
"""simple docstring"""




import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing

class 		a__						(      a_						):
   def __init__(			self	,    _a = "▁"	,    _a = True	,    _a = "<unk>"	,    _a = "</s>"	,    _a = "<pad>"	,    ):
          lowercase    :				Union[str, Any]       		=							{
              "pad": {"id": 0, "token": pad_token},
              "eos": {"id": 1, "token": eos_token},
              "unk": {"id": 2, "token": unk_token},
          }

          lowercase    :				Union[str, Any]       		=							[None] * len(self.special_tokens				)
          for token_dict in self.special_tokens.values():
                 lowercase    :				Union[str, Any]       		=							token_dict["token"]

          lowercase    :				Any       		=							Tokenizer(Unigram()				)

          lowercase    :				Optional[int]       		=							normalizers.Sequence(
              [
                  normalizers.Nmt(),
                  normalizers.NFKC(),
                  normalizers.Replace(Regex(" {2,}"				)	,    " "				),
                  normalizers.Lowercase(),
              ]				)
          lowercase    :				int       		=							pre_tokenizers.Sequence(
              [
                  pre_tokenizers.Metaspace(replacement=_a	,    add_prefix_space=_a				),
                  pre_tokenizers.Digits(individual_digits=_a				),
                  pre_tokenizers.Punctuation(),
              ]				)
          lowercase    :				Dict       		=							decoders.Metaspace(replacement=_a	,    add_prefix_space=_a				)

          lowercase    :				Tuple       		=							TemplateProcessing(
              single=f"""$A {self.special_tokens["eos"]["token"]}"""	,    special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]	,    )

          lowercase    :				int       		=							{
              "model": "SentencePieceUnigram",
              "replacement": replacement,
              "add_prefix_space": add_prefix_space,
          }

          super().__init__(_a	,    _a				)
   def 			__magic_name__			(			self	,    _a	,    _a = 8_000	,    _a = True	,    ):
          lowercase    :				Dict       		=							trainers.UnigramTrainer(
              vocab_size=_a	,    special_tokens=self.special_tokens_list	,    show_progress=_a	,    )

          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[int]       		=							[files]
          self._tokenizer.train(_a	,    trainer=_a				)

          self.add_unk_id()
   def 			__magic_name__			(			self	,    _a	,    _a = 8_000	,    _a = True	,    ):
          lowercase    :				int       		=							trainers.UnigramTrainer(
              vocab_size=_a	,    special_tokens=self.special_tokens_list	,    show_progress=_a	,    )

          self._tokenizer.train_from_iterator(_a	,    trainer=_a				)

          self.add_unk_id()

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							json.loads(self._tokenizer.to_str()				)

          lowercase    :				List[str]       		=							self.special_tokens["unk"]["id"]

          lowercase    :				Tuple       		=							Tokenizer.from_str(json.dumps(_a				)				)




"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"laion/clap-htsat-unfused"
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
   def 			__magic_name__			(			self	,    **_a				):
          return RobertaTokenizer.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ClapFeatureExtractor.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_tokenizer()
          lowercase    :				List[Any]       		=							self.get_feature_extractor()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				int       		=							ClapProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							ClapProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				Union[str, Any]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[int]       		=							self.get_feature_extractor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Dict       		=							ClapProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				int       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Dict       		=							floats_list((3, 1_000)				)

          lowercase    :				str       		=							feature_extractor(_a	,    return_tensors="np"				)
          lowercase    :				Dict       		=							processor(audios=_a	,    return_tensors="np"				)

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1E-2				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_feature_extractor()
          lowercase    :				int       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Optional[Any]       		=							"This is a test string"

          lowercase    :				Any       		=							processor(text=_a				)

          lowercase    :				List[Any]       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_feature_extractor()
          lowercase    :				Any       		=							self.get_tokenizer()

          lowercase    :				Union[str, Any]       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Union[str, Any]       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				str       		=							processor.batch_decode(_a				)
          lowercase    :				Optional[int]       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				Any       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          self.assertListEqual(
              processor.model_input_names[2:]	,    feature_extractor.model_input_names	,    msg="`processor` and `feature_extractor` model input names do not match"	,    )


