


"""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 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       WavaVecaPhonemeCTCTokenizer
    UpperCAmelCase       :							Dict	=       False






    def 	__snake_case							(					self				:    str):
      super().setUp()

      a						:		List[Any]      							=			(
          "<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(__UpperCAmelCase				, range(len(__UpperCAmelCase))))

      a						:		List[str]      							=			{"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

      a						:		Union[str, 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(__UpperCAmelCase) + "\n")






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    Optional[int]				, __UpperCAmelCase				:    Optional[int]=False				, __UpperCAmelCase				:    Optional[int]=20				, __UpperCAmelCase				:    Union[str, Any]=5):
      a						:		Any      							=			[(i, tokenizer.decode([i]				, clean_up_tokenization_spaces=__UpperCAmelCase)) for i in range(len(__UpperCAmelCase))]
      a						:		Dict      							=			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:
        a						:		List[str]      							=			toks[:max_length]
      if min_length is not None and len(__UpperCAmelCase) < min_length and len(__UpperCAmelCase) > 0:
        while len(__UpperCAmelCase) < min_length:
          a						:		Tuple      							=			toks + toks
             # toks_str = [t[1] for t in toks]
      a						:		Union[str, Any]      							=			[t[0] for t in toks]

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






    def 	__snake_case							(					self				:    Union[str, Any]				, **__UpperCAmelCase				:    int):
      kwargs.update(self.special_tokens_map)
      return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple):
      a						:		str      							=			self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")

      # check adding a single token
      tokenizer.add_tokens("xxx")
      a						:		List[Any]      							=			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"])
      a						:		Tuple      							=			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

      a						:		List[str]      							=			tokenizer("maɪ c"				, do_phonemize=__UpperCAmelCase).input_ids
      self.assertEqual(__UpperCAmelCase				, [3, 200])  # mai should be <unk> (=3)






    def 	__snake_case							(					self				:    List[str]):
      a						:		Optional[int]      							=			self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")

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






    def 	__snake_case							(					self				:    Dict):
      a						:		Tuple      							=			self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")

      a						:		Tuple      							=			"Hello how are you"
      a						:		Union[str, Any]      							=			tokenizer.phonemize(__UpperCAmelCase				, phonemizer_lang="en-us")
      self.assertEqual(tokenizer(__UpperCAmelCase).input_ids				, tokenizer(__UpperCAmelCase				, do_phonemize=__UpperCAmelCase).input_ids)






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

      a						:		Union[str, Any]      							=			tokenizer.decode(tokenizer(__UpperCAmelCase).input_ids)

      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)






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

      a						:		Dict      							=			[
          [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
          [24, 22, 5, 24, 22, 5, 77],
      ]
      a						:		Union[str, Any]      							=			tokenizer.decode(sample_ids[0])
      a						:		Tuple      							=			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				:    List[str]):
      a						:		Optional[Any]      							=			self.tokenizer_class.from_pretrained(
          "facebook/wav2vec2-lv-60-espeak-cv-ft"				, word_delimiter_token="|")
      tokenizer.add_tokens("|")

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






    def 	__snake_case							(					self				:    List[Any]):
      a						:		Dict      							=			self.tokenizer_class.from_pretrained(
          "facebook/wav2vec2-lv-60-espeak-cv-ft"				, word_delimiter_token="|")
      tokenizer.add_tokens("|")

      a						:		Tuple      							=			"Hello how are you"
      a						:		Union[str, Any]      							=			tokenizer.phonemize(__UpperCAmelCase				, phonemizer_lang="en-us")
      self.assertEqual(tokenizer(__UpperCAmelCase).input_ids				, tokenizer(__UpperCAmelCase				, do_phonemize=__UpperCAmelCase).input_ids)






    def 	__snake_case							(					self				:    Dict):
      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]      							=			[
          [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
      a						:		Dict      							=			tokenizer.decode(sample_ids[0])
      a						:		Optional[Any]      							=			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
      a						:		Any      							=			tokenizer.decode(sample_ids[0]				, filter_word_delimiter_token=__UpperCAmelCase)
      a						:		List[str]      							=			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				:    Tuple):
      a						:		List[str]      							=			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						:		List[Any]      							=			tokenizer.phonemize(__UpperCAmelCase				, phonemizer_lang="en-us")

      a						:		Any      							=			tokenizer.decode(tokenizer(__UpperCAmelCase).input_ids				, filter_word_delimiter_token=__UpperCAmelCase)

