



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						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     WavaVecaPhonemeCTCTokenizer
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self			):
         super().setUp()

         _lowerCAmelCase 			=  (
             '''<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(''' '''			)
         _lowerCAmelCase 			=  dict(zip(_lowerCAmelCase       ,     range(len(_lowerCAmelCase			)			)			)			)

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

         _lowerCAmelCase 			=  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(_lowerCAmelCase			) + '''\n'''			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=False       ,     _lowerCAmelCase=20       ,     _lowerCAmelCase=5			):
         _lowerCAmelCase 			=  [(i, tokenizer.decode([i]       ,     clean_up_tokenization_spaces=_lowerCAmelCase			)) for i in range(len(_lowerCAmelCase			)			)]
         _lowerCAmelCase 			=  list(filter(lambda _lowerCAmelCase			: [t[0]] == tokenizer.encode(t[1]       ,     do_phonemize=_lowerCAmelCase			)       ,     _lowerCAmelCase			)			)
         if max_length is not None and len(_lowerCAmelCase			) > max_length:
               _lowerCAmelCase 			=  toks[:max_length]
         if min_length is not None and len(_lowerCAmelCase			) < min_length and len(_lowerCAmelCase			) > 0:
               while len(_lowerCAmelCase			) < min_length:
                     _lowerCAmelCase 			=  toks + toks
        # toks_str = [t[1] for t in toks]
         _lowerCAmelCase 			=  [t[0] for t in toks]

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





   def 	__lowerCAmelCase							(	self       ,     **_lowerCAmelCase			):
         kwargs.update(self.special_tokens_map			)
         return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

         # check adding a single token
         tokenizer.add_tokens('''xxx'''			)
         _lowerCAmelCase 			=  tokenizer('''m xxx ɪ'''       ,     do_phonemize=_lowerCAmelCase			).input_ids
         self.assertEqual(_lowerCAmelCase       ,     [13, 392, 17]			)  # xxx should be last token

         tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc''']			)
         _lowerCAmelCase 			=  tokenizer('''m aaa ɪ ccc'''       ,     do_phonemize=_lowerCAmelCase			).input_ids
         self.assertEqual(_lowerCAmelCase       ,     [13, 393, 17, 395]			)  # aaa and ccc should be after xxx and 2 after aaa

         _lowerCAmelCase 			=  tokenizer('''maɪ c'''       ,     do_phonemize=_lowerCAmelCase			).input_ids
         self.assertEqual(_lowerCAmelCase       ,     [3, 200]			)  # mai should be <unk> (=3)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

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





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

         _lowerCAmelCase 			=  tokenizer.decode(tokenizer(_lowerCAmelCase			).input_ids			)

         self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''			)

         _lowerCAmelCase 			=  [
             [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
             [24, 22, 5, 24, 22, 5, 77],
         ]
         _lowerCAmelCase 			=  tokenizer.decode(sample_ids[0]			)
         _lowerCAmelCase 			=  tokenizer.batch_decode(_lowerCAmelCase			)
         self.assertEqual(_lowerCAmelCase       ,     batch_tokens[0]			)
         self.assertEqual(_lowerCAmelCase       ,     ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']			)





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

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





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

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





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

         # fmt: off
         _lowerCAmelCase 			=  [
             [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
             [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
         ]
         # fmt: on

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

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





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

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

         _lowerCAmelCase 			=  tokenizer.decode(tokenizer(_lowerCAmelCase			).input_ids       ,     filter_word_delimiter_token=_lowerCAmelCase			)

         self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





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

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

         _lowerCAmelCase 			=  tokenizer.decode(tokenizer(_lowerCAmelCase			).input_ids       ,     filter_word_delimiter_token=_lowerCAmelCase			)

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





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

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

         self.assertNotEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  tokenizer.decode(_lowerCAmelCase			)
         _lowerCAmelCase 			=  tokenizer.decode(_lowerCAmelCase			)

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





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

         _lowerCAmelCase 			=  tokenizer(_lowerCAmelCase			).input_ids
         _lowerCAmelCase 			=  tokenizer(_lowerCAmelCase			).input_ids

         self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





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

         # fmt: off
         _lowerCAmelCase 			=  [
             [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
             [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
         ]
         # fmt: on

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





   @staticmethod
   def 	__lowerCAmelCase							(	_lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  [d[key] for d in offsets]
         return retrieved_list





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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"
         _lowerCAmelCase 			=  [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
         # fmt: on

         _lowerCAmelCase 			=  tokenizer.decode(_lowerCAmelCase       ,     output_char_offsets=_lowerCAmelCase       ,     filter_word_delimiter_token=_lowerCAmelCase			)
         # 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(_lowerCAmelCase       ,     _lowerCAmelCase			)			)

