
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 					_lowerCamelCase			(   UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	WavaVecaPhonemeCTCTokenizer
     SCREAMING_SNAKE_CASE_		 =	False






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           super().setUp()

           UpperCamelCase__     : str											=					(
               '''<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(''' '''	)
           UpperCamelCase__     : List[Any]											=					dict(zip(snake_case__    ,    range(len(snake_case__	)	)	)	)

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

           UpperCamelCase__     : Optional[int]											=					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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=2_0    ,    __SCREAMING_SNAKE_CASE=5	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					[(i, tokenizer.decode([i]    ,    clean_up_tokenization_spaces=snake_case__	)) for i in range(len(snake_case__	)	)]
           UpperCamelCase__     : str											=					list(filter(lambda __SCREAMING_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:
                 UpperCamelCase__     : 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:
                       UpperCamelCase__     : Any											=					toks + toks
                        # toks_str = [t[1] for t in toks]
           UpperCamelCase__     : Dict											=					[t[0] for t in toks]

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







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

           # check adding a single token
           tokenizer.add_tokens('''xxx'''	)
           UpperCamelCase__     : List[str]											=					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''']	)
           UpperCamelCase__     : Optional[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

           UpperCamelCase__     : 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  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







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

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







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

           UpperCamelCase__     : Dict											=					'''Hello how are you'''
           UpperCamelCase__     : List[str]											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)
           UpperCamelCase__     : Any											=					'''Hello how are you'''
           UpperCamelCase__     : Dict											=					tokenizer.phonemize(snake_case__    ,    phonemizer_lang='''en-us'''	)

           UpperCamelCase__     : Tuple											=					tokenizer.decode(tokenizer(snake_case__	).input_ids	)

           self.assertEqual(snake_case__    ,    snake_case__	)






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




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)

           UpperCamelCase__     : Union[str, 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],
           ]
           UpperCamelCase__     : List[str]											=					tokenizer.decode(sample_ids[0]	)
           UpperCamelCase__     : 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''']	)






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




           """simple docstring"""







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

           UpperCamelCase__     : Union[str, Any]											=					'''Hello how are you'''
           UpperCamelCase__     : Optional[int]											=					tokenizer.phonemize(snake_case__    ,    phonemizer_lang='''en-us'''	)
           self.assertEqual(snake_case__    ,    '''h ə l oʊ | h aʊ | ɑːɹ | j uː |'''	)






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




           """simple docstring"""







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

           UpperCamelCase__     : Any											=					'''Hello how are you'''
           UpperCamelCase__     : 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  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







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

           # fmt: off
           UpperCamelCase__     : List[Any]											=					[
               [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
           UpperCamelCase__     : Union[str, Any]											=					tokenizer.decode(sample_ids[0]	)
           UpperCamelCase__     : Optional[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
           UpperCamelCase__     : Dict											=					tokenizer.decode(sample_ids[0]    ,    filter_word_delimiter_token=snake_case__	)
           UpperCamelCase__     : Optional[Any]											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







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

           UpperCamelCase__     : Optional[Any]											=					'''Hello how are you'''
           UpperCamelCase__     : Dict											=					tokenizer.phonemize(snake_case__    ,    phonemizer_lang='''en-us'''	)

           UpperCamelCase__     : Optional[Any]											=					tokenizer.decode(tokenizer(snake_case__	).input_ids    ,    filter_word_delimiter_token=snake_case__	)

           self.assertEqual(snake_case__    ,    snake_case__	)






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




           """simple docstring"""







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

           UpperCamelCase__     : List[Any]											=					'''Hello how are you'''
           UpperCamelCase__     : int											=					tokenizer.phonemize(snake_case__    ,    phonemizer_lang='''en-us'''	)

           UpperCamelCase__     : int											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.tokenizer_class.from_pretrained(
               '''facebook/wav2vec2-lv-60-espeak-cv-ft'''    ,    word_delimiter_token=snake_case__	)
           UpperCamelCase__     : Union[str, Any]											=					'''Hello how are you'''

           UpperCamelCase__     : Optional[int]											=					tokenizer(snake_case__    ,    phonemizer_lang='''en-us'''	).input_ids
           UpperCamelCase__     : Tuple											=					tokenizer(snake_case__    ,    phonemizer_lang='''fr-fr'''	).input_ids

           self.assertNotEqual(snake_case__    ,    snake_case__	)

           UpperCamelCase__     : Union[str, Any]											=					tokenizer.decode(snake_case__	)
           UpperCamelCase__     : List[str]											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







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

           UpperCamelCase__     : Tuple											=					tokenizer(snake_case__	).input_ids
           UpperCamelCase__     : Optional[int]											=					tokenizer(snake_case__	).input_ids

           self.assertEqual(snake_case__    ,    snake_case__	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







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

           # fmt: off
           UpperCamelCase__     : int											=					[
               [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

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






     @staticmethod
     def  __SCREAMING_SNAKE_CASE      (				__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					[d[key] for d in offsets]
           return retrieved_list






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					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"
           UpperCamelCase__     : Union[str, Any]											=					[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

           UpperCamelCase__     : 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  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.get_tokenizer(word_delimiter_token='''|'''	)

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

                 # transform list to ModelOutput
                 UpperCamelCase__     : int											=					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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                       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
           UpperCamelCase__     : Dict											=					[
               [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
           UpperCamelCase__     : Optional[int]											=					tokenizer.batch_decode(snake_case__    ,    output_char_offsets=snake_case__	)
           UpperCamelCase__     : List[Any]											=					[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  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           pass






     @unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'''	)
     def  __SCREAMING_SNAKE_CASE      (				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  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           pass






     @unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing'''	)
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					self.get_tokenizers(do_lower_case=snake_case__	)
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''	):
                       UpperCamelCase__     : List[Any]											=					tokenizer.vocab_size
                       UpperCamelCase__     : str											=					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)

                       UpperCamelCase__     : List[str]											=					['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
                       UpperCamelCase__     : str											=					tokenizer.add_tokens(snake_case__	)
                       UpperCamelCase__     : List[str]											=					tokenizer.vocab_size
                       UpperCamelCase__     : Union[str, 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 + len(snake_case__	)	)

                       UpperCamelCase__     : Optional[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	)

                       UpperCamelCase__     : List[Any]											=					{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
                       UpperCamelCase__     : str											=					tokenizer.add_special_tokens(snake_case__	)
                       UpperCamelCase__     : Tuple											=					tokenizer.vocab_size
                       UpperCamelCase__     : 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__	)	)

                       UpperCamelCase__     : Optional[int]											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           pass






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




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







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

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





import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      BigBirdForPreTraining(_UpperCamelCase						)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_UpperCamelCase		,				_UpperCamelCase		,				is_trivia_qa=_UpperCamelCase						)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
 model.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    int     		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--big_bird_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained BERT model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
      )
      a_     :    Any     		=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

