


'''simple docstring'''

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      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  WavaVecaPhonemeCTCTokenizer
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   super().setUp()

   lowerCamelCase_      =  (
       "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
       "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
       "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
       "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
       "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
       "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
       "əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
       "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
       "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
       "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
       "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
       "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
       "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
   ).split(" "     )
   lowerCamelCase_      =  dict(zip(UpperCamelCase			,  range(len(UpperCamelCase     )     )     )     )

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

   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]     )
   with open(self.vocab_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write(json.dumps(UpperCamelCase     ) + "\n"     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=20			,  UpperCamelCase=5     ):







   """simple docstring"""
   lowerCamelCase_      =  [(i, tokenizer.decode([i]			,  clean_up_tokenization_spaces=UpperCamelCase     )) for i in range(len(UpperCamelCase     )     )]
   lowerCamelCase_      =  list(filter(lambda UpperCamelCase     : [t[0]] == tokenizer.encode(t[1]			,  do_phonemize=UpperCamelCase     )			,  UpperCamelCase     )     )
   if max_length is not None and len(UpperCamelCase     ) > max_length:
    lowerCamelCase_      =  toks[:max_length]
   if min_length is not None and len(UpperCamelCase     ) < min_length and len(UpperCamelCase     ) > 0:
    while len(UpperCamelCase     ) < min_length:
     lowerCamelCase_      =  toks + toks
        # toks_str = [t[1] for t in toks]
   lowerCamelCase_      =  [t[0] for t in toks]

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






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   kwargs.update(self.special_tokens_map     )
   return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname			,  **UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

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

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

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

   lowerCamelCase_      =  "Hello how are you"
   lowerCamelCase_      =  tokenizer.phonemize(UpperCamelCase			,  phonemizer_lang="en-us"     )
   self.assertEqual(tokenizer(UpperCamelCase     ).input_ids			,  tokenizer(UpperCamelCase			,  do_phonemize=UpperCamelCase     ).input_ids     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )
   lowerCamelCase_      =  "Hello how are you"
   lowerCamelCase_      =  tokenizer.phonemize(UpperCamelCase			,  phonemizer_lang="en-us"     )

   lowerCamelCase_      =  tokenizer.decode(tokenizer(UpperCamelCase     ).input_ids     )

   self.assertEqual(UpperCamelCase			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained(
       "facebook/wav2vec2-lv-60-espeak-cv-ft"			,  word_delimiter_token="|"     )
   tokenizer.add_tokens("|"     )

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained(
       "facebook/wav2vec2-lv-60-espeak-cv-ft"			,  word_delimiter_token="|"     )
   tokenizer.add_tokens("|"     )

   lowerCamelCase_      =  "Hello how are you"
   lowerCamelCase_      =  tokenizer.phonemize(UpperCamelCase			,  phonemizer_lang="en-us"     )
   self.assertEqual(tokenizer(UpperCamelCase     ).input_ids			,  tokenizer(UpperCamelCase			,  do_phonemize=UpperCamelCase     ).input_ids     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained(
       "facebook/wav2vec2-lv-60-espeak-cv-ft"			,  word_delimiter_token="|"     )
   tokenizer.add_tokens("|"     )

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

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

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained(
       "facebook/wav2vec2-lv-60-espeak-cv-ft"			,  word_delimiter_token="|"     )
   tokenizer.add_tokens("|"     )

   lowerCamelCase_      =  "Hello how are you"
   lowerCamelCase_      =  tokenizer.phonemize(UpperCamelCase			,  phonemizer_lang="en-us"     )

   lowerCamelCase_      =  tokenizer.decode(tokenizer(UpperCamelCase     ).input_ids			,  filter_word_delimiter_token=UpperCamelCase     )

   self.assertEqual(UpperCamelCase			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained(
       "facebook/wav2vec2-lv-60-espeak-cv-ft"			,  word_delimiter_token="|"     )
   tokenizer.add_tokens("|"     )

   lowerCamelCase_      =  "Hello how are you"
   lowerCamelCase_      =  tokenizer.phonemize(UpperCamelCase			,  phonemizer_lang="en-us"     )

   lowerCamelCase_      =  tokenizer.decode(tokenizer(UpperCamelCase     ).input_ids			,  filter_word_delimiter_token=UpperCamelCase     )

