )
    return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    __a :  Dict						=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''   )

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

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

    __a :  Optional[int]						=  tokenizer('''maɪ c'''					,				do_phonemize=snake_case_   ).input_ids
    self.assertEqual(snake_case_					,				[3, 2_0_0]   )  # mai should be <unk> (=3)
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  List[str]						=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''   )

    __a :  Optional[int]						=  '''Hello how are you'''
    __a :  int						=  tokenizer.phonemize(snake_case_					,				phonemizer_lang='''en-us'''   )
    self.assertEqual(snake_case_					,				'''h ə l oʊ h aʊ ɑːɹ j uː'''   )
   def       lowerCAmelCase			(self :					Any   ):
    __a :  str						=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''   )

    __a :  Tuple						=  '''Hello how are you'''
    __a :  Optional[int]						=  tokenizer.phonemize(snake_case_					,				phonemizer_lang='''en-us'''   )
    self.assertEqual(tokenizer(snake_case_   ).input_ids					,				tokenizer(snake_case_					,				do_phonemize=snake_case_   ).input_ids   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Union[str, Any]						=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''   )
    __a :  List[str]						=  '''Hello how are you'''
    __a :  Union[str, Any]						=  tokenizer.phonemize(snake_case_					,				phonemizer_lang='''en-us'''   )

    __a :  List[str]						=  tokenizer.decode(tokenizer(snake_case_   ).input_ids   )

    self.assertEqual(snake_case_					,				snake_case_   )
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Union[str, Any]						=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''   )

    __a :  List[Any]						=  [
        [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],
    ]
    __a :  Any						=  tokenizer.decode(sample_ids[0]   )
    __a :  Tuple						=  tokenizer.batch_decode(snake_case_   )
    self.assertEqual(snake_case_					,				batch_tokens[0]   )
    self.assertEqual(snake_case_					,				['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']   )
   def       lowerCAmelCase			(self :					str   ):
    __a :  Optional[int]						=  self.tokenizer_class.from_pretrained(
        '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,				word_delimiter_token='''|'''   )
    tokenizer.add_tokens('''|'''   )

    __a :  Union[str, Any]						=  '''Hello how are you'''
    __a :  Tuple						=  tokenizer.phonemize(snake_case_					,				phonemizer_lang='''en-us'''   )
    self.assertEqual(snake_case_					,				'''h ə l oʊ | h aʊ | ɑːɹ | j uː |'''   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    __a :  str						=  self.tokenizer_class.from_pretrained(
        '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,				word_delimiter_token='''|'''   )
    tokenizer.add_tokens('''|'''   )

    __a :  str						=  '''Hello how are you'''
    __a :  Dict						=  tokenizer.phonemize(snake_case_					,				phonemizer_lang='''en-us'''   )
    self.assertEqual(tokenizer(snake_case_   ).input_ids					,				tokenizer(snake_case_					,				do_phonemize=snake_case_   ).input_ids   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Tuple						=  self.tokenizer_class.from_pretrained(
        '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,				word_delimiter_token='''|'''   )
    tokenizer.add_tokens('''|'''   )

    # fmt: off
    __a :  str						=  [
        [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
    __a :  Optional[Any]						=  tokenizer.decode(sample_ids[0]   )
    __a :  Any						=  tokenizer.batch_decode(snake_case_   )
    self.assertEqual(snake_case_					,				batch_tokens[0]   )
    self.assertEqual(snake_case_					,				['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']   )

    # decode with no word_del_token filter
    __a :  str						=  tokenizer.decode(sample_ids[0]					,				filter_word_delimiter_token=snake_case_   )
    __a :  str						=  tokenizer.batch_decode(snake_case_					,				filter_word_delimiter_token=snake_case_   )
    self.assertEqual(snake_case_					,				batch_tokens[0]   )
    self.assertEqual(snake_case_					,				['''k s ɾ | ɾ l | ɭʲ''', '''| j ð | s j ð s oːɹ''']   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    __a :  Optional[Any]						=  self.tokenizer_class.from_pretrained(
        '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,				word_delimiter_token='''|'''   )
    tokenizer.add_tokens('''|'''   )

    __a :  str						=  '''Hello how are you'''
    __a :  Optional[Any]						=  tokenizer.phonemize(snake_case_					,				phonemizer_lang='''en-us'''   )

    __a :  Union[str, Any]						=  tokenizer.decode(tokenizer(snake_case_   ).input_ids					,				filter_word_delimiter_token=snake_case_   )

    self.assertEqual(snake_case_					,				snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  int						=  self.tokenizer_class.from_pretrained(
        '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,				word_delimiter_token='''|'''   )
    tokenizer.add_tokens('''|'''   )

