


import json
import os
import unittest
from typing import Tuple

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

from ...test_tokenization_common import TokenizerTesterMixin







@require_phonemizer
class     _lowercase				(							__SCREAMING_SNAKE_CASE							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    WavaVecaPhonemeCTCTokenizer
       A__      		=    False





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             super().setUp()

             lowerCamelCase__	:       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(" " )
             lowerCamelCase__	:       List[Any]					=						dict(zip(_a      ,   range(len(_a ) ) ) )

             lowerCamelCase__	:       int					=						{"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

             lowerCamelCase__	:       Optional[Any]					=						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(_a ) + "\n" )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    List[Any]=False      ,   __lowerCamelCase							:    Union[str, Any]=20      ,   __lowerCamelCase							:    Dict=5 ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						[(i, tokenizer.decode([i]      ,   clean_up_tokenization_spaces=_a )) for i in range(len(_a ) )]
             lowerCamelCase__	:       Optional[int]					=						list(filter(lambda __lowerCamelCase : [t[0]] == tokenizer.encode(t[1]      ,   do_phonemize=_a )      ,   _a ) )
             if max_length is not None and len(_a ) > max_length:
                   lowerCamelCase__	:       Optional[int]					=						toks[:max_length]
             if min_length is not None and len(_a ) < min_length and len(_a ) > 0:
                   while len(_a ) < min_length:
                         lowerCamelCase__	:       Union[str, Any]					=						toks + toks
        # toks_str = [t[1] for t in toks]
             lowerCamelCase__	:       Optional[int]					=						[t[0] for t in toks]

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





       def 		lowerCAmelCase  (     self							:    int      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

             # check adding a single token
             tokenizer.add_tokens("xxx" )
             lowerCamelCase__	:       Union[str, Any]					=						tokenizer("m xxx ɪ"      ,   do_phonemize=_a ).input_ids
             self.assertEqual(_a      ,   [13, 392, 17] )  # xxx should be last token

             tokenizer.add_tokens(["aaa", "bbb", "ccc"] )
             lowerCamelCase__	:       Optional[int]					=						tokenizer("m aaa ɪ ccc"      ,   do_phonemize=_a ).input_ids
             self.assertEqual(_a      ,   [13, 393, 17, 395] )  # aaa and ccc should be after xxx and 2 after aaa

             lowerCamelCase__	:       Dict					=						tokenizer("maɪ c"      ,   do_phonemize=_a ).input_ids
             self.assertEqual(_a      ,   [3, 200] )  # mai should be <unk> (=3)





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

             lowerCamelCase__	:       List[str]					=						"Hello how are you"
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.phonemize(_a      ,   phonemizer_lang="en-us" )
             self.assertEqual(_a      ,   "h ə l oʊ h aʊ ɑːɹ j uː" )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

             lowerCamelCase__	:       str					=						"Hello how are you"
             lowerCamelCase__	:       str					=						tokenizer.phonemize(_a      ,   phonemizer_lang="en-us" )
             self.assertEqual(tokenizer(_a ).input_ids      ,   tokenizer(_a      ,   do_phonemize=_a ).input_ids )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



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

             lowerCamelCase__	:       List[str]					=						tokenizer.decode(tokenizer(_a ).input_ids )

             self.assertEqual(_a      ,   _a )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

             lowerCamelCase__	:       Dict					=						[
                 [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
                 [24, 22, 5, 24, 22, 5, 77],
             ]
             lowerCamelCase__	:       int					=						tokenizer.decode(sample_ids[0] )
             lowerCamelCase__	:       List[str]					=						tokenizer.batch_decode(_a )
             self.assertEqual(_a      ,   batch_tokens[0] )
             self.assertEqual(_a      ,   ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"] )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



