


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 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   unittest.TestCase      ):
      snake_case__    :					Optional[int]					       =						WavaVecaPhonemeCTCTokenizer
      snake_case__    :					Union[str, Any]					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[Any]:
             super().setUp()

             a_ :		Optional[int]							=  (
                 '<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(' '					)
             a_ :		Union[str, Any]							=  dict(zip(SCREAMING_SNAKE_CASE__					,							range(len(SCREAMING_SNAKE_CASE__					)					)					)					)

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

             a_ :		Tuple							=  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(SCREAMING_SNAKE_CASE__					) + '\n'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=False					,							SCREAMING_SNAKE_CASE__    :	List[str]=2_0					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=5					)		->      Tuple[str, list]:
             a_ :		int							=  [(i, tokenizer.decode([i]					,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__					)) for i in range(len(SCREAMING_SNAKE_CASE__					)					)]
             a_ :		Dict							=  list(filter(lambda SCREAMING_SNAKE_CASE__					: [t[0]] == tokenizer.encode(t[1]					,							do_phonemize=SCREAMING_SNAKE_CASE__					)					,							SCREAMING_SNAKE_CASE__					)					)
             if max_length is not None and len(SCREAMING_SNAKE_CASE__					) > max_length:
                    a_ :		str							=  toks[:max_length]
             if min_length is not None and len(SCREAMING_SNAKE_CASE__					) < min_length and len(SCREAMING_SNAKE_CASE__					) > 0:
                    while len(SCREAMING_SNAKE_CASE__					) < min_length:
                           a_ :		List[Any]							=  toks + toks
        # toks_str = [t[1] for t in toks]
             a_ :		Optional[int]							=  [t[0] for t in toks]

             # Ensure consistency
             a_ :		Optional[int]							=  tokenizer.decode(SCREAMING_SNAKE_CASE__					,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__					)
             if " " not in output_txt and len(SCREAMING_SNAKE_CASE__					) > 1:
                    a_ :		Tuple							=  (
                        tokenizer.decode([toks_ids[0]]					,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__					)
                        + ' '
                        + tokenizer.decode(toks_ids[1:]					,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__					)
                    )
             if with_prefix_space:
                    a_ :		List[str]							=  ' ' + output_txt
             a_ :		Union[str, Any]							=  tokenizer.encode(SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)
             return output_txt, output_ids
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							**SCREAMING_SNAKE_CASE__    :	List[str]					)		->      str:
             kwargs.update(self.special_tokens_map					)
             return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Optional[int]:
             a_ :		Tuple							=  self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

             # check adding a single token
             tokenizer.add_tokens('xxx'					)
             a_ :		Optional[int]							=  tokenizer('m xxx ɪ'					,							do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							[1_3, 3_9_2, 1_7]					)  # xxx should be last token

             tokenizer.add_tokens(['aaa', 'bbb', 'ccc']					)
             a_ :		str							=  tokenizer('m aaa ɪ ccc'					,							do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							[1_3, 3_9_3, 1_7, 3_9_5]					)  # aaa and ccc should be after xxx and 2 after aaa

             a_ :		str							=  tokenizer('maɪ c'					,							do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							[3, 2_0_0]					)  # mai should be <unk> (=3)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[int]:
             a_ :		Optional[int]							=  self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

             a_ :		Union[str, Any]							=  'Hello how are you'
             a_ :		List[str]							=  tokenizer.phonemize(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							'h ə l oʊ h aʊ ɑːɹ j uː'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[Any]:
             a_ :		Any							=  self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

             a_ :		Union[str, Any]							=  'Hello how are you'
             a_ :		int							=  tokenizer.phonemize(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					)
             self.assertEqual(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids					,							tokenizer(SCREAMING_SNAKE_CASE__					,							do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[str]:
             a_ :		Optional[int]							=  self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)
             a_ :		int							=  'Hello how are you'
             a_ :		Optional[int]							=  tokenizer.phonemize(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					)

             a_ :		int							=  tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids					)

             self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      List[Any]:
             a_ :		int							=  self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