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained(
       "facebook/wav2vec2-lv-60-espeak-cv-ft"			,  word_delimiter_token=UpperCamelCase     )
   lowerCamelCase_      =  "Hello how are you"

   lowerCamelCase_      =  tokenizer(UpperCamelCase			,  phonemizer_lang="en-us"     ).input_ids
   lowerCamelCase_      =  tokenizer(UpperCamelCase			,  phonemizer_lang="fr-fr"     ).input_ids

   self.assertNotEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  tokenizer.decode(UpperCamelCase     )
   lowerCamelCase_      =  tokenizer.decode(UpperCamelCase     )

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )
   lowerCamelCase_      =  "Hello how Are you"
   lowerCamelCase_      =  "hello how are you"

   lowerCamelCase_      =  tokenizer(UpperCamelCase     ).input_ids
   lowerCamelCase_      =  tokenizer(UpperCamelCase     ).input_ids

   self.assertEqual(UpperCamelCase			,  UpperCamelCase     )






  def   snake_case		(    self     ):







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

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

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






  @staticmethod
  def   snake_case		(    UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  [d[key] for d in offsets]
   return retrieved_list






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizer(word_delimiter_token="|"     )
   tokenizer.add_tokens("|"     )

   # fmt: off
   # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
   lowerCamelCase_      =  [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
   # fmt: on

   lowerCamelCase_      =  tokenizer.decode(UpperCamelCase			,  output_char_offsets=UpperCamelCase			,  filter_word_delimiter_token=UpperCamelCase     )
   # 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(UpperCamelCase			,  UpperCamelCase     )     )

   # 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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizer(word_delimiter_token="|"     )

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

    # transform list to ModelOutput
    lowerCamelCase_      =  WavaVecaPhonemeCTCTokenizerOutput(
        {k: [d[k] for d in outputs_list] for k in outputs_list[0]}     )

    self.assertListEqual(outputs_batch["text"]			,  outputs_batch_a["text"]     )

    def recursive_check(UpperCamelCase			,  UpperCamelCase     ):
     if isinstance(UpperCamelCase			,  UpperCamelCase     ):
      [recursive_check(UpperCamelCase			,  UpperCamelCase     ) for la, la in zip(UpperCamelCase			,  UpperCamelCase     )]
     self.assertEqual(UpperCamelCase			,  UpperCamelCase     )

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

        # fmt: off
   lowerCamelCase_      =  [
       [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
       [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
   ]
   # fmt: on

   # We assume that `decode` works as expected. All we will check now is
   # the output type is correct and the output is identical to `decode`

   # char
   lowerCamelCase_      =  tokenizer.batch_decode(UpperCamelCase			,  output_char_offsets=UpperCamelCase     )
   lowerCamelCase_      =  [tokenizer.decode(UpperCamelCase			,  output_char_offsets=UpperCamelCase     ) for ids in sample_ids]
   check_list_tuples_equal(UpperCamelCase			,  UpperCamelCase     )






  @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






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







   """simple docstring"""
   pass






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







   """simple docstring"""
   pass






  @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizers(do_lower_case=UpperCamelCase     )
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
     lowerCamelCase_      =  tokenizer.vocab_size
     lowerCamelCase_      =  len(UpperCamelCase     )

     self.assertNotEqual(UpperCamelCase			,  0     )

     # We usually have added tokens from the start in tests because our vocab fixtures are
     # smaller than the original vocabs - let's not assert this
     # self.assertEqual(vocab_size, all_size)

     lowerCamelCase_      =  ["aaaaa bbbbbb", "cccccccccdddddddd"]
     lowerCamelCase_      =  tokenizer.add_tokens(UpperCamelCase     )
     lowerCamelCase_      =  tokenizer.vocab_size
     lowerCamelCase_      =  len(UpperCamelCase     )

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

     lowerCamelCase_      =  tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"			,  add_special_tokens=UpperCamelCase     )