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

             lowerCamelCase__	:       Tuple					=						"Hello how are you"
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.phonemize(_a      ,   phonemizer_lang="en-us" )
             self.assertEqual(_a      ,   "h ə l oʊ | h aʊ | ɑːɹ | j uː |" )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.tokenizer_class.from_pretrained(
                 "facebook/wav2vec2-lv-60-espeak-cv-ft"      ,   word_delimiter_token="|" )
             tokenizer.add_tokens("|" )

             lowerCamelCase__	:       str					=						"Hello how are you"
             lowerCamelCase__	:       List[Any]					=						tokenizer.phonemize(_a      ,   phonemizer_lang="en-us" )
             self.assertEqual(tokenizer(_a ).input_ids      ,   tokenizer(_a      ,   do_phonemize=_a ).input_ids )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.tokenizer_class.from_pretrained(
                 "facebook/wav2vec2-lv-60-espeak-cv-ft"      ,   word_delimiter_token="|" )
             tokenizer.add_tokens("|" )

             # fmt: off
             lowerCamelCase__	:       List[str]					=						[
                 [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__	:       List[Any]					=						tokenizer.decode(sample_ids[0] )
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.batch_decode(_a )
             self.assertEqual(_a      ,   batch_tokens[0] )
             self.assertEqual(_a      ,   ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"] )

             # decode with no word_del_token filter
             lowerCamelCase__	:       int					=						tokenizer.decode(sample_ids[0]      ,   filter_word_delimiter_token=_a )
             lowerCamelCase__	:       List[Any]					=						tokenizer.batch_decode(_a      ,   filter_word_delimiter_token=_a )
             self.assertEqual(_a      ,   batch_tokens[0] )
             self.assertEqual(_a      ,   ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"] )





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.tokenizer_class.from_pretrained(
                 "facebook/wav2vec2-lv-60-espeak-cv-ft"      ,   word_delimiter_token="|" )
             tokenizer.add_tokens("|" )

             lowerCamelCase__	:       Any					=						"Hello how are you"
             lowerCamelCase__	:       Union[str, Any]					=						tokenizer.phonemize(_a      ,   phonemizer_lang="en-us" )

             lowerCamelCase__	:       List[str]					=						tokenizer.decode(tokenizer(_a ).input_ids      ,   filter_word_delimiter_token=_a )

             self.assertEqual(_a      ,   _a )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



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

             lowerCamelCase__	:       str					=						"Hello how are you"
             lowerCamelCase__	:       Optional[int]					=						tokenizer.phonemize(_a      ,   phonemizer_lang="en-us" )

             lowerCamelCase__	:       List[str]					=						tokenizer.decode(tokenizer(_a ).input_ids      ,   filter_word_delimiter_token=_a )

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





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self.tokenizer_class.from_pretrained(
                 "facebook/wav2vec2-lv-60-espeak-cv-ft"      ,   word_delimiter_token=_a )
             lowerCamelCase__	:       Optional[int]					=						"Hello how are you"

             lowerCamelCase__	:       Tuple					=						tokenizer(_a      ,   phonemizer_lang="en-us" ).input_ids
             lowerCamelCase__	:       int					=						tokenizer(_a      ,   phonemizer_lang="fr-fr" ).input_ids

             self.assertNotEqual(_a      ,   _a )

             lowerCamelCase__	:       int					=						tokenizer.decode(_a )
             lowerCamelCase__	:       int					=						tokenizer.decode(_a )

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





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
             lowerCamelCase__	:       Optional[Any]					=						"Hello how Are you"
             lowerCamelCase__	:       Optional[int]					=						"hello how are you"

             lowerCamelCase__	:       Dict					=						tokenizer(_a ).input_ids
             lowerCamelCase__	:       Tuple					=						tokenizer(_a ).input_ids

             self.assertEqual(_a      ,   _a )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



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

             # fmt: off
             lowerCamelCase__	:       Union[str, Any]					=						[
                 [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__	:       int					=						tokenizer.batch_decode(_a )
             self.assertEqual(_a      ,   ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"] )





       @staticmethod
       def 		lowerCAmelCase  (     __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						[d[key] for d in offsets]
             return retrieved_list





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						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__	:       Any					=						[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__	:       List[str]					=						tokenizer.decode(_a      ,   output_char_offsets=_a      ,   filter_word_delimiter_token=_a )
             # 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(_a      ,   _a ) )

             # 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							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						self.get_tokenizer(word_delimiter_token="|" )

             def check_list_tuples_equal(__lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int ):
                   self.assertTrue(isinstance(_a      ,   _a ) )
                   self.assertTrue(isinstance(outputs_list[0]      ,   _a ) )

