




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				None
    UpperCAmelCase__							  =				BloomTokenizerFast
    UpperCAmelCase__							  =				BloomTokenizerFast
    UpperCAmelCase__							  =				True
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				'''tokenizer_file'''
    UpperCAmelCase__							  =				{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}


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





           '''simple docstring'''

           super().setUp()
           A__							=   BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''')
           tokenizer.save_pretrained(self.tmpdirname)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			**UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

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


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





           '''simple docstring'''

           A__							=   self.get_rust_tokenizer()

           A__							=   ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
           A__							=   [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

           A__							=   tokenizer.batch_encode_plus(UpperCAmelCase__)['''input_ids''']
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   tokenizer.batch_decode(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     List[Any]=6)						->Any:





           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                         A__							=   self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ ,			**UpperCAmelCase__)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         A__							=   '''This is a simple input'''
                         A__							=   ['''This is a simple input 1''', '''This is a simple input 2''']
                         A__							=   ('''This is a simple input''', '''This is a pair''')
                         A__							=   [
                             ('''This is a simple input 1''', '''This is a simple input 2'''),
                             ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(UpperCAmelCase__ ,			max_length=UpperCAmelCase__)
                                tokenizer_r.encode_plus(UpperCAmelCase__ ,			max_length=UpperCAmelCase__)

                                tokenizer_r.batch_encode_plus(UpperCAmelCase__ ,			max_length=UpperCAmelCase__)
                                tokenizer_r.encode(UpperCAmelCase__ ,			max_length=UpperCAmelCase__)
                                tokenizer_r.batch_encode_plus(UpperCAmelCase__ ,			max_length=UpperCAmelCase__)
                         except ValueError:
                                self.fail('''Bloom Tokenizer should be able to deal with padding''')

                         A__							=   None  # Hotfixing padding = None
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Simple input
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Simple input
                         self.assertRaises(
                             UpperCAmelCase__ ,			tokenizer_r.batch_encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''' ,			)

                         # Pair input
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Pair input
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Pair input
                         self.assertRaises(
                             UpperCAmelCase__ ,			tokenizer_r.batch_encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''' ,			)


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





           '''simple docstring'''

           A__							=   self.get_rust_tokenizer()
           A__							=   load_dataset('''xnli''' ,			'''all_languages''' ,			split='''test''' ,			streaming=UpperCAmelCase__)

           A__							=   next(iter(UpperCAmelCase__))['''premise''']  # pick up one data
           A__							=   list(sample_data.values())

           A__							=   list(map(tokenizer.encode ,			UpperCAmelCase__))
           A__							=   [tokenizer.decode(UpperCAmelCase__ ,			clean_up_tokenization_spaces=UpperCAmelCase__) for x in output_tokens]
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)





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





           '''simple docstring'''

           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map) ,			1)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]) ,			1)








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()