    __a :  List[str]						=  '''Hello how are you'''
    __a :  Any						=  tokenizer.phonemize(snake_case_					,				phonemizer_lang='''en-us'''   )

    __a :  List[Any]						=  tokenizer.decode(tokenizer(snake_case_   ).input_ids					,				filter_word_delimiter_token=snake_case_   )

    self.assertEqual(''' '''.join([p.strip() for p in phonemes.split(''' |'''   )]   ).strip()					,				snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    __a :  Any						=  self.tokenizer_class.from_pretrained(
        '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,				word_delimiter_token=snake_case_   )
    __a :  Optional[Any]						=  '''Hello how are you'''

    __a :  Union[str, Any]						=  tokenizer(snake_case_					,				phonemizer_lang='''en-us'''   ).input_ids
    __a :  Any						=  tokenizer(snake_case_					,				phonemizer_lang='''fr-fr'''   ).input_ids

    self.assertNotEqual(snake_case_					,				snake_case_   )

    __a :  Tuple						=  tokenizer.decode(snake_case_   )
    __a :  Any						=  tokenizer.decode(snake_case_   )

    self.assertEqual(snake_case_					,				'''h ə l oʊ h aʊ ɑːɹ j uː'''   )
    self.assertEqual(snake_case_					,				'''ɛ l o h aʊ a ʁ j u'''   )
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  List[Any]						=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''   )
    __a :  str						=  '''Hello how Are you'''
    __a :  Union[str, Any]						=  '''hello how are you'''

    __a :  Optional[Any]						=  tokenizer(snake_case_   ).input_ids
    __a :  str						=  tokenizer(snake_case_   ).input_ids

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

    # fmt: off
    __a :  List[Any]						=  [
        [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

    __a :  str						=  tokenizer.batch_decode(snake_case_   )
    self.assertEqual(snake_case_					,				['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$''']   )
   @staticmethod
   def       lowerCAmelCase			(snake_case_ :					List[str]					,				snake_case_ :					Optional[Any]   ):
    __a :  List[Any]						=  [d[key] for d in offsets]
    return retrieved_list
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    __a :  Dict						=  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"
    __a :  List[str]						=  [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

    __a :  List[Any]						=  tokenizer.decode(snake_case_					,				output_char_offsets=snake_case_					,				filter_word_delimiter_token=snake_case_   )
    # 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(snake_case_					,				snake_case_   )   )

    # 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       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  str						=  self.get_tokenizer(word_delimiter_token='''|'''   )

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

     # transform list to ModelOutput
     __a :  Tuple						=  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(snake_case_ :					Optional[int]					,				snake_case_ :					List[Any]   ):
      if isinstance(snake_case_					,				snake_case_   ):
       [recursive_check(snake_case_					,				snake_case_   ) for la, la in zip(snake_case_					,				snake_case_   )]
      self.assertEqual(snake_case_					,				snake_case_   )

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

        # fmt: off
    __a :  Union[str, Any]						=  [
        [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
    __a :  Dict						=  tokenizer.batch_decode(snake_case_					,				output_char_offsets=snake_case_   )
    __a :  List[str]						=  [tokenizer.decode(snake_case_					,				output_char_offsets=snake_case_   ) for ids in sample_ids]
    check_list_tuples_equal(snake_case_					,				snake_case_   )
   @unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'''   )
   def       lowerCAmelCase			(self :					List[str]   ):
    pass
   @unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'''   )
   def       lowerCAmelCase			(self :					int   ):
    pass
   @unittest.skip('''encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency'''   )
   def       lowerCAmelCase			(self :					Tuple   ):
    pass
   @unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing'''   )
   def       lowerCAmelCase			(self :					Tuple   ):
    pass
   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  str						=  self.get_tokenizers(do_lower_case=snake_case_   )
    for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"   ):
      __a :  Dict						=  tokenizer.vocab_size
      __a :  Dict						=  len(snake_case_   )

      self.assertNotEqual(snake_case_					,				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)

      __a :  str						=  ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
      __a :  Any						=  tokenizer.add_tokens(snake_case_   )
      __a :  Optional[Any]						=  tokenizer.vocab_size
      __a :  Optional[int]						=  len(snake_case_   )

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

      __a :  Any						=  tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''					,				add_special_tokens=snake_case_   )

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

      __a :  Union[str, Any]						=  {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
      __a :  Optional[int]						=  tokenizer.add_special_tokens(snake_case_   )
      __a :  str						=  tokenizer.vocab_size
      __a :  List[Any]						=  len(snake_case_   )

