"""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							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    int	      =	WavaVecaPhonemeCTCTokenizer
     _UpperCamelCase							:    Tuple	      =	False






     def 					__A						(	self						):
       super().setUp()

       _lowerCAmelCase		: Tuple						    =  (
           """<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		: Optional[Any]						    =  dict(zip(a__       ,  range(len(a__						)						)						)						)

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

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






     def 					__A						(	self       ,  a__       ,  a__=False       ,  a__=20       ,  a__=5						):
       _lowerCAmelCase		: List[Any]						    =  [(i, tokenizer.decode([i]       ,  clean_up_tokenization_spaces=a__						)) for i in range(len(a__						)						)]
       _lowerCAmelCase		: Union[str, Any]						    =  list(filter(lambda a__						: [t[0]] == tokenizer.encode(t[1]       ,  do_phonemize=a__						)       ,  a__						)						)
       if max_length is not None and len(a__						) > max_length:
         _lowerCAmelCase		: Tuple						    =  toks[:max_length]
       if min_length is not None and len(a__						) < min_length and len(a__						) > 0:
         while len(a__						) < min_length:
           _lowerCAmelCase		: List[Any]						    =  toks + toks
        # toks_str = [t[1] for t in toks]
       _lowerCAmelCase		: List[str]						    =  [t[0] for t in toks]

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






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""						)

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

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

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""						)

       _lowerCAmelCase		: Dict						    =  """Hello how are you"""
       _lowerCAmelCase		: int						    =  tokenizer.phonemize(a__       ,  phonemizer_lang="""en-us"""						)
       self.assertEqual(tokenizer(a__						).input_ids       ,  tokenizer(a__       ,  do_phonemize=a__						).input_ids						)






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""						)
       _lowerCAmelCase		: Dict						    =  """Hello how are you"""
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.phonemize(a__       ,  phonemizer_lang="""en-us"""						)

       _lowerCAmelCase		: Dict						    =  tokenizer.decode(tokenizer(a__						).input_ids						)

       self.assertEqual(a__       ,  a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""						)

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






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

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






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

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






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

       # fmt: off
       _lowerCAmelCase		: Tuple						    =  [
           [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		: Optional[Any]						    =  tokenizer.decode(sample_ids[0]						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.batch_decode(a__						)
       self.assertEqual(a__       ,  batch_tokens[0]						)
       self.assertEqual(a__       ,  ["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]						)

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






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

       _lowerCAmelCase		: Union[str, Any]						    =  """Hello how are you"""
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.phonemize(a__       ,  phonemizer_lang="""en-us"""						)

       _lowerCAmelCase		: Dict						    =  tokenizer.decode(tokenizer(a__						).input_ids       ,  filter_word_delimiter_token=a__						)

       self.assertEqual(a__       ,  a__						)






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

       _lowerCAmelCase		: int						    =  """Hello how are you"""
       _lowerCAmelCase		: Dict						    =  tokenizer.phonemize(a__       ,  phonemizer_lang="""en-us"""						)

       _lowerCAmelCase		: List[Any]						    =  tokenizer.decode(tokenizer(a__						).input_ids       ,  filter_word_delimiter_token=a__						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  self.tokenizer_class.from_pretrained(
           """facebook/wav2vec2-lv-60-espeak-cv-ft"""       ,  word_delimiter_token=a__						)
       _lowerCAmelCase		: List[Any]						    =  """Hello how are you"""

       _lowerCAmelCase		: Optional[int]						    =  tokenizer(a__       ,  phonemizer_lang="""en-us"""						).input_ids
       _lowerCAmelCase		: int						    =  tokenizer(a__       ,  phonemizer_lang="""fr-fr"""						).input_ids

       self.assertNotEqual(a__       ,  a__						)

       _lowerCAmelCase		: Optional[int]						    =  tokenizer.decode(a__						)
       _lowerCAmelCase		: str						    =  tokenizer.decode(a__						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""						)
       _lowerCAmelCase		: Dict						    =  """Hello how Are you"""
       _lowerCAmelCase		: Any						    =  """hello how are you"""

       _lowerCAmelCase		: Any						    =  tokenizer(a__						).input_ids
       _lowerCAmelCase		: Tuple						    =  tokenizer(a__						).input_ids

       self.assertEqual(a__       ,  a__						)






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

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

       _lowerCAmelCase		: Tuple						    =  tokenizer.batch_decode(a__						)
       self.assertEqual(a__       ,  ["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""]						)






     @staticmethod
     def 					__A						(	a__       ,  a__						):
       _lowerCAmelCase		: int						    =  [d[key] for d in offsets]
       return retrieved_list






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizer(word_delimiter_token="""|"""						)
       tokenizer.add_tokens("""|"""						)

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

       _lowerCAmelCase		: Tuple						    =  tokenizer.decode(a__       ,  output_char_offsets=a__       ,  filter_word_delimiter_token=a__						)
       # check Wav2Vec2CTCTokenizerOutput keys for char
       self.assertEqual(len(outputs.keys()						)       ,  2						)
       self.assertTrue("""text""" in outputs						)
       self.assertTrue("""char_offsets""" in outputs						)
       self.assertTrue(isinstance(a__       ,  a__						)						)

       # check that order of chars is correct and identical for both outputs
       self.assertEqual(""" """.join(self.get_from_offsets(outputs["""char_offsets"""]       ,  """char"""						)						)       ,  outputs.text						)
       self.assertListEqual(
           self.get_from_offsets(outputs["""char_offsets"""]       ,  """char"""						)       ,  ["""k""", """s""", """ɾ""", """ɾ""", """|""", """ɾ""", """l""", """|""", """ɭʲ"""]						)