                   # transform list to ModelOutput
                   lowerCamelCase__	:       Union[str, Any]					=						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							:    List[Any]      ,   __lowerCamelCase							:    Union[str, Any] ):
                         if isinstance(_a      ,   _a ):
                               [recursive_check(_a      ,   _a ) for la, la in zip(_a      ,   _a )]
                         self.assertEqual(_a      ,   _a )

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

        # fmt: off
             lowerCamelCase__	:       Union[str, Any]					=						[
                 [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__	:       Union[str, Any]					=						tokenizer.batch_decode(_a      ,   output_char_offsets=_a )
             lowerCamelCase__	:       Union[str, Any]					=						[tokenizer.decode(_a      ,   output_char_offsets=_a ) for ids in sample_ids]
             check_list_tuples_equal(_a      ,   _a )





       @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes" )
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             pass





       @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes" )
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             pass





       @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency" )
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             pass





       @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing" )
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.get_tokenizers(do_lower_case=_a )
             for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}" ):
                         lowerCamelCase__	:       Dict					=						tokenizer.vocab_size
                         lowerCamelCase__	:       List[Any]					=						len(_a )

                         self.assertNotEqual(_a      ,   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__	:       str					=						["aaaaa bbbbbb", "cccccccccdddddddd"]
                         lowerCamelCase__	:       Optional[int]					=						tokenizer.add_tokens(_a )
                         lowerCamelCase__	:       int					=						tokenizer.vocab_size
                         lowerCamelCase__	:       Any					=						len(_a )

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

                         lowerCamelCase__	:       Union[str, Any]					=						tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"      ,   add_special_tokens=_a )

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

                         lowerCamelCase__	:       Dict					=						{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
                         lowerCamelCase__	:       Optional[Any]					=						tokenizer.add_special_tokens(_a )
                         lowerCamelCase__	:       Any					=						tokenizer.vocab_size
                         lowerCamelCase__	:       Any					=						len(_a )

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

                         lowerCamelCase__	:       Tuple					=						tokenizer.encode(
                             ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"      ,   add_special_tokens=_a )

                         self.assertGreaterEqual(len(_a )      ,   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							:    List[Any] ):


             '''simple docstring'''



             pass





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


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.get_tokenizers(fast=_a      ,   do_lower_case=_a )
             for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}" ):
                         lowerCamelCase__	:       Dict					=						["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
                         lowerCamelCase__	:       Dict					=						tokenizer.convert_tokens_to_string(_a )

                         self.assertIsInstance(output["text"]      ,   _a )






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


A   :					Union[str, Any]	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A   :					Optional[Any]	=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     _lowercase				:


       """simple docstring"""




       A__      		=    field(
           default=lowercase__							,						metadata={"help": "Model type selected in the list: " + ", ".join(lowercase__)})
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."})
       A__      		=    field(
           default=1_28							,						metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }							,						)
       A__      		=    field(
           default=1_28							,						metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}							,						)
       A__      		=    field(
           default=64							,						metadata={
               "help": (
                   "The maximum number of tokens for the question. Questions longer than this will "
                   "be truncated to this length."
               )
           }							,						)
       A__      		=    field(
           default=30							,						metadata={
               "help": (
                   "The maximum length of an answer that can be generated. This is needed because the start "
                   "and end predictions are not conditioned on one another."
               )
           }							,						)
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "Overwrite the cached training and evaluation sets"})
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."})
       A__      		=    field(
           default=0.0							,						metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
       A__      		=    field(
           default=20							,						metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
       A__      		=    field(
           default=0							,						metadata={
               "help": (
                   "language id of input for language-specific xlm models (see"
                   " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
               )
           }							,						)
       A__      		=    field(default=1							,						metadata={"help": "multiple threads for converting example to features"})







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "train"
       A__      		=    "dev"







