

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			(  SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =WavaVecaPhonemeCTCTokenizer
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     super().setUp()

     SCREAMING_SNAKE_CASE		    =      (
         '<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(' '     )
     SCREAMING_SNAKE_CASE		    =      dict(zip(snake_case__				,     range(len(snake_case__     )     )     )     )

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

     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Optional[int]				,     snake_case__			:   str=False				,     snake_case__			:   Optional[int]=2_0				,     snake_case__			:   Dict=5     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [(i, tokenizer.decode([i]				,     clean_up_tokenization_spaces=snake_case__     )) for i in range(len(snake_case__     )     )]
     SCREAMING_SNAKE_CASE		    =      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:
      SCREAMING_SNAKE_CASE		    =      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:
       SCREAMING_SNAKE_CASE		    =      toks + toks
        # toks_str = [t[1] for t in toks]
     SCREAMING_SNAKE_CASE		    =      [t[0] for t in toks]

     # Ensure consistency
     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(snake_case__				,     clean_up_tokenization_spaces=snake_case__     )
     if " " not in output_txt and len(snake_case__     ) > 1:
      SCREAMING_SNAKE_CASE		    =      (
          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:
      SCREAMING_SNAKE_CASE		    =      ' ' + output_txt
     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(snake_case__				,     add_special_tokens=snake_case__     )
     return output_txt, output_ids

    def UpperCamelCase      (      self			:   Any				,     **snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     kwargs.update(self.special_tokens_map     )
     return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname				,     **snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






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

     # check adding a single token
     tokenizer.add_tokens('xxx'     )
     SCREAMING_SNAKE_CASE		    =      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']     )
     SCREAMING_SNAKE_CASE		    =      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

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

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






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

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

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      'Hello how are you'
     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(tokenizer(snake_case__     ).input_ids     )

     self.assertEqual(snake_case__				,     snake_case__     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      [
         [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],
     ]
     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(sample_ids[0]     )
     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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

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

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      'Hello how are you'
     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






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

     # fmt: off
     SCREAMING_SNAKE_CASE		    =      [
         [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
     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(sample_ids[0]     )
     SCREAMING_SNAKE_CASE		    =      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
     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(sample_ids[0]				,     filter_word_delimiter_token=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(tokenizer(snake_case__     ).input_ids				,     filter_word_delimiter_token=snake_case__     )

     self.assertEqual(snake_case__				,     snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






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

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

     self.assertNotEqual(snake_case__				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__     ).input_ids
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__     ).input_ids

     self.assertEqual(snake_case__				,     snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






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

     # fmt: off
     SCREAMING_SNAKE_CASE		    =      [
         [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

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

    @staticmethod
    def UpperCamelCase      (      snake_case__			:   Optional[int]				,     snake_case__			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [d[key] for d in offsets]
     return retrieved_list

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      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"
     SCREAMING_SNAKE_CASE		    =      [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

     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizer(word_delimiter_token='|'     )

     def check_list_tuples_equal(snake_case__			:   Optional[int]				,     snake_case__			:   Tuple     ):
      self.assertTrue(isinstance(snake_case__				,     snake_case__     )     )
      self.assertTrue(isinstance(outputs_list[0]				,     snake_case__     )     )

      # transform list to ModelOutput
      SCREAMING_SNAKE_CASE		    =      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[str]     ):
       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
     SCREAMING_SNAKE_CASE		    =      [
         [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
     SCREAMING_SNAKE_CASE		    =      tokenizer.batch_decode(snake_case__				,     output_char_offsets=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      [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 UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     pass

    @unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'     )
    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     pass

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

     """simple docstring"""






     pass

    @unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing'     )
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizers(do_lower_case=snake_case__     )
     for tokenizer in tokenizers:
      with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
       SCREAMING_SNAKE_CASE		    =      tokenizer.vocab_size
       SCREAMING_SNAKE_CASE		    =      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)

       SCREAMING_SNAKE_CASE		    =      ['aaaaa bbbbbb', 'cccccccccdddddddd']
       SCREAMING_SNAKE_CASE		    =      tokenizer.add_tokens(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      tokenizer.vocab_size
       SCREAMING_SNAKE_CASE		    =      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__     )     )

       SCREAMING_SNAKE_CASE		    =      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     )

       SCREAMING_SNAKE_CASE		    =      {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
       SCREAMING_SNAKE_CASE		    =      tokenizer.add_special_tokens(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      tokenizer.vocab_size
       SCREAMING_SNAKE_CASE		    =      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__     )     )

       SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     pass

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

     """simple docstring"""






     pass




    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizers(fast=snake_case__				,     do_lower_case=snake_case__     )
     for tokenizer in tokenizers:
      with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
       SCREAMING_SNAKE_CASE		    =      ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
       SCREAMING_SNAKE_CASE		    =      tokenizer.convert_tokens_to_string(snake_case__     )

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




from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel




@require_tf
class   UpperCamelCase			:
    __UpperCamelCase    =BlenderbotSmallConfig
    __UpperCamelCase    ={}
    __UpperCamelCase    ="gelu"

    def __init__(      self			:   Optional[int]				,     snake_case__			:   Dict				,     snake_case__			:   Optional[int]=1_3				,     snake_case__			:   Optional[int]=7				,     snake_case__			:   Optional[Any]=True				,     snake_case__			:   List[str]=False				,     snake_case__			:   int=9_9				,     snake_case__			:   Any=3_2				,     snake_case__			:   Tuple=2				,     snake_case__			:   Optional[int]=4				,     snake_case__			:   int=3_7				,     snake_case__			:   Optional[Any]=0.1				,     snake_case__			:   int=0.1				,     snake_case__			:   Any=2_0				,     snake_case__			:   List[Any]=2				,     snake_case__			:   int=1				,     snake_case__			:   int=0				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      seq_length
     SCREAMING_SNAKE_CASE		    =      is_training
     SCREAMING_SNAKE_CASE		    =      use_labels
     SCREAMING_SNAKE_CASE		    =      vocab_size
     SCREAMING_SNAKE_CASE		    =      hidden_size
     SCREAMING_SNAKE_CASE		    =      num_hidden_layers
     SCREAMING_SNAKE_CASE		    =      num_attention_heads
     SCREAMING_SNAKE_CASE		    =      intermediate_size