         # 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, 11, 12, 15, 16]			)
         self.assertListEqual(
             self.get_from_offsets(outputs['''char_offsets''']       ,     '''end_offset'''			)       ,     [1, 4, 6, 9, 10, 12, 15, 16, 17]			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.get_tokenizer(word_delimiter_token='''|'''			)

         def check_list_tuples_equal(_lowerCAmelCase       ,     _lowerCAmelCase			):
               self.assertTrue(isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			)			)
               self.assertTrue(isinstance(outputs_list[0]       ,     _lowerCAmelCase			)			)

               # transform list to ModelOutput
               _lowerCAmelCase 			=  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(_lowerCAmelCase       ,     _lowerCAmelCase			):
                     if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           [recursive_check(_lowerCAmelCase       ,     _lowerCAmelCase			) for la, la in zip(_lowerCAmelCase       ,     _lowerCAmelCase			)]
                     self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

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

        # fmt: off
         _lowerCAmelCase 			=  [
             [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
             [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
         ]
         # 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
         _lowerCAmelCase 			=  tokenizer.batch_decode(_lowerCAmelCase       ,     output_char_offsets=_lowerCAmelCase			)
         _lowerCAmelCase 			=  [tokenizer.decode(_lowerCAmelCase       ,     output_char_offsets=_lowerCAmelCase			) for ids in sample_ids]
         check_list_tuples_equal(_lowerCAmelCase       ,     _lowerCAmelCase			)





   @unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'''			)
   def 	__lowerCAmelCase							(	self			):
         pass





   @unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'''			)
   def 	__lowerCAmelCase							(	self			):
         pass





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





   @unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing'''			)
   def 	__lowerCAmelCase							(	self			):
         pass





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.get_tokenizers(do_lower_case=_lowerCAmelCase			)
         for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
                     _lowerCAmelCase 			=  tokenizer.vocab_size
                     _lowerCAmelCase 			=  len(_lowerCAmelCase			)

                     self.assertNotEqual(_lowerCAmelCase       ,     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)

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

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

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

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

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

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

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

                     self.assertGreaterEqual(len(_lowerCAmelCase			)       ,     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			):
         pass





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





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

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




import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=2       ,     _lowerCAmelCase=8       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=99       ,     _lowerCAmelCase=16       ,     _lowerCAmelCase=5       ,     _lowerCAmelCase=2       ,     _lowerCAmelCase=36       ,     _lowerCAmelCase="gelu"       ,     _lowerCAmelCase=0.0       ,     _lowerCAmelCase=0.0       ,     _lowerCAmelCase=512       ,     _lowerCAmelCase=16       ,     _lowerCAmelCase=2       ,     _lowerCAmelCase=0.02       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=4       ,     _lowerCAmelCase=None       ,     ):
         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  seq_length
         _lowerCAmelCase 			=  is_training
         _lowerCAmelCase 			=  use_input_mask
         _lowerCAmelCase 			=  use_token_type_ids
         _lowerCAmelCase 			=  use_labels
         _lowerCAmelCase 			=  vocab_size
         _lowerCAmelCase 			=  hidden_size
         _lowerCAmelCase 			=  num_hidden_layers
         _lowerCAmelCase 			=  num_attention_heads
         _lowerCAmelCase 			=  intermediate_size
         _lowerCAmelCase 			=  hidden_act
         _lowerCAmelCase 			=  hidden_dropout_prob
         _lowerCAmelCase 			=  attention_probs_dropout_prob
         _lowerCAmelCase 			=  max_position_embeddings
         _lowerCAmelCase 			=  type_vocab_size
         _lowerCAmelCase 			=  type_sequence_label_size
         _lowerCAmelCase 			=  initializer_range
         _lowerCAmelCase 			=  num_labels
         _lowerCAmelCase 			=  num_choices
         _lowerCAmelCase 			=  scope





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size			)

         _lowerCAmelCase 			=  None
         if self.use_input_mask:
               _lowerCAmelCase 			=  random_attention_mask([self.batch_size, self.seq_length]			)

         _lowerCAmelCase 			=  None
         if self.use_token_type_ids:
               _lowerCAmelCase 			=  ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size			)

         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None
         if self.use_labels:
               _lowerCAmelCase 			=  ids_tensor([self.batch_size]       ,     self.type_sequence_label_size			)
               _lowerCAmelCase 			=  ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels			)
               _lowerCAmelCase 			=  ids_tensor([self.batch_size]       ,     self.num_choices			)

         _lowerCAmelCase 			=  self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





   def 	__lowerCAmelCase							(	self			):
         return MraConfig(
             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       ,     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=_lowerCAmelCase       ,     initializer_range=self.initializer_range       ,     )