       # check that offsets are actually correct for char
       # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
       # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
       self.assertListEqual(
           self.get_from_offsets(outputs["""char_offsets"""]       ,  """start_offset"""						)       ,  [0, 1, 4, 7, 9, 11, 12, 15, 16]						)
       self.assertListEqual(
           self.get_from_offsets(outputs["""char_offsets"""]       ,  """end_offset"""						)       ,  [1, 4, 6, 9, 10, 12, 15, 16, 17]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.get_tokenizer(word_delimiter_token="""|"""						)

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

         # transform list to ModelOutput
         _lowerCAmelCase		: 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(a__       ,  a__						):
           if isinstance(a__       ,  a__						):
             [recursive_check(a__       ,  a__						) for la, la in zip(a__       ,  a__						)]
           self.assertEqual(a__       ,  a__						)

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

        # fmt: off
       _lowerCAmelCase		: int						    =  [
           [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		: str						    =  tokenizer.batch_decode(a__       ,  output_char_offsets=a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  [tokenizer.decode(a__       ,  output_char_offsets=a__						) for ids in sample_ids]
       check_list_tuples_equal(a__       ,  a__						)






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






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






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






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






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizers(do_lower_case=a__						)
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"						):
           _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.vocab_size
           _lowerCAmelCase		: Optional[int]						    =  len(a__						)

           self.assertNotEqual(a__       ,  0						)

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

           _lowerCAmelCase		: List[Any]						    =  ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
           _lowerCAmelCase		: Any						    =  tokenizer.add_tokens(a__						)
           _lowerCAmelCase		: int						    =  tokenizer.vocab_size
           _lowerCAmelCase		: Optional[int]						    =  len(a__						)

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

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

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

           _lowerCAmelCase		: List[str]						    =  {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
           _lowerCAmelCase		: str						    =  tokenizer.add_special_tokens(a__						)
           _lowerCAmelCase		: Optional[Any]						    =  tokenizer.vocab_size
           _lowerCAmelCase		: List[str]						    =  len(a__						)

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

           _lowerCAmelCase		: Optional[Any]						    =  tokenizer.encode(
               """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""       ,  add_special_tokens=a__						)

           self.assertGreaterEqual(len(a__						)       ,  6						)
           self.assertGreater(tokens[0]       ,  tokenizer.vocab_size - 1						)
           self.assertGreater(tokens[0]       ,  tokens[1]						)
           self.assertGreater(tokens[-3]       ,  tokenizer.vocab_size - 1						)
           self.assertGreater(tokens[-3]       ,  tokens[-4]						)
           self.assertEqual(tokens[0]       ,  tokenizer.eos_token_id						)
           self.assertEqual(tokens[-3]       ,  tokenizer.pad_token_id						)






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






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






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.get_tokenizers(fast=a__       ,  do_lower_case=a__						)
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"						):
           _lowerCAmelCase		: Any						    =  ["""ð""", """ɪ""", """s""", """ɪ""", """z""", """ɐ""", """t""", """ɛ""", """k""", """s""", """t"""]
           _lowerCAmelCase		: List[str]						    =  tokenizer.convert_tokens_to_string(a__						)

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





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	DiTPipeline
     _UpperCamelCase							:    Union[str, Any]	      =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     _UpperCamelCase							:    Dict	      =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	False






     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: Tuple						    =  TransformeraDModel(
           sample_size=16       ,  num_layers=2       ,  patch_size=4       ,  attention_head_dim=8       ,  num_attention_heads=2       ,  in_channels=4       ,  out_channels=8       ,  attention_bias=a__       ,  activation_fn="""gelu-approximate"""       ,  num_embeds_ada_norm=1000       ,  norm_type="""ada_norm_zero"""       ,  norm_elementwise_affine=a__       ,  )
       _lowerCAmelCase		: Optional[int]						    =  AutoencoderKL()
       _lowerCAmelCase		: Union[str, Any]						    =  DDIMScheduler()
       _lowerCAmelCase		: Optional[Any]						    =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Any						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Tuple						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Any						    =  {
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  """cpu"""

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_components()
       _lowerCAmelCase		: Optional[int]						    =  self.pipeline_class(**a__						)
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_inputs(a__						)
       _lowerCAmelCase		: List[str]						    =  pipe(**a__						).images
       _lowerCAmelCase		: Optional[int]						    =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape       ,  (1, 16, 16, 3)						)
       _lowerCAmelCase		: List[Any]						    =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
       _lowerCAmelCase		: Optional[int]						    =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(a__       ,  1e-3						)






     def 					__A						(	self						):
       self._test_inference_batch_single_identical(relax_max_difference=a__       ,  expected_max_diff=1e-3						)






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()       ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,  )
     def 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







@require_torch_gpu
@slow
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  torch.manual_seed(0						)

       _lowerCAmelCase		: int						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: Dict						    =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
       _lowerCAmelCase		: Union[str, Any]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: Any						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=40       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"						)
         assert np.abs((expected_image - image).max()						) < 1e-2






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
       _lowerCAmelCase		: Dict						    =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: List[str]						    =  ["""vase""", """umbrella"""]
       _lowerCAmelCase		: Optional[int]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: str						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: List[str]						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=25       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             F"/dit/{word}_512.npy"						)

         assert np.abs((expected_image - image).max()						) < 1e-1