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

     lowerCamelCase_      =  {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
     lowerCamelCase_      =  tokenizer.add_special_tokens(UpperCamelCase     )
     lowerCamelCase_      =  tokenizer.vocab_size
     lowerCamelCase_      =  len(UpperCamelCase     )

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

     lowerCamelCase_      =  tokenizer.encode(
         ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"			,  add_special_tokens=UpperCamelCase     )

     self.assertGreaterEqual(len(UpperCamelCase     )			,  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   snake_case		(    self     ):







   """simple docstring"""
   pass






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







   """simple docstring"""
   pass







  def   snake_case		(    self     ):







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

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







'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


a_  :				Optional[Any]						= logging.getLogger(__name__)





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,						)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Whether tp freeze the encoder."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Whether  to freeze the embeddings."}			)





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
  _lowerCamelCase		    =  field(
      default="summarization"			,						metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}			,						)
  _lowerCamelCase		    =  field(
      default=10_24			,						metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_28			,						metadata={
          "help": (
              "The maximum total sequence length for target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_42			,						metadata={
          "help": (
              "The maximum total sequence length for validation target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded. "
              "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
              "during ``evaluate`` and ``predict``."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_42			,						metadata={
          "help": (
              "The maximum total sequence length for test target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,						)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# training examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# validation examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# test examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Source language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Target language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "# num_beams to use for evaluation."}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}			,						)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[Any]		):
 logger.info(F'''***** {split} metrics *****'''		)
 for key in sorted(metrics.keys()		):
  logger.info(F'''  {key} = {metrics[key]}'''		)
 save_json(UpperCAmelCase_					,				os.path.join(UpperCAmelCase_					,				F'''{split}_results.json'''		)		)





def __snake_case					(      ):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCamelCase_      =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)		)

 if len(sys.argv		) == 2 and sys.argv[1].endswith(".json"		):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  parser.parse_args_into_dataclasses()

 check_output_dir(UpperCAmelCase_		)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,				datefmt="%m/%d/%Y %H:%M:%S"					,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,				)
 logger.warning(
     "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,				training_args.local_rank					,				training_args.device					,				training_args.n_gpu					,				bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED		)					,				training_args.fpaa					,				)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank		):
  transformers.utils.logging.set_verbosity_info()
 logger.info("Training/evaluation parameters %s"					,				UpperCAmelCase_		)

 # Set seed
 set_seed(training_args.seed		)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 lowerCamelCase_      =  AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path					,				cache_dir=model_args.cache_dir					,				)

 lowerCamelCase_      =  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
 for p in extra_model_params:
  if getattr(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		):
   assert hasattr(UpperCAmelCase_					,				UpperCAmelCase_		), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
   setattr(UpperCAmelCase_					,				UpperCAmelCase_					,				getattr(UpperCAmelCase_					,				UpperCAmelCase_		)		)

 lowerCamelCase_      =  AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,				cache_dir=model_args.cache_dir					,				)
 lowerCamelCase_      =  AutoModelForSeqaSeqLM.from_pretrained(
     model_args.model_name_or_path					,				from_tf=".ckpt" in model_args.model_name_or_path					,				config=UpperCAmelCase_					,				cache_dir=model_args.cache_dir					,				)

 # use task specific params
 use_task_specific_params(UpperCAmelCase_					,				data_args.task		)

 # set num_beams for evaluation
 if data_args.eval_beams is None:
  lowerCamelCase_      =  model.config.num_beams

 # set decoder_start_token_id for MBart
 if model.config.decoder_start_token_id is None and isinstance(UpperCAmelCase_					,				(MBartTokenizer, MBartTokenizerFast)		):
  assert (
      data_args.tgt_lang is not None and data_args.src_lang is not None
  ), "mBart requires --tgt_lang and --src_lang"
  if isinstance(UpperCAmelCase_					,				UpperCAmelCase_		):
   lowerCamelCase_      =  tokenizer.lang_code_to_id[data_args.tgt_lang]
  else:
   lowerCamelCase_      =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang		)

 if model_args.freeze_embeds:
  freeze_embeds(UpperCAmelCase_		)
 if model_args.freeze_encoder:
  freeze_params(model.get_encoder()		)
  assert_all_frozen(model.get_encoder()		)