      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)






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

      a						:		Optional[Any]      							=			"Hello how are you"
      a						:		Optional[Any]      							=			tokenizer.phonemize(__UpperCAmelCase				, phonemizer_lang="en-us")

      a						:		Union[str, Any]      							=			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				:    Any):
      a						:		Dict      							=			self.tokenizer_class.from_pretrained(
          "facebook/wav2vec2-lv-60-espeak-cv-ft"				, word_delimiter_token=__UpperCAmelCase)
      a						:		List[str]      							=			"Hello how are you"

      a						:		Any      							=			tokenizer(__UpperCAmelCase				, phonemizer_lang="en-us").input_ids
      a						:		Dict      							=			tokenizer(__UpperCAmelCase				, phonemizer_lang="fr-fr").input_ids

      self.assertNotEqual(__UpperCAmelCase				, __UpperCAmelCase)

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

      a						:		Optional[int]      							=			tokenizer(__UpperCAmelCase).input_ids
      a						:		Optional[int]      							=			tokenizer(__UpperCAmelCase).input_ids

      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)






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

      # fmt: off
      a						:		List[str]      							=			[
          [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

      a						:		Union[str, Any]      							=			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				:    int				, __UpperCAmelCase				:    Union[str, Any]):
      a						:		Tuple      							=			[d[key] for d in offsets]
      return retrieved_list






    def 	__snake_case							(					self				:    Tuple):
      a						:		int      							=			self.get_tokenizer(word_delimiter_token="|")
      tokenizer.add_tokens("|")

      # fmt: off
      # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
      a						:		Dict      							=			[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

      a						:		Optional[Any]      							=			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				:    int):
      a						:		str      							=			self.get_tokenizer(word_delimiter_token="|")

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

        # transform list to ModelOutput
        a						:		str      							=			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				:    List[str]				, __UpperCAmelCase				:    List[str]):
          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
      a						:		Optional[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
      a						:		List[str]      							=			tokenizer.batch_decode(__UpperCAmelCase				, output_char_offsets=__UpperCAmelCase)
      a						:		Dict      							=			[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				:    Dict):
      pass






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






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






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






    def 	__snake_case							(					self				:    Dict):
      a						:		List[Any]      							=			self.get_tokenizers(do_lower_case=__UpperCAmelCase)
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''):
          a						:		Tuple      							=			tokenizer.vocab_size
          a						:		str      							=			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)

          a						:		Dict      							=			["aaaaa bbbbbb", "cccccccccdddddddd"]
          a						:		Optional[int]      							=			tokenizer.add_tokens(__UpperCAmelCase)
          a						:		List[Any]      							=			tokenizer.vocab_size
          a						:		str      							=			len(__UpperCAmelCase)

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

          a						:		Dict      							=			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)

          a						:		Optional[int]      							=			{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
          a						:		Any      							=			tokenizer.add_special_tokens(__UpperCAmelCase)
          a						:		Tuple      							=			tokenizer.vocab_size
          a						:		List[Any]      							=			len(__UpperCAmelCase)

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

          a						:		Any      							=			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				:    Dict):
      pass






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






    def 	__snake_case							(					self				:    int):
      a						:		Union[str, Any]      							=			self.get_tokenizers(fast=__UpperCAmelCase				, do_lower_case=__UpperCAmelCase)
      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						:		Optional[int]      							=			tokenizer.convert_tokens_to_string(__UpperCAmelCase)

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










'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
    lowerCamelCase_      =  self.scheduler.sigmas[i] * torch.ones(shape[0]			,  device=self.device     )

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