             a_ :		Tuple							=  [
                 [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],
             ]
             a_ :		Any							=  tokenizer.decode(sample_ids[0]					)
             a_ :		Tuple							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							batch_tokens[0]					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Dict:
             a_ :		List[Any]							=  self.tokenizer_class.from_pretrained(
                 'facebook/wav2vec2-lv-60-espeak-cv-ft'					,							word_delimiter_token='|'					)
             tokenizer.add_tokens('|'					)

             a_ :		Union[str, Any]							=  'Hello how are you'
             a_ :		Optional[int]							=  tokenizer.phonemize(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							'h ə l oʊ | h aʊ | ɑːɹ | j uː |'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Optional[Any]:
             a_ :		Any							=  self.tokenizer_class.from_pretrained(
                 'facebook/wav2vec2-lv-60-espeak-cv-ft'					,							word_delimiter_token='|'					)
             tokenizer.add_tokens('|'					)

             a_ :		int							=  'Hello how are you'
             a_ :		Tuple							=  tokenizer.phonemize(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					)
             self.assertEqual(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids					,							tokenizer(SCREAMING_SNAKE_CASE__					,							do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[str]:
             a_ :		Optional[Any]							=  self.tokenizer_class.from_pretrained(
                 'facebook/wav2vec2-lv-60-espeak-cv-ft'					,							word_delimiter_token='|'					)
             tokenizer.add_tokens('|'					)

             # fmt: off
             a_ :		Union[str, 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
             a_ :		str							=  tokenizer.decode(sample_ids[0]					)
             a_ :		Union[str, Any]							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							batch_tokens[0]					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']					)

             # decode with no word_del_token filter
             a_ :		int							=  tokenizer.decode(sample_ids[0]					,							filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)
             a_ :		Tuple							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					,							filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							batch_tokens[0]					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      int:
             a_ :		Dict							=  self.tokenizer_class.from_pretrained(
                 'facebook/wav2vec2-lv-60-espeak-cv-ft'					,							word_delimiter_token='|'					)
             tokenizer.add_tokens('|'					)

             a_ :		str							=  'Hello how are you'
             a_ :		Optional[int]							=  tokenizer.phonemize(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					)

             a_ :		Union[str, Any]							=  tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids					,							filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)

             self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Tuple:
             a_ :		Optional[Any]							=  self.tokenizer_class.from_pretrained(
                 'facebook/wav2vec2-lv-60-espeak-cv-ft'					,							word_delimiter_token='|'					)
             tokenizer.add_tokens('|'					)

             a_ :		Dict							=  'Hello how are you'
             a_ :		List[str]							=  tokenizer.phonemize(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					)

             a_ :		Tuple							=  tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids					,							filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)

             self.assertEqual(' '.join([p.strip() for p in phonemes.split(' |'					)]					).strip()					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      List[Any]:
             a_ :		Tuple							=  self.tokenizer_class.from_pretrained(
                 'facebook/wav2vec2-lv-60-espeak-cv-ft'					,							word_delimiter_token=SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  'Hello how are you'

             a_ :		Tuple							=  tokenizer(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='en-us'					).input_ids
             a_ :		Dict							=  tokenizer(SCREAMING_SNAKE_CASE__					,							phonemizer_lang='fr-fr'					).input_ids

             self.assertNotEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  tokenizer.decode(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  tokenizer.decode(SCREAMING_SNAKE_CASE__					)

             self.assertEqual(SCREAMING_SNAKE_CASE__					,							'h ə l oʊ h aʊ ɑːɹ j uː'					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							'ɛ l o h aʊ a ʁ j u'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		str							=  self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)
             a_ :		Optional[int]							=  'Hello how Are you'
             a_ :		Any							=  'hello how are you'

             a_ :		Union[str, Any]							=  tokenizer(SCREAMING_SNAKE_CASE__					).input_ids
             a_ :		List[str]							=  tokenizer(SCREAMING_SNAKE_CASE__					).input_ids

             self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             a_ :		Tuple							=  self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)
             tokenizer.add_tokens(['!', '?']					)
             tokenizer.add_special_tokens({'cls_token': '$$$'}					)

