"""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							:			Optional[int]							=		WavaVecaPhonemeCTCTokenizer
     _UpperCamelCase							:			Optional[int]							=		False






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

       _lowerCAmelCase						:       Union[str, 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(""" """  )
       _lowerCAmelCase						:       str  			=     dict(zip(a__				,     range(len(a__  )  )  )  )

       _lowerCAmelCase						:       Union[str, Any]  			=     {"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

       _lowerCAmelCase						:       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(a__  ) + """\n"""  )






     def     __A  (		self				,     a__				,     a__=False				,     a__=20				,     a__=5  ):
       _lowerCAmelCase						:       Dict  			=     [(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						:       Union[str, Any]  			=     toks[:max_length]
       if min_length is not None and len(a__  ) < min_length and len(a__  ) > 0:
         while len(a__  ) < min_length:
           _lowerCAmelCase						:       Optional[int]  			=     toks + toks
        # toks_str = [t[1] for t in toks]
       _lowerCAmelCase						:       str  			=     [t[0] for t in toks]

       # Ensure consistency
       _lowerCAmelCase						:       Optional[int]  			=     tokenizer.decode(a__				,     clean_up_tokenization_spaces=a__  )
       if " " not in output_txt and len(a__  ) > 1:
         _lowerCAmelCase						:       Optional[int]  			=     (
             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						:       str  			=     """ """ + output_txt
       _lowerCAmelCase						:       Any  			=     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						:       List[str]  			=     self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""  )

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

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

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






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

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






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

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

       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer.decode(tokenizer(a__  ).input_ids  )

       self.assertEqual(a__				,     a__  )






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

       _lowerCAmelCase						:       int  			=     [
           [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						:       List[str]  			=     tokenizer.batch_decode(a__  )
       self.assertEqual(a__				,     batch_tokens[0]  )
       self.assertEqual(a__				,     ["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]  )






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

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






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

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






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

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

       # decode with word_del_token filter
       _lowerCAmelCase						:       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						:       Dict  			=     tokenizer.decode(sample_ids[0]				,     filter_word_delimiter_token=a__  )
       _lowerCAmelCase						:       Dict  			=     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						:       str  			=     self.tokenizer_class.from_pretrained(
           """facebook/wav2vec2-lv-60-espeak-cv-ft"""				,     word_delimiter_token="""|"""  )
       tokenizer.add_tokens("""|"""  )

       _lowerCAmelCase						:       Tuple  			=     """Hello how are you"""
       _lowerCAmelCase						:       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						:       Union[str, Any]  			=     self.tokenizer_class.from_pretrained(
           """facebook/wav2vec2-lv-60-espeak-cv-ft"""				,     word_delimiter_token="""|"""  )
       tokenizer.add_tokens("""|"""  )

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

       _lowerCAmelCase						:       int  			=     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						:       str  			=     self.tokenizer_class.from_pretrained(
           """facebook/wav2vec2-lv-60-espeak-cv-ft"""				,     word_delimiter_token=a__  )
       _lowerCAmelCase						:       Any  			=     """Hello how are you"""

       _lowerCAmelCase						:       str  			=     tokenizer(a__				,     phonemizer_lang="""en-us"""  ).input_ids
       _lowerCAmelCase						:       Dict  			=     tokenizer(a__				,     phonemizer_lang="""fr-fr"""  ).input_ids

       self.assertNotEqual(a__				,     a__  )

       _lowerCAmelCase						:       Dict  			=     tokenizer.decode(a__  )
       _lowerCAmelCase						:       List[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						:       Optional[Any]  			=     """Hello how Are you"""
       _lowerCAmelCase						:       str  			=     """hello how are you"""

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

       self.assertEqual(a__				,     a__  )