     SCREAMING_SNAKE_CASE		    =      hidden_dropout_prob
     SCREAMING_SNAKE_CASE		    =      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE		    =      max_position_embeddings
     SCREAMING_SNAKE_CASE		    =      eos_token_id
     SCREAMING_SNAKE_CASE		    =      pad_token_id
     SCREAMING_SNAKE_CASE		    =      bos_token_id

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )				,     1     )
     SCREAMING_SNAKE_CASE		    =      tf.concat([input_ids, eos_tensor]				,     axis=1     )

     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )

     SCREAMING_SNAKE_CASE		    =      self.config_cls(
         vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     encoder_layers=self.num_hidden_layers				,     decoder_layers=self.num_hidden_layers				,     encoder_attention_heads=self.num_attention_heads				,     decoder_attention_heads=self.num_attention_heads				,     encoder_ffn_dim=self.intermediate_size				,     decoder_ffn_dim=self.intermediate_size				,     dropout=self.hidden_dropout_prob				,     attention_dropout=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     eos_token_ids=[2]				,     bos_token_id=self.bos_token_id				,     pad_token_id=self.pad_token_id				,     decoder_start_token_id=self.pad_token_id				,     **self.config_updates				,     )
     SCREAMING_SNAKE_CASE		    =      prepare_blenderbot_small_inputs_dict(snake_case__				,     snake_case__				,     snake_case__     )
     return config, inputs_dict




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   Tuple				,     snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFBlenderbotSmallModel(config=snake_case__     ).get_decoder()
     SCREAMING_SNAKE_CASE		    =      inputs_dict['input_ids']

     SCREAMING_SNAKE_CASE		    =      input_ids[:1, :]
     SCREAMING_SNAKE_CASE		    =      inputs_dict['attention_mask'][:1, :]
     SCREAMING_SNAKE_CASE		    =      inputs_dict['head_mask']
     SCREAMING_SNAKE_CASE		    =      1

     # first forward pass
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     head_mask=snake_case__				,     use_cache=snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE		    =      ids_tensor((self.batch_size, 3)				,     config.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      tf.cast(ids_tensor((self.batch_size, 3)				,     2     )				,     tf.inta     )

     # append to next input_ids and
     SCREAMING_SNAKE_CASE		    =      tf.concat([input_ids, next_tokens]				,     axis=-1     )
     SCREAMING_SNAKE_CASE		    =      tf.concat([attention_mask, next_attn_mask]				,     axis=-1     )

     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     past_key_values=snake_case__     )[0]

     self.parent.assertEqual(next_tokens.shape[1]				,     output_from_past.shape[1]     )

     # select random slice
     SCREAMING_SNAKE_CASE		    =      int(ids_tensor((1,)				,     output_from_past.shape[-1]     )     )
     SCREAMING_SNAKE_CASE		    =      output_from_no_past[:, -3:, random_slice_idx]
     SCREAMING_SNAKE_CASE		    =      output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(snake_case__				,     snake_case__				,     rtol=1E-3     )


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	Optional[int]=None		,				_UpperCamelCase		:	Any=None		,				_UpperCamelCase		:	List[str]=None		,				_UpperCamelCase		:	Optional[Any]=None		,				)		->				str:
 '''simple docstring'''


 if attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      tf.cast(tf.math.not_equal(_UpperCamelCase		,				config.pad_token_id						)		,				tf.inta						)
 if decoder_attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape		,				dtype=tf.inta						),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,				config.pad_token_id						)		,				tf.inta						),
      ]		,				axis=-1		,				)
 if head_mask is None:
  SCREAMING_SNAKE_CASE		    =      tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
 if decoder_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
 if cross_attn_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }




@require_tf
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =(
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    __UpperCamelCase    =(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    __UpperCamelCase    =(
        {
            "conversational": TFBlenderbotSmallForConditionalGeneration,
            "feature-extraction": TFBlenderbotSmallModel,
            "summarization": TFBlenderbotSmallForConditionalGeneration,
            "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
            "translation": TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __UpperCamelCase    =True
    __UpperCamelCase    =False
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFBlenderbotSmallModelTester(self     )
     SCREAMING_SNAKE_CASE		    =      ConfigTester(self				,     config_class=snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     self.config_tester.run_common_tests()




    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*snake_case__     )




@require_tokenizers
@require_tf
class   UpperCamelCase			(  unittest.TestCase ):
    __UpperCamelCase    =[
        "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
        "  i'm going to throw up.\nand why is that?"
    ]
    __UpperCamelCase    ="facebook/blenderbot_small-90M"

    @cached_property
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'     )

    @cached_property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
     return model




    @slow
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer(self.src_text				,     return_tensors='tf'     )
     SCREAMING_SNAKE_CASE		    =      self.model.generate(
         model_inputs.input_ids				,     attention_mask=model_inputs.attention_mask				,     num_beams=2				,     use_cache=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      self.tokenizer.batch_decode(generated_ids.numpy()				,     skip_special_tokens=snake_case__     )[0]
     assert generated_words in (
         "i don't know. i just feel like i'm going to throw up. it's not fun.",
         "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
         "i'm not sure. i just feel like i've been in a bad situation.",
     )