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

      __a :  Union[str, Any]						=  tokenizer.encode(
          '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''					,				add_special_tokens=snake_case_   )

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







   def       lowerCAmelCase			(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.
    __a :  int						=  self.get_tokenizers(fast=snake_case_					,				do_lower_case=snake_case_   )
    for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"   ):
      __a :  List[str]						=  ['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
      __a :  Dict						=  tokenizer.convert_tokens_to_string(snake_case_   )

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


import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertModel,
      )



class 		UpperCamelCase__  :
   def __init__(self :					List[Any]					,				snake_case_ :					int					,				snake_case_ :					List[str]=1_3					,				snake_case_ :					Tuple=7					,				snake_case_ :					List[Any]=True					,				snake_case_ :					List[Any]=True					,				snake_case_ :					Dict=True					,				snake_case_ :					Optional[int]=True					,				snake_case_ :					str=9_9					,				snake_case_ :					Dict=6_4					,				snake_case_ :					Any=3_2					,				snake_case_ :					str=5					,				snake_case_ :					int=4					,				snake_case_ :					List[Any]=3_7					,				snake_case_ :					Any="gelu"					,				snake_case_ :					Dict=0.1					,				snake_case_ :					List[str]=0.1					,				snake_case_ :					str=5_1_2					,				snake_case_ :					Any=1_6					,				snake_case_ :					str=2					,				snake_case_ :					int=0.02					,				snake_case_ :					Union[str, Any]=3					,				snake_case_ :					Optional[Any]=4					,				snake_case_ :					List[Any]=None					,				):
    __a :  Any						=  parent
    __a :  Optional[int]						=  batch_size
    __a :  Any						=  seq_length
    __a :  int						=  is_training
    __a :  Optional[int]						=  use_input_mask
    __a :  List[Any]						=  use_token_type_ids
    __a :  Dict						=  use_labels
    __a :  Tuple						=  vocab_size
    __a :  str						=  hidden_size
    __a :  List[Any]						=  embedding_size
    __a :  List[Any]						=  num_hidden_layers
    __a :  str						=  num_attention_heads
    __a :  str						=  intermediate_size
    __a :  Union[str, Any]						=  hidden_act
    __a :  Optional[Any]						=  hidden_dropout_prob
    __a :  Tuple						=  attention_probs_dropout_prob
    __a :  Union[str, Any]						=  max_position_embeddings
    __a :  Any						=  type_vocab_size
    __a :  int						=  type_sequence_label_size
    __a :  int						=  initializer_range
    __a :  int						=  num_labels
    __a :  Union[str, Any]						=  num_choices
    __a :  Dict						=  scope
   def       lowerCAmelCase			(self :					str   ):
    __a :  int						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

    __a :  List[Any]						=  None
    if self.use_input_mask:
     __a :  Dict						=  random_attention_mask([self.batch_size, self.seq_length]   )

    __a :  Optional[Any]						=  None
    if self.use_token_type_ids:
     __a :  List[Any]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size   )

    __a :  Dict						=  None
    __a :  List[str]						=  None
    __a :  Optional[Any]						=  None
    if self.use_labels:
     __a :  str						=  ids_tensor([self.batch_size]					,				self.type_sequence_label_size   )
     __a :  Dict						=  ids_tensor([self.batch_size, self.seq_length]					,				self.num_labels   )
     __a :  Union[str, Any]						=  ids_tensor([self.batch_size]					,				self.num_choices   )

    __a :  Optional[int]						=  self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def       lowerCAmelCase			(self :					int   ):
    return MobileBertConfig(
        vocab_size=self.vocab_size					,				hidden_size=self.hidden_size					,				num_hidden_layers=self.num_hidden_layers					,				num_attention_heads=self.num_attention_heads					,				intermediate_size=self.intermediate_size					,				embedding_size=self.embedding_size					,				hidden_act=self.hidden_act					,				hidden_dropout_prob=self.hidden_dropout_prob					,				attention_probs_dropout_prob=self.attention_probs_dropout_prob					,				max_position_embeddings=self.max_position_embeddings					,				type_vocab_size=self.type_vocab_size					,				is_decoder=snake_case_					,				initializer_range=self.initializer_range					,				)
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[str]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					Any   ):
    __a :  Any						=  MobileBertModel(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[str]						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  model(snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  model(snake_case_   )

