import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


UpperCAmelCase      		=					get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')




@require_torch
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Tuple	)			->				Any:
        _UpperCamelCase  :							List[Any]	    =	tempfile.mkdtemp()

        _UpperCamelCase  :							str	    =	SpeechTaTokenizer(lowerCamelCase	)
        tokenizer.save_pretrained(self.tmpdirname	)

        _UpperCamelCase  :							Union[str, Any]	    =	{
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 1_6_0_0_0,
            'do_normalize': False,
            'num_mel_bins': 8_0,
            'hop_length': 1_6,
            'win_length': 6_4,
            'win_function': 'hann_window',
            'fmin': 8_0,
            'fmax': 7_6_0_0,
            'mel_floor': 1e-10,
            'reduction_factor': 2,
            'return_attention_mask': True,
        }

        _UpperCamelCase  :							List[str]	    =	os.path.join(self.tmpdirname							,      lowerCamelCase	)
        with open(self.feature_extraction_file							,      'w'							,      encoding='utf-8'	) as fp:
               fp.write(json.dumps(lowerCamelCase	) + '\n'	)

 def        __snake_case       (      self							:		Union[str, Any]							,      **lowerCamelCase							:		int	)			->				Tuple:
        return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]							,      **lowerCamelCase							:		Optional[int]	)			->				Optional[Any]:
        return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				str:
        shutil.rmtree(self.tmpdirname	)

 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        _UpperCamelCase  :							int	    =	self.get_tokenizer()
        _UpperCamelCase  :							Dict	    =	self.get_feature_extractor()

        _UpperCamelCase  :							Optional[Any]	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        processor.save_pretrained(self.tmpdirname	)
        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor.from_pretrained(self.tmpdirname	)

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

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

 def        __snake_case       (      self							:		str	)			->				int:
        _UpperCamelCase  :							Union[str, Any]	    =	SpeechTaProcessor(tokenizer=self.get_tokenizer()							,      feature_extractor=self.get_feature_extractor()	)
        processor.save_pretrained(self.tmpdirname	)

        _UpperCamelCase  :							Any	    =	self.get_tokenizer(bos_token='(BOS)'							,      eos_token='(EOS)'	)
        _UpperCamelCase  :							Any	    =	self.get_feature_extractor(do_normalize=lowerCamelCase							,      padding_value=1.0	)

        _UpperCamelCase  :							int	    =	SpeechTaProcessor.from_pretrained(
            self.tmpdirname							,      bos_token='(BOS)'							,      eos_token='(EOS)'							,      do_normalize=lowerCamelCase							,      padding_value=1.0	)

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

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

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Tuple:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							Dict	    =	self.get_tokenizer()

        _UpperCamelCase  :							Dict	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	floats_list((3, 1_0_0_0)	)

        _UpperCamelCase  :							Union[str, Any]	    =	feature_extractor(audio=lowerCamelCase							,      return_tensors='np'	)
        _UpperCamelCase  :							Optional[int]	    =	processor(audio=lowerCamelCase							,      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        __snake_case       (      self							:		List[str]	)			->				int:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							int	    =	self.get_tokenizer()

        _UpperCamelCase  :							Any	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	floats_list((3, 1_0_0_0)	)

        _UpperCamelCase  :							Any	    =	feature_extractor(audio_target=lowerCamelCase							,      return_tensors='np'	)
        _UpperCamelCase  :							List[Any]	    =	processor(audio_target=lowerCamelCase							,      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        __snake_case       (      self							:		Tuple	)			->				Any:
        _UpperCamelCase  :							Dict	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							List[str]	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	'This is a test string'

        _UpperCamelCase  :							Dict	    =	processor(text=lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	tokenizer(lowerCamelCase	)

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

 def        __snake_case       (      self							:		List[Any]	)			->				int:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	'This is a test string'

        _UpperCamelCase  :							Any	    =	processor(text_target=lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	tokenizer(lowerCamelCase	)

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

 def        __snake_case       (      self							:		List[str]	)			->				Union[str, Any]:
        _UpperCamelCase  :							Optional[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							str	    =	self.get_tokenizer()

        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							str	    =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :							str	    =	processor.batch_decode(lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	tokenizer.batch_decode(lowerCamelCase	)

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		str	)			->				Any:
        _UpperCamelCase  :							int	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							Union[str, Any]	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

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








'''simple docstring'''






from __future__ import annotations

A_				:					Tuple							=      """Alexander Joslin"""




def        snake_case_   (	lowerCAmelCase_						)->						int:





       '''simple docstring'''


       if number <= 0:
              _UpperCAmelCase    :		Union[str, Any]     	=					F'''n must be greater than 0. Got n = {number}'''
              raise ValueError(lowerCAmelCase_						)

       _UpperCAmelCase    :		List[str]     	=					[number + 1] * (number + 1)

       # starting position
       _UpperCAmelCase    :		Dict     	=					0
       for i in range(1			,  lowerCAmelCase_						):
              _UpperCAmelCase    :		int     	=					min(table[i + 1]			,  table[i] + 1						)
              # check if out of bounds
              if i * 2 <= number:
                     _UpperCAmelCase    :		Dict     	=					min(table[i * 2]			,  table[i] + 1						)
              # check if out of bounds
              if i * 3 <= number:
                     _UpperCAmelCase    :		Tuple     	=					min(table[i * 3]			,  table[i] + 1						)
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