 lowerCamelCase_      =  SeqaSeqDataset

 # Get datasets
 lowerCamelCase_      =  (
     dataset_class(
         UpperCAmelCase_					,				type_path="train"					,				data_dir=data_args.data_dir					,				n_obs=data_args.n_train					,				max_target_length=data_args.max_target_length					,				max_source_length=data_args.max_source_length					,				prefix=model.config.prefix or ""					,				)
     if training_args.do_train
     else None
 )
 lowerCamelCase_      =  (
     dataset_class(
         UpperCAmelCase_					,				type_path="val"					,				data_dir=data_args.data_dir					,				n_obs=data_args.n_val					,				max_target_length=data_args.val_max_target_length					,				max_source_length=data_args.max_source_length					,				prefix=model.config.prefix or ""					,				)
     if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
     else None
 )
 lowerCamelCase_      =  (
     dataset_class(
         UpperCAmelCase_					,				type_path="test"					,				data_dir=data_args.data_dir					,				n_obs=data_args.n_test					,				max_target_length=data_args.test_max_target_length					,				max_source_length=data_args.max_source_length					,				prefix=model.config.prefix or ""					,				)
     if training_args.do_predict
     else None
 )

 # Initialize our Trainer
 lowerCamelCase_      =  (
     build_compute_metrics_fn(data_args.task					,				UpperCAmelCase_		) if training_args.predict_with_generate else None
 )
 lowerCamelCase_      =  SeqaSeqTrainer(
     model=UpperCAmelCase_					,				args=UpperCAmelCase_					,				data_args=UpperCAmelCase_					,				train_dataset=UpperCAmelCase_					,				eval_dataset=UpperCAmelCase_					,				data_collator=SeqaSeqDataCollator(
         UpperCAmelCase_					,				UpperCAmelCase_					,				model.config.decoder_start_token_id					,				training_args.tpu_num_cores		)					,				compute_metrics=UpperCAmelCase_					,				tokenizer=UpperCAmelCase_					,				)

 lowerCamelCase_      =  {}
 # Training
 if training_args.do_train:
  logger.info("*** Train ***"		)

  lowerCamelCase_      =  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
  lowerCamelCase_      =  train_result.metrics
  lowerCamelCase_      =  data_args.n_train

  trainer.save_model()  # this also saves the tokenizer

  if trainer.is_world_process_zero():
   handle_metrics("train"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

   # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir					,				"trainer_state.json"		)		)

   # For convenience, we also re-save the tokenizer to the same directory,
   # so that you can share your model easily on huggingface.co/models =)
   tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
 if training_args.do_eval:
  logger.info("*** Evaluate ***"		)

  lowerCamelCase_      =  trainer.evaluate(metric_key_prefix="val"		)
  lowerCamelCase_      =  data_args.n_val
  lowerCamelCase_      =  round(metrics["val_loss"]					,				4		)

  if trainer.is_world_process_zero():
   handle_metrics("val"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

 if training_args.do_predict:
  logger.info("*** Predict ***"		)

  lowerCamelCase_      =  trainer.predict(test_dataset=UpperCAmelCase_					,				metric_key_prefix="test"		)
  lowerCamelCase_      =  test_output.metrics
  lowerCamelCase_      =  data_args.n_test

  if trainer.is_world_process_zero():
   lowerCamelCase_      =  round(metrics["test_loss"]					,				4		)
   handle_metrics("test"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

   if training_args.predict_with_generate:
    lowerCamelCase_      =  tokenizer.batch_decode(
        test_output.predictions					,				skip_special_tokens=UpperCAmelCase_					,				clean_up_tokenization_spaces=UpperCAmelCase_		)
    lowerCamelCase_      =  lmap(str.strip					,				UpperCAmelCase_		)
    write_txt_file(UpperCAmelCase_					,				os.path.join(training_args.output_dir					,				"test_generations.txt"		)		)

 if trainer.is_world_process_zero():
  save_json(UpperCAmelCase_					,				os.path.join(training_args.output_dir					,				"all_results.json"		)		)

 return all_metrics





def __snake_case					(      UpperCAmelCase_						:						Dict		):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()



