






import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list


class 		lowerCAmelCase_							(     unittest.TestCase   ):





     def 	lowerCamelCase       (  self     )		->							Union[str, Any]:
        lowerCamelCase											="""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """     )
        lowerCamelCase											=dict(zip(_UpperCAmelCase       ,   range(len(_UpperCAmelCase     )     )     )     )

        lowerCamelCase											={
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        lowerCamelCase											={
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        lowerCamelCase											=tempfile.mkdtemp()
        lowerCamelCase											=os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""vocab_file"""]     )
        lowerCamelCase											=os.path.join(self.tmpdirname       ,   _UpperCAmelCase     )
        with open(self.vocab_file       ,   """w"""       ,   encoding="""utf-8"""     ) as fp:
           fp.write(json.dumps(_UpperCAmelCase     ) + """\n"""     )

        with open(self.feature_extraction_file       ,   """w"""       ,   encoding="""utf-8"""     ) as fp:
           fp.write(json.dumps(_UpperCAmelCase     ) + """\n"""     )





     def 	lowerCamelCase       (  self       ,   **_UpperCAmelCase     )		->							List[str]:
        lowerCamelCase											=self.add_kwargs_tokens_map.copy()
        kwargs.update(_UpperCAmelCase     )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,   **_UpperCAmelCase     )





     def 	lowerCamelCase       (  self       ,   **_UpperCAmelCase     )		->							Dict:
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,   **_UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							Tuple:
        shutil.rmtree(self.tmpdirname     )





     def 	lowerCamelCase       (  self     )		->							Union[str, Any]:
        lowerCamelCase											=self.get_tokenizer()
        lowerCamelCase											=self.get_feature_extractor()

        lowerCamelCase											=WavaVecaProcessor(tokenizer=_UpperCAmelCase       ,   feature_extractor=_UpperCAmelCase     )

        processor.save_pretrained(self.tmpdirname     )
        lowerCamelCase											=WavaVecaProcessor.from_pretrained(self.tmpdirname     )

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

        self.assertEqual(processor.feature_extractor.to_json_string()       ,   feature_extractor.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor       ,   _UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							Union[str, Any]:
        lowerCamelCase											=WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,   feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        lowerCamelCase											=self.get_tokenizer(bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""     )
        lowerCamelCase											=self.get_feature_extractor(do_normalize=_UpperCAmelCase       ,   padding_value=1.0     )

        lowerCamelCase											=WavaVecaProcessor.from_pretrained(
            self.tmpdirname       ,   bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""       ,   do_normalize=_UpperCAmelCase       ,   padding_value=1.0     )

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

        self.assertEqual(processor.feature_extractor.to_json_string()       ,   feature_extractor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor       ,   _UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							int:
        lowerCamelCase											=self.get_feature_extractor()
        lowerCamelCase											=self.get_tokenizer()

        lowerCamelCase											=WavaVecaProcessor(tokenizer=_UpperCAmelCase       ,   feature_extractor=_UpperCAmelCase     )

        lowerCamelCase											=floats_list((3, 1000)     )

        lowerCamelCase											=feature_extractor(_UpperCAmelCase       ,   return_tensors="""np"""     )
        lowerCamelCase											=processor(_UpperCAmelCase       ,   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 	lowerCamelCase       (  self     )		->							int:
        lowerCamelCase											=self.get_feature_extractor()
        lowerCamelCase											=self.get_tokenizer()

        lowerCamelCase											=WavaVecaProcessor(tokenizer=_UpperCAmelCase       ,   feature_extractor=_UpperCAmelCase     )

        lowerCamelCase											="""This is a test string"""

        lowerCamelCase											=processor(text=_UpperCAmelCase     )

        lowerCamelCase											=tokenizer(_UpperCAmelCase     )

        for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]       ,   encoded_processor[key]     )





     def 	lowerCamelCase       (  self     )		->							List[Any]:
        lowerCamelCase											=self.get_feature_extractor()
        lowerCamelCase											=self.get_tokenizer()

        lowerCamelCase											=WavaVecaProcessor(tokenizer=_UpperCAmelCase       ,   feature_extractor=_UpperCAmelCase     )

        lowerCamelCase											=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowerCamelCase											=processor.batch_decode(_UpperCAmelCase     )
        lowerCamelCase											=tokenizer.batch_decode(_UpperCAmelCase     )

        self.assertListEqual(_UpperCAmelCase       ,   _UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							int:
        lowerCamelCase											=self.get_feature_extractor()
        lowerCamelCase											=self.get_tokenizer()

        lowerCamelCase											=WavaVecaProcessor(tokenizer=_UpperCAmelCase       ,   feature_extractor=_UpperCAmelCase     )

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







import math
from decimal import Decimal, getcontext






def    UpperCAmelCase__	(						lowerCamelCase  : int = 4_00_00_00						):

   try:
      lowerCamelCase											=int(lowerCamelCase						)
   except (TypeError, ValueError):
      raise TypeError("""Parameter n must be int or castable to int."""						)
   if n <= 0:
      raise ValueError("""Parameter n must be greater than or equal to one."""						)
   lowerCamelCase											=1_00
   lowerCamelCase											=(Decimal(5						) ** Decimal(0.5						) + 1) / Decimal(2						)

   lowerCamelCase											=(math.floor(math.log(n * (phi + 2) ,lowerCamelCase						) - 1						) // 3) * 3 + 2
   lowerCamelCase											=Decimal(round(phi ** Decimal(index + 1						)						)						) / (phi + 2)
   lowerCamelCase											=num // 2
   return int(lowerCamelCase						)


if __name__ == "__main__":
   print(f"{solution() = }")