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    42
       A__      		=    42
       A__      		=    42
       A__      		=    42





       def __init__(     self							:    Optional[int]      ,   __lowerCamelCase							:    SquadDataTrainingArguments      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    Union[str, Split] = Split.train      ,   __lowerCamelCase							:    Optional[bool] = False      ,   __lowerCamelCase							:    Optional[str] = None      ,   __lowerCamelCase							:    Optional[str] = "pt"      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						args
             lowerCamelCase__	:       Tuple					=						is_language_sensitive
             lowerCamelCase__	:       int					=						SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
             if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   try:
                         lowerCamelCase__	:       List[str]					=						Split[mode]
                   except KeyError:
                         raise KeyError("mode is not a valid split name" )
             lowerCamelCase__	:       str					=						mode
             # Load data features from cache or dataset file
             lowerCamelCase__	:       Any					=						"v2" if args.version_2_with_negative else "v1"
             lowerCamelCase__	:       List[str]					=						os.path.join(
                 cache_dir if cache_dir is not None else args.data_dir      ,   f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"      ,   )

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             lowerCamelCase__	:       List[str]					=						cached_features_file + ".lock"
             with FileLock(__lowerCamelCase ):
                   if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                         lowerCamelCase__	:       str					=						time.time()
                         lowerCamelCase__	:       Tuple					=						torch.load(__lowerCamelCase )

                         # Legacy cache files have only features, while new cache files
                         # will have dataset and examples also.
                         lowerCamelCase__	:       Optional[Any]					=						self.old_features["features"]
                         lowerCamelCase__	:       Optional[int]					=						self.old_features.get("dataset"      ,   __lowerCamelCase )
                         lowerCamelCase__	:       Optional[Any]					=						self.old_features.get("examples"      ,   __lowerCamelCase )
                         logger.info(
                             f"Loading features from cached file {cached_features_file} [took %.3f s]"      ,   time.time() - start )

                         if self.dataset is None or self.examples is None:
                               logger.warning(
                                   f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
                                   " future run" )
                   else:
                         if mode == Split.dev:
                               lowerCamelCase__	:       List[Any]					=						self.processor.get_dev_examples(args.data_dir )
                         else:
                               lowerCamelCase__	:       str					=						self.processor.get_train_examples(args.data_dir )

                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						squad_convert_examples_to_features(
                             examples=self.examples      ,   tokenizer=__lowerCamelCase      ,   max_seq_length=args.max_seq_length      ,   doc_stride=args.doc_stride      ,   max_query_length=args.max_query_length      ,   is_training=mode == Split.train      ,   threads=args.threads      ,   return_dataset=__lowerCamelCase      ,   )

                         lowerCamelCase__	:       int					=						time.time()
                         torch.save(
                             {"features": self.features, "dataset": self.dataset, "examples": self.examples}      ,   __lowerCamelCase      ,   )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )





       def __len__(     self							:    List[Any] ):


             '''simple docstring'''



             return len(self.features )





       def __getitem__(     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.features[i]

             lowerCamelCase__	:       Tuple					=						torch.tensor(feature.input_ids      ,   dtype=torch.long )
             lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.attention_mask      ,   dtype=torch.long )
             lowerCamelCase__	:       Tuple					=						torch.tensor(feature.token_type_ids      ,   dtype=torch.long )
             lowerCamelCase__	:       Any					=						torch.tensor(feature.cls_index      ,   dtype=torch.long )
             lowerCamelCase__	:       Any					=						torch.tensor(feature.p_mask      ,   dtype=torch.float )
             lowerCamelCase__	:       Union[str, Any]					=						torch.tensor(feature.is_impossible      ,   dtype=torch.float )

             lowerCamelCase__	:       List[str]					=						{
                 "input_ids": input_ids,
                 "attention_mask": attention_mask,
                 "token_type_ids": token_type_ids,
             }

             if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                   del inputs["token_type_ids"]

             if self.args.model_type in ["xlnet", "xlm"]:
                   inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
                   if self.args.version_2_with_negative:
                         inputs.update({"is_impossible": is_impossible} )
                   if self.is_language_sensitive:
                         inputs.update({"langs": (torch.ones(input_ids.shape      ,   dtype=torch.intaa ) * self.args.lang_id)} )

             if self.mode == Split.train:
                   lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.start_position      ,   dtype=torch.long )
                   lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.end_position      ,   dtype=torch.long )
                   inputs.update({"start_positions": start_positions, "end_positions": end_positions} )

             return inputs