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

       # fmt: off
       _lowerCAmelCase						:       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						:       List[Any]  			=     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						:       List[str]  			=     [d[key] for d in offsets]
       return retrieved_list






     def     __A  (		self  ):
       _lowerCAmelCase						:       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"
       _lowerCAmelCase						:       str  			=     [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						:       Optional[int]  			=     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						:       int  			=     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						:       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(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						:       Tuple  			=     [
           [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						:       Optional[int]  			=     tokenizer.batch_decode(a__				,     output_char_offsets=a__  )
       _lowerCAmelCase						:       str  			=     [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[Any]  			=     self.get_tokenizers(do_lower_case=a__  )
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           _lowerCAmelCase						:       int  			=     tokenizer.vocab_size
           _lowerCAmelCase						:       List[str]  			=     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						:       Optional[int]  			=     ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
           _lowerCAmelCase						:       str  			=     tokenizer.add_tokens(a__  )
           _lowerCAmelCase						:       Any  			=     tokenizer.vocab_size
           _lowerCAmelCase						:       str  			=     len(a__  )

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

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

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

           _lowerCAmelCase						:       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  ):
       # 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						:       Optional[int]  			=     self.get_tokenizers(fast=a__				,     do_lower_case=a__  )
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           _lowerCAmelCase						:       str  			=     ["""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 inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import UperNetForSemanticSegmentation
				from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=13						,__UpperCAmelCase=32						,__UpperCAmelCase=3						,__UpperCAmelCase=4						,__UpperCAmelCase=[10, 20, 30, 40]						,__UpperCAmelCase=[2, 2, 3, 2]						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=37						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=10						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=["stage2", "stage3", "stage4"]						,__UpperCAmelCase=3						,__UpperCAmelCase=None						,)    -> Optional[int]:
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							image_size
													A__		   =							num_channels
													A__		   =							num_stages
													A__		   =							hidden_sizes
													A__		   =							depths
													A__		   =							is_training
													A__		   =							use_labels
													A__		   =							intermediate_size
													A__		   =							hidden_act
													A__		   =							type_sequence_label_size
													A__		   =							initializer_range
													A__		   =							out_features
													A__		   =							num_labels
													A__		   =							scope
													A__		   =							num_stages





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

													A__		   =							None
													if self.use_labels:
																				A__		   =							ids_tensor([self.batch_size]						,self.type_sequence_label_size   )

													A__		   =							self.get_config()

													return config, pixel_values, labels





						def  snake_case__		(				self   )    -> str:
													return ConvNextConfig(
													    num_channels=self.num_channels						,num_stages=self.num_stages						,hidden_sizes=self.hidden_sizes						,depths=self.depths						,is_training=self.is_training						,intermediate_size=self.intermediate_size						,hidden_act=self.hidden_act						,out_features=self.out_features						,)





						def  snake_case__		(				self   )    -> Tuple:
													return UperNetConfig(
													    backbone_config=self.get_backbone_config()						,hidden_size=5_12						,pool_scales=[1, 2, 3, 6]						,use_auxiliary_head=__UpperCAmelCase						,auxiliary_loss_weight=0.4						,auxiliary_in_channels=40						,auxiliary_channels=2_56						,auxiliary_num_convs=1						,auxiliary_concat_input=__UpperCAmelCase						,loss_ignore_index=2_55						,num_labels=self.num_labels						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							UperNetForSemanticSegmentation(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(
													    result.logits.shape						,(self.batch_size, self.num_labels, self.image_size, self.image_size)   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							self.prepare_config_and_inputs()
													(
													    (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     
													)		   =							config_and_inputs
													A__		   =							{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class 			UpperCamelCase__(	__A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  int  							=						(UperNetForSemanticSegmentation,) if is_torch_available() else ()
						lowerCAmelCase__     :  int  							=						{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
						lowerCAmelCase__     :  Optional[int]  							=						False
						lowerCAmelCase__     :  List[Any]  							=						False
						lowerCAmelCase__     :  Tuple  							=						False
						lowerCAmelCase__     :  Optional[Any]  							=						False
						lowerCAmelCase__     :  Union[str, Any]  							=						False
						lowerCAmelCase__     :  Dict  							=						False