             # fmt: off
             a_ :		Union[str, Any]							=  [
                 [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

             a_ :		str							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$']					)
      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(	SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	str					)		->      List[str]:
             a_ :		Optional[Any]							=  [d[key] for d in offsets]
             return retrieved_list
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
             a_ :		Tuple							=  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"
             a_ :		str							=  [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

             a_ :		int							=  tokenizer.decode(SCREAMING_SNAKE_CASE__					,							output_char_offsets=SCREAMING_SNAKE_CASE__					,							filter_word_delimiter_token=SCREAMING_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(SCREAMING_SNAKE_CASE__					,							SCREAMING_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    :	List[Any]					)		->      int:
             a_ :		Optional[int]							=  self.get_tokenizer(word_delimiter_token='|'					)

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

                    # transform list to ModelOutput
                    a_ :		List[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(SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					):
                           if isinstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
                                  [recursive_check(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					) for la, la in zip(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)]
                           self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

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

        # fmt: off
             a_ :		Union[str, Any]							=  [
                 [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
             a_ :		str							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					,							output_char_offsets=SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  [tokenizer.decode(SCREAMING_SNAKE_CASE__					,							output_char_offsets=SCREAMING_SNAKE_CASE__					) for ids in sample_ids]
             check_list_tuples_equal(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      @unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[int]:
             pass
      @unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      int:
             pass
      @unittest.skip('encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      int:
             pass
      @unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Tuple:
             pass
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[str]:
             a_ :		Tuple							=  self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__					)
             for tokenizer in tokenizers:
                    with self.subTest(F"""{tokenizer.__class__.__name__}"""					):
                           a_ :		Any							=  tokenizer.vocab_size
                           a_ :		List[str]							=  len(SCREAMING_SNAKE_CASE__					)

                           self.assertNotEqual(SCREAMING_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)

                           a_ :		Any							=  ['aaaaa bbbbbb', 'cccccccccdddddddd']
                           a_ :		int							=  tokenizer.add_tokens(SCREAMING_SNAKE_CASE__					)
                           a_ :		List[str]							=  tokenizer.vocab_size
                           a_ :		Optional[int]							=  len(SCREAMING_SNAKE_CASE__					)

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

                           a_ :		Tuple							=  tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

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

                           a_ :		List[Any]							=  {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
                           a_ :		Optional[Any]							=  tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE__					)
                           a_ :		Dict							=  tokenizer.vocab_size
                           a_ :		Optional[Any]							=  len(SCREAMING_SNAKE_CASE__					)

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

                           a_ :		List[str]							=  tokenizer.encode(
                               '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

                           self.assertGreaterEqual(len(SCREAMING_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    :	Dict					)		->      Any:
             pass
      @unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Union[str, Any]:
             pass

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

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






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase_				:			str   =	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_				:			Optional[int]   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Optional[Any]							=  parser.parse_args()
       return args.f







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      List[Any]="eval"    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		List[Any]							=  os.path.join(__A  ,   F"""{split}_results.json"""    )
       if os.path.exists(__A    ):
              with open(__A  ,   'r'    ) as f:
                     return json.load(__A    )
       raise ValueError(F"""can't find {path}"""    )


UpperCAmelCase_				:			str   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             a_ :		Optional[Any]							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_glue.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		Union[str, Any]							=  F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_clm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							1_0_0					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Tuple							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_summarization_flax.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					,							split='test'					)
                    self.assertGreaterEqual(result['test_rouge1']					,							1_0					)
                    self.assertGreaterEqual(result['test_rouge2']					,							2					)
                    self.assertGreaterEqual(result['test_rougeL']					,							7					)
                    self.assertGreaterEqual(result['test_rougeLsum']					,							7					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		int							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_mlm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							4_2					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             a_ :		str							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_ta_mlm_flax.main()
                    a_ :		Dict							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.42					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_ :		int							=  7 if get_gpu_count() > 1 else 2

             a_ :		Dict							=  self.get_auto_remove_tmp_dir()
             a_ :		Tuple							=  F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_ner.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
                    self.assertGreaterEqual(result['eval_f1']					,							0.3					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		int							=  F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_qa.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_f1']					,							3_0					)
                    self.assertGreaterEqual(result['eval_exact']					,							3_0					)


