


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__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Dict							    =     WavaVecaPhonemeCTCTokenizer
       lowerCamelCase__:     Any							    =     False

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            super().setUp()

            a_    :     Optional[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_    :     Optional[int]										=    dict(zip(lowerCamelCase_      ,							range(len(lowerCamelCase_    )    )    )    )

            a_    :     Tuple										=    {"""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(lowerCamelCase_    ) + """\n"""    )

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

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

       def 						UpperCAmelCase(   self      :     Dict      ,							**lowerCamelCase_      :     List[Any]    ):
            kwargs.update(self.special_tokens_map    )
            return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname      ,							**lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     List[Any]										=    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=lowerCamelCase_    ).input_ids
            self.assertEqual(lowerCamelCase_      ,							[1_3, 3_9_2, 1_7]    )  # xxx should be last token

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

            a_    :     Tuple										=    tokenizer("""maɪ c"""      ,							do_phonemize=lowerCamelCase_    ).input_ids
            self.assertEqual(lowerCamelCase_      ,							[3, 2_0_0]    )  # mai should be <unk> (=3)

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Any										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )

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

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

            a_    :     Dict										=    """Hello how are you"""
            a_    :     Optional[int]										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )
            self.assertEqual(tokenizer(lowerCamelCase_    ).input_ids      ,							tokenizer(lowerCamelCase_      ,							do_phonemize=lowerCamelCase_    ).input_ids    )

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

            a_    :     Dict										=    tokenizer.decode(tokenizer(lowerCamelCase_    ).input_ids    )

            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

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

            a_    :     Union[str, Any]										=    [
                [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_    :     Tuple										=    tokenizer.decode(sample_ids[0]    )
            a_    :     Tuple										=    tokenizer.batch_decode(lowerCamelCase_    )
            self.assertEqual(lowerCamelCase_      ,							batch_tokens[0]    )
            self.assertEqual(lowerCamelCase_      ,							["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]    )

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

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

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

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

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

            # fmt: off
            a_    :     Optional[int]										=    [
                [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_    :     List[str]										=    tokenizer.decode(sample_ids[0]    )
            a_    :     List[str]										=    tokenizer.batch_decode(lowerCamelCase_    )
            self.assertEqual(lowerCamelCase_      ,							batch_tokens[0]    )
            self.assertEqual(lowerCamelCase_      ,							["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]    )

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

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     List[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(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )

            a_    :     Optional[int]										=    tokenizer.decode(tokenizer(lowerCamelCase_    ).input_ids      ,							filter_word_delimiter_token=lowerCamelCase_    )

            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Union[str, Any]										=    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_    :     Tuple										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )

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

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

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Optional[Any]										=    self.tokenizer_class.from_pretrained(
                """facebook/wav2vec2-lv-60-espeak-cv-ft"""      ,							word_delimiter_token=lowerCamelCase_    )
            a_    :     Union[str, Any]										=    """Hello how are you"""

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

            self.assertNotEqual(lowerCamelCase_      ,							lowerCamelCase_    )

            a_    :     Union[str, Any]										=    tokenizer.decode(lowerCamelCase_    )
            a_    :     Union[str, Any]										=    tokenizer.decode(lowerCamelCase_    )

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

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     str										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )
            a_    :     Optional[Any]										=    """Hello how Are you"""
            a_    :     List[str]										=    """hello how are you"""

            a_    :     Tuple										=    tokenizer(lowerCamelCase_    ).input_ids
            a_    :     Any										=    tokenizer(lowerCamelCase_    ).input_ids

            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Dict										=    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_    :     int										=    tokenizer.batch_decode(lowerCamelCase_    )
            self.assertEqual(lowerCamelCase_      ,							["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""]    )

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Any    ):
            a_    :     Optional[Any]										=    [d[key] for d in offsets]
            return retrieved_list

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Dict										=    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_    :     Tuple										=    [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_    :     Union[str, Any]										=    tokenizer.decode(lowerCamelCase_      ,							output_char_offsets=lowerCamelCase_      ,							filter_word_delimiter_token=lowerCamelCase_    )
            # 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(lowerCamelCase_      ,							lowerCamelCase_    )    )

            # 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 						UpperCAmelCase(   self      :     Any    ):
            a_    :     List[Any]										=    self.get_tokenizer(word_delimiter_token="""|"""    )

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

                 # transform list to ModelOutput
                 a_    :     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(lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int    ):
                      if isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                           [recursive_check(lowerCamelCase_      ,							lowerCamelCase_    ) for la, la in zip(lowerCamelCase_      ,							lowerCamelCase_    )]
                      self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

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

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

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

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

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

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

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     int										=    self.get_tokenizers(do_lower_case=lowerCamelCase_    )
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                      a_    :     Tuple										=    tokenizer.vocab_size
                      a_    :     List[Any]										=    len(lowerCamelCase_    )

                      self.assertNotEqual(lowerCamelCase_      ,							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_    :     Union[str, Any]										=    ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
                      a_    :     Dict										=    tokenizer.add_tokens(lowerCamelCase_    )
                      a_    :     Union[str, Any]										=    tokenizer.vocab_size
                      a_    :     Optional[int]										=    len(lowerCamelCase_    )

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

                      a_    :     Dict										=    tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""      ,							add_special_tokens=lowerCamelCase_    )

                      self.assertGreaterEqual(len(lowerCamelCase_    )      ,							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_    :     Dict										=    tokenizer.add_special_tokens(lowerCamelCase_    )
                      a_    :     Optional[int]										=    tokenizer.vocab_size
                      a_    :     Tuple										=    len(lowerCamelCase_    )