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							UperNetModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,has_text_modality=__UpperCAmelCase						,hidden_size=37   )





						def  snake_case__		(				self   )    -> List[Any]:
													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()





						def  snake_case__		(				self   )    -> int:
													return





						def  snake_case__		(				self   )    -> List[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							model_class(__UpperCAmelCase   )
																				A__		   =							inspect.signature(model.forward   )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				A__		   =							[*signature.parameters.keys()]

																				A__		   =							['pixel_values']
																				self.assertListEqual(arg_names[:1]						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase   )





						@unittest.skip(reason='UperNet does not use inputs_embeds'   )
						def  snake_case__		(				self   )    -> Optional[int]:
													pass





						@unittest.skip(reason='UperNet does not support input and output embeddings'   )
						def  snake_case__		(				self   )    -> Tuple:
													pass





						@unittest.skip(reason='UperNet does not have a base model'   )
						def  snake_case__		(				self   )    -> List[Any]:
													pass





						@unittest.skip(reason='UperNet does not have a base model'   )
						def  snake_case__		(				self   )    -> Dict:
													pass





						@require_torch_multi_gpu
						@unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'   )
						def  snake_case__		(				self   )    -> Any:
													pass





						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
						def  snake_case__		(				self   )    -> Dict:
													pass





						def  snake_case__		(				self   )    -> Optional[int]:
													def check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   ):
																				A__		   =							model_class(__UpperCAmelCase   )
																				model.to(__UpperCAmelCase   )
																				model.eval()

																				with torch.no_grad():
																											A__		   =							model(**self._prepare_for_class(__UpperCAmelCase						,__UpperCAmelCase   )   )

																				A__		   =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				A__		   =							self.model_tester.num_stages
																				self.assertEqual(len(__UpperCAmelCase   )						,expected_num_stages + 1   )

																				# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-2:]   )						,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,)

													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							True
																				check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__		   =							True

																				check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													A__		   =							_config_zero_init(__UpperCAmelCase   )
													A__		   =							_config_zero_init(configs_no_init.backbone_config   )
													for model_class in self.all_model_classes:
																				A__		   =							model_class(config=__UpperCAmelCase   )
																				for name, param in model.named_parameters():
																											if param.requires_grad:
																																		self.assertIn(
																																		    ((param.data.mean() * 1e9).round() / 1e9).item()						,[0.0, 1.0]						,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''						,)





						@unittest.skip(reason='UperNet does not have tied weights'   )
						def  snake_case__		(				self   )    -> str:
													pass





						@slow
						def  snake_case__		(				self   )    -> List[str]:
													for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__		   =							UperNetForSemanticSegmentation.from_pretrained(__UpperCAmelCase   )
																				self.assertIsNotNone(__UpperCAmelCase   )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							hf_hub_download(
							    repo_id='hf-internal-testing/fixtures_ade20k'				,			repo_type='dataset'				,			filename='ADE_val_00000001.jpg' )
							A__		   =							Image.open(UpperCamelCase__ ).convert('RGB' )
							return image


@require_torch
@require_vision
@slow
class 			UpperCamelCase__(	unittest.TestCase       ):





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'   )
													A__		   =							UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'   ).to(__UpperCAmelCase   )

													A__		   =							prepare_img()
													A__		   =							processor(images=__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

													A__		   =							torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
													self.assertEqual(outputs.logits.shape						,__UpperCAmelCase   )

													A__		   =							torch.tensor(
													    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,__UpperCAmelCase						,atol=1e-4   )   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'   )
													A__		   =							UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'   ).to(__UpperCAmelCase   )

													A__		   =							prepare_img()
													A__		   =							processor(images=__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

													A__		   =							torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
													self.assertEqual(outputs.logits.shape						,__UpperCAmelCase   )

													A__		   =							torch.tensor(
													    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,__UpperCAmelCase						,atol=1e-4   )   )