    self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)   )
    self.parent.assertEqual(result.pooler_output.shape					,				(self.batch_size, self.hidden_size)   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Tuple					,				snake_case_ :					Dict					,				snake_case_ :					Tuple					,				snake_case_ :					str					,				snake_case_ :					List[Any]   ):
    __a :  str						=  MobileBertForMaskedLM(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					Dict   ):
    __a :  Optional[Any]						=  MobileBertForNextSentencePrediction(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  int						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				)
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, 2)   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]					,				snake_case_ :					Any					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]   ):
    __a :  str						=  MobileBertForPreTraining(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Union[str, Any]						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				next_sentence_label=snake_case_					,				)
    self.parent.assertEqual(result.prediction_logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
    self.parent.assertEqual(result.seq_relationship_logits.shape					,				(self.batch_size, 2)   )
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					str					,				snake_case_ :					str   ):
    __a :  str						=  MobileBertForQuestionAnswering(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Optional[Any]						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				start_positions=snake_case_					,				end_positions=snake_case_					,				)
    self.parent.assertEqual(result.start_logits.shape					,				(self.batch_size, self.seq_length)   )
    self.parent.assertEqual(result.end_logits.shape					,				(self.batch_size, self.seq_length)   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					Any					,				snake_case_ :					List[str]					,				snake_case_ :					List[Any]					,				snake_case_ :					str					,				snake_case_ :					Any					,				snake_case_ :					Tuple					,				snake_case_ :					Optional[int]   ):
    __a :  Any						=  self.num_labels
    __a :  Union[str, Any]						=  MobileBertForSequenceClassification(snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)   )
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					str					,				snake_case_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]   ):
    __a :  Union[str, Any]						=  self.num_labels
    __a :  str						=  MobileBertForTokenClassification(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Any						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.num_labels)   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					List[str]					,				snake_case_ :					Any					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Union[str, Any]   ):
    __a :  Union[str, Any]						=  self.num_choices
    __a :  List[str]						=  MobileBertForMultipleChoice(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[Any]						=  input_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  int						=  token_type_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  str						=  input_mask.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  Any						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				)
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_choices)   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Optional[Any]						=  self.prepare_config_and_inputs()
    (
        (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       
    ) :  int						=  config_and_inputs
    __a :  Union[str, Any]						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
    return config, inputs_dict





@require_torch
class 		UpperCamelCase__  (       __lowercase    ,__lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							str	       =      (
       (
           MobileBertModel,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE      :							Any	       =      (
       {
           "feature-extraction": MobileBertModel,
           "fill-mask": MobileBertForMaskedLM,
           "question-answering": MobileBertForQuestionAnswering,
           "text-classification": MobileBertForSequenceClassification,
           "token-classification": MobileBertForTokenClassification,
           "zero-shot": MobileBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      True
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[Any]					,				snake_case_ :					Union[str, Any]=False   ):
    __a :  List[str]						=  super()._prepare_for_class(snake_case_					,				snake_case_					,				return_labels=snake_case_   )

    if return_labels:
     if model_class in get_values(snake_case_   ):
      __a :  Optional[int]						=  torch.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length)					,				dtype=torch.long					,				device=snake_case_   )
      __a :  int						=  torch.zeros(
          self.model_tester.batch_size					,				dtype=torch.long					,				device=snake_case_   )
    return inputs_dict
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  List[Any]						=  MobileBertModelTester(self   )
    __a :  int						=  ConfigTester(self					,				config_class=snake_case_					,				hidden_size=3_7   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    self.config_tester.run_common_tests()
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_model(*snake_case_   )
   def       lowerCAmelCase			(self :					str   ):
    __a :  Any						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_   )
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  str						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_   )
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Optional[Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Tuple						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  int						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_   )







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_   )





def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 return torch.tensor(
     lowerCAmelCase__  ,   dtype=torch.long  ,   device=lowerCAmelCase__  ,   )


lowercase__       =1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (       unittest.TestCase    ):
   @slow
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Dict						=  MobileBertModel.from_pretrained('''google/mobilebert-uncased'''   ).to(snake_case_   )
    __a :  Tuple						=  _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   )
    with torch.no_grad():
     __a :  str						=  model(snake_case_   )[0]
    __a :  List[Any]						=  torch.Size((1, 9, 5_1_2)   )
    self.assertEqual(output.shape					,				snake_case_   )
    __a :  Union[str, Any]						=  torch.tensor(
        [
            [
                [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
                [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
                [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
            ]
        ]					,				device=snake_case_					,				)

    # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
    # ~1 difference, it's therefore not a good idea to measure using addition.
    # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
    # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
    __a :  List[str]						=  torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
    __a :  Any						=  torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

    self.assertTrue(lower_bound and upper_bound   )