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

                      a_    :     Union[str, Any]										=    tokenizer.encode(
                          """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""      ,							add_special_tokens=lowerCamelCase_    )

                      self.assertGreaterEqual(len(lowerCamelCase_    )      ,							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 						UpperCAmelCase(   self      :     Optional[int]    ):
            pass

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

       def 						UpperCAmelCase(   self      :     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_    :     Optional[int]										=    self.get_tokenizers(fast=lowerCamelCase_      ,							do_lower_case=lowerCamelCase_    )
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                      a_    :     List[str]										=    ["""ð""", """ɪ""", """s""", """ɪ""", """z""", """ɐ""", """t""", """ɛ""", """k""", """s""", """t"""]
                      a_    :     Tuple										=    tokenizer.convert_tokens_to_string(lowerCamelCase_    )

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







from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__lowerCamelCase        =     logging.get_logger(__name__)  # pylint: disable=invalid-name


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     List[str]      ,							lowerCamelCase_      :     CLIPSegForImageSegmentation      ,							lowerCamelCase_      :     CLIPSegProcessor      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     CLIPTextModel      ,							lowerCamelCase_      :     CLIPTokenizer      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,							lowerCamelCase_      :     StableDiffusionSafetyChecker      ,							lowerCamelCase_      :     CLIPImageProcessor      ,							):
            super().__init__()

            if hasattr(scheduler.config      ,							"""steps_offset"""    ) and scheduler.config.steps_offset != 1:
                 a_    :     Dict										=    (
                     F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                     F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                     """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
                     """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
                     """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
                     """ file"""
                 )
                 deprecate("""steps_offset!=1"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    dict(scheduler.config    )
                 a_    :     Optional[int]										=    1
                 a_    :     int										=    FrozenDict(lowerCamelCase_    )

            if hasattr(scheduler.config      ,							"""skip_prk_steps"""    ) and scheduler.config.skip_prk_steps is False:
                 a_    :     List[str]										=    (
                     F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
                     """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
                     """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
                     """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
                     """ Hub, it would be very nice if you could open a Pull request for the"""
                     """ `scheduler/scheduler_config.json` file"""
                 )
                 deprecate("""skip_prk_steps not set"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     List[str]										=    dict(scheduler.config    )
                 a_    :     List[str]										=    True
                 a_    :     str										=    FrozenDict(lowerCamelCase_    )

            if safety_checker is None:
                 logger.warning(
                     F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                     """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
                     """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
                     """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
                     """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
                     """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""    )

            self.register_modules(
                segmentation_model=lowerCamelCase_      ,							segmentation_processor=lowerCamelCase_      ,							vae=lowerCamelCase_      ,							text_encoder=lowerCamelCase_      ,							tokenizer=lowerCamelCase_      ,							unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							safety_checker=lowerCamelCase_      ,							feature_extractor=lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Optional[Union[str, int]] = "auto"    ):
            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 a_    :     Any										=    self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            self.enable_attention_slicing(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

            a_    :     Any										=    torch.device("""cuda"""    )

            for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
                 if cpu_offloaded_model is not None:
                      cpu_offload(lowerCamelCase_      ,							lowerCamelCase_    )

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						UpperCAmelCase(   self      :     int    ):
            if self.device != torch.device("""meta"""    ) or not hasattr(self.unet      ,							"""_hf_hook"""    ):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(lowerCamelCase_      ,							"""_hf_hook"""    )
                     and hasattr(module._hf_hook      ,							"""execution_device"""    )
                     and module._hf_hook.execution_device is not None
                 ):
                      return torch.device(module._hf_hook.execution_device    )
            return self.device

       @torch.no_grad()
       def __call__(   self      :     Any      ,							lowerCamelCase_      :     Union[str, List[str]]      ,							lowerCamelCase_      :     Union[torch.FloatTensor, PIL.Image.Image]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_0      ,							lowerCamelCase_      :     float = 7.5      ,							lowerCamelCase_      :     Optional[Union[str, List[str]]] = None      ,							lowerCamelCase_      :     Optional[int] = 1      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     Optional[torch.Generator] = None      ,							lowerCamelCase_      :     Optional[torch.FloatTensor] = None      ,							lowerCamelCase_      :     Optional[str] = "pil"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,							lowerCamelCase_      :     int = 1      ,							**lowerCamelCase_      :     Any      ,							):
            a_    :     str										=    self.segmentation_processor(
                text=[text]      ,							images=[image]      ,							padding="""max_length"""      ,							return_tensors="""pt"""    ).to(self.device    )
            a_    :     List[str]										=    self.segmentation_model(**lowerCamelCase_    )
            a_    :     str										=    torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
            a_    :     Optional[int]										=    self.numpy_to_pil(lowerCamelCase_    )[0].resize(image.size    )

            # Run inpainting pipeline with the generated mask
            a_    :     Any										=    StableDiffusionInpaintPipeline(
                vae=self.vae      ,							text_encoder=self.text_encoder      ,							tokenizer=self.tokenizer      ,							unet=self.unet      ,							scheduler=self.scheduler      ,							safety_checker=self.safety_checker      ,							feature_extractor=self.feature_extractor      ,							)
            return inpainting_pipeline(
                prompt=lowerCamelCase_      ,							image=lowerCamelCase_      ,							mask_image=lowerCamelCase_      ,							height=lowerCamelCase_      ,							width=lowerCamelCase_      ,							num_inference_steps=lowerCamelCase_      ,							guidance_scale=lowerCamelCase_      ,							negative_prompt=lowerCamelCase_      ,							num_images_per_prompt=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							latents=lowerCamelCase_      ,							output_type=lowerCamelCase_      ,							return_dict=lowerCamelCase_      ,							callback=lowerCamelCase_      ,							callback_steps=lowerCamelCase_      ,							)



