




"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				PhobertTokenizer
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->List[Any]:





           '''simple docstring'''

           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           A__							=   ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
           A__							=   dict(zip(UpperCAmelCase__ ,			range(len(UpperCAmelCase__))))
           A__							=   ['''#version: 0.2''', '''l à</w>''']
           A__							=   {'''unk_token''': '''<unk>'''}

           A__							=   os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES['''vocab_file'''])
           A__							=   os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES['''merges_file'''])

           with open(self.vocab_file ,			'''w''' ,			encoding='''utf-8''') as fp:
                  for token in vocab_tokens:
                         fp.write(f"""{token} {vocab_tokens[token]}\n""")
           with open(self.merges_file ,			'''w''' ,			encoding='''utf-8''') as fp:
                  fp.write('''\n'''.join(UpperCAmelCase__))


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			**UpperCAmelCase__				:     List[Any])						->Tuple:





           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return PhobertTokenizer.from_pretrained(self.tmpdirname ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[Any])						->Dict:





           '''simple docstring'''

           A__							=   '''Tôi là VinAI Research'''
           A__							=   '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
           return input_text, output_text





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->str:





           '''simple docstring'''

           A__							=   PhobertTokenizer(self.vocab_file ,			self.merges_file ,			**self.special_tokens_map)
           A__							=   '''Tôi là VinAI Research'''
           A__							=   '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
           A__							=   tokenizer.tokenize(UpperCAmelCase__)
           print(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   tokens + [tokenizer.unk_token]

           A__							=   [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__) ,			UpperCAmelCase__)








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     float)						->float:





           '''simple docstring'''

           return 0.0







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	tuple[int | float, int | float]:



       """simple docstring"""

       A__							=   min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
       A__							=   max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
       return lowest, highest







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.abs(np.fft.fft(lowercase_						)						)
       A__							=   20 * np.logaa(lowercase_						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       # Display within reasonable bounds
       A__							=   get_bounds(lowercase_			,    lowercase_						)
       plt.ylim(max([-80, bounds[0]]						)			,    min([80, bounds[1]]						)						)
       plt.ylabel('''Gain (dB)'''						)

       plt.plot(lowercase_						)
       plt.show()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.angle(np.fft.fft(lowercase_						)						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       plt.ylim(-2 * pi			,    2 * pi						)
       plt.ylabel('''Phase shift (Radians)'''						)
       plt.plot(np.unwrap(lowercase_			,    -2 * pi						)						)
       plt.show()