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.get_config()
         _lowerCAmelCase 			=  300
         return config





   def 	__lowerCAmelCase							(	self			):
         (
             (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   
         ) 			=  self.prepare_config_and_inputs()

         _lowerCAmelCase 			=  True
         _lowerCAmelCase 			=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
         _lowerCAmelCase 			=  ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2			)

         return (
             config,
             input_ids,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  MraModel(config=_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  model(_lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase			)
         _lowerCAmelCase 			=  model(_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase			)
         _lowerCAmelCase 			=  model(_lowerCAmelCase			)
         self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  True
         _lowerCAmelCase 			=  MraModel(_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  model(
             _lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase       ,     encoder_hidden_states=_lowerCAmelCase       ,     encoder_attention_mask=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  model(
             _lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase       ,     encoder_hidden_states=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  model(_lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase			)
         self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  MraForMaskedLM(config=_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  model(_lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase       ,     labels=_lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  MraForQuestionAnswering(config=_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  model(
             _lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase       ,     start_positions=_lowerCAmelCase       ,     end_positions=_lowerCAmelCase       ,     )
         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       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.num_labels
         _lowerCAmelCase 			=  MraForSequenceClassification(_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  model(_lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase       ,     labels=_lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.num_labels
         _lowerCAmelCase 			=  MraForTokenClassification(config=_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  model(_lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase       ,     labels=_lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.num_choices
         _lowerCAmelCase 			=  MraForMultipleChoice(config=_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  input_ids.unsqueeze(1			).expand(-1       ,     self.num_choices       ,     -1			).contiguous()
         _lowerCAmelCase 			=  token_type_ids.unsqueeze(1			).expand(-1       ,     self.num_choices       ,     -1			).contiguous()
         _lowerCAmelCase 			=  input_mask.unsqueeze(1			).expand(-1       ,     self.num_choices       ,     -1			).contiguous()
         _lowerCAmelCase 			=  model(
             _lowerCAmelCase       ,     attention_mask=_lowerCAmelCase       ,     token_type_ids=_lowerCAmelCase       ,     labels=_lowerCAmelCase       ,     )
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.prepare_config_and_inputs()
         (
             (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   (
             _lowerCAmelCase
         )			,   
         ) 			=  config_and_inputs
         _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_torch
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     (
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     False

   SCREAMING_SNAKE_CASE__							=     ()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MraModelTester(self			)
         _lowerCAmelCase 			=  ConfigTester(self       ,     config_class=_lowerCAmelCase       ,     hidden_size=37			)





   def 	__lowerCAmelCase							(	self			):
         self.config_tester.run_common_tests()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
               _lowerCAmelCase 			=  type
               self.model_tester.create_and_check_model(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowerCAmelCase 			=  MraModel.from_pretrained(_lowerCAmelCase			)
               self.assertIsNotNone(_lowerCAmelCase			)





   @unittest.skip(reason='''MRA does not output attentions'''			)
   def 	__lowerCAmelCase							(	self			):
         return






@require_torch
class 		UpperCAmelCase						(						unittest.TestCase				):





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MraModel.from_pretrained('''uw-madison/mra-base-512-4'''			)
         _lowerCAmelCase 			=  torch.arange(256			).unsqueeze(0			)

         with torch.no_grad():
               _lowerCAmelCase 			=  model(_lowerCAmelCase			)[0]

         _lowerCAmelCase 			=  torch.Size((1, 256, 768)			)
         self.assertEqual(output.shape       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.tensor(
             [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]			)

         self.assertTrue(torch.allclose(output[:, :3, :3]       ,     _lowerCAmelCase       ,     atol=1E-4			)			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''			)
         _lowerCAmelCase 			=  torch.arange(256			).unsqueeze(0			)

         with torch.no_grad():
               _lowerCAmelCase 			=  model(_lowerCAmelCase			)[0]

         _lowerCAmelCase 			=  50_265

         _lowerCAmelCase 			=  torch.Size((1, 256, vocab_size)			)
         self.assertEqual(output.shape       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.tensor(
             [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]			)

         self.assertTrue(torch.allclose(output[:, :3, :3]       ,     _lowerCAmelCase       ,     atol=1E-4			)			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''			)
         _lowerCAmelCase 			=  torch.arange(4_096			).unsqueeze(0			)

         with torch.no_grad():
               _lowerCAmelCase 			=  model(_lowerCAmelCase			)[0]

         _lowerCAmelCase 			=  50_265

         _lowerCAmelCase 			=  torch.Size((1, 4_096, vocab_size)			)
         self.assertEqual(output.shape       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.tensor(
             [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]			)

         self.assertTrue(torch.allclose(output[:, :3, :3]       ,     _lowerCAmelCase       ,     atol=1E-4			)			)