




'''simple docstring'''







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A									=get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')


@require_sentencepiece
@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  List[Any]            =      GPTSwaTokenizer
       __a       :  List[str]            =      False
       __a       :  str            =      True
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           super().setUp()

           # We have a SentencePiece fixture for testing
           UpperCAmelCase     =	GPTSwaTokenizer(lowercase     ,       eos_token='''<unk>'''     ,       bos_token='''<unk>'''     ,       pad_token='''<unk>'''       )

           tokenizer.save_pretrained(self.tmpdirname       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''This is a test'''
           UpperCAmelCase     =	'''This is a test'''
           return input_text, output_text
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''<s>'''
           UpperCAmelCase     =	1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase       )     ,       lowercase       )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase       )     ,       lowercase       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	list(self.get_tokenizer().get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]     ,       '''<unk>'''       )
           self.assertEqual(vocab_keys[1]     ,       '''<s>'''       )
           self.assertEqual(vocab_keys[-1]     ,       '''j'''       )
           self.assertEqual(len(lowercase       )     ,       2_000       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size     ,       2_000       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	GPTSwaTokenizer(lowercase       )

           UpperCAmelCase     =	tokenizer.tokenize('''This is a test'''       )
           self.assertListEqual(lowercase     ,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase       )     ,       [465, 287, 265, 631, 842]       )

           UpperCAmelCase     =	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
           # fmt: off
           self.assertListEqual(
               lowercase     ,       ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']     ,       )
           # fmt: on

           UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
           self.assertListEqual(
               lowercase     ,       [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]     ,       )

           UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
           # fmt: off
           self.assertListEqual(
               lowercase     ,       ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']       )
           # fmt: on
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	GPTSwaTokenizer(lowercase       )
           UpperCAmelCase     =	['''This is a test''', '''I was born in 92000, and this is falsé.''']
           UpperCAmelCase     =	[
               [465, 287, 265, 631, 842],
               [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
           ]

           # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
           for text, expected_ids in zip(lowercase     ,       lowercase       ):
               self.assertListEqual(tokenizer.encode_fast(lowercase       )     ,       lowercase       )

           # Test that decode_fast returns the input text
           for text, token_ids in zip(lowercase     ,       lowercase       ):
               self.assertEqual(tokenizer.decode_fast(lowercase       )     ,       lowercase       )







       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	[
               '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
               '''Hey there, how are you doing this fine day?''',
               '''This is a text with a trailing spaces followed by a dot     .''',
               '''Häj sväjs lillebrör! =)''',
               '''Det är inget fel på Mr. Cool''',
           ]

           # fmt: off
           UpperCAmelCase     =	{'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
           # fmt: on
           self.tokenizer_integration_test_util(
               expected_encoding=lowercase     ,       model_name='''AI-Sweden/gpt-sw3-126m'''     ,       sequences=lowercase     ,       )










'''simple docstring'''







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=logging.get_logger(__name__)

A									={
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class     _a						(			__a				):
       __a       :  Union[str, Any]            =      """encodec"""
       def __init__(			self		:			Optional[int]     ,       lowercase		:			Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,       lowercase		:			str=24_000     ,       lowercase		:			int=1     ,       lowercase		:			int=False     ,       lowercase		:			List[str]=None     ,       lowercase		:			Tuple=None     ,       lowercase		:			int=128     ,       lowercase		:			List[Any]=32     ,       lowercase		:			Optional[int]=1     ,       lowercase		:			Optional[int]=[8, 5, 4, 2]     ,       lowercase		:			Union[str, Any]="weight_norm"     ,       lowercase		:			Optional[int]=7     ,       lowercase		:			Tuple=7     ,       lowercase		:			Tuple=3     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Tuple="reflect"     ,       lowercase		:			Any=2     ,       lowercase		:			List[str]=2     ,       lowercase		:			str=1.0     ,       lowercase		:			List[str]=1_024     ,       lowercase		:			Optional[Any]=None     ,       lowercase		:			Any=True     ,       **lowercase		:			Optional[Any]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	target_bandwidths
           UpperCAmelCase     =	sampling_rate
           UpperCAmelCase     =	audio_channels
           UpperCAmelCase     =	normalize
           UpperCAmelCase     =	chunk_length_s
           UpperCAmelCase     =	overlap
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_filters
           UpperCAmelCase     =	num_residual_layers
           UpperCAmelCase     =	upsampling_ratios
           UpperCAmelCase     =	norm_type
           UpperCAmelCase     =	kernel_size
           UpperCAmelCase     =	last_kernel_size
           UpperCAmelCase     =	residual_kernel_size
           UpperCAmelCase     =	dilation_growth_rate
           UpperCAmelCase     =	use_causal_conv
           UpperCAmelCase     =	pad_mode
           UpperCAmelCase     =	compress
           UpperCAmelCase     =	num_lstm_layers
           UpperCAmelCase     =	trim_right_ratio
           UpperCAmelCase     =	codebook_size
           UpperCAmelCase     =	codebook_dim if codebook_dim is not None else hidden_size
           UpperCAmelCase     =	use_conv_shortcut

           if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"       )

           super().__init__(**lowercase       )
       @property
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           if self.chunk_length_s is None:
               return None
           else:
               return int(self.chunk_length_s * self.sampling_rate       )
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           if self.chunk_length_s is None or self.overlap is None:
               return None
           else:
               return max(1     ,       int((1.0 - self.overlap) * self.chunk_length       )       )
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	np.prod(self.upsampling_ratios       )
           return math.ceil(self.sampling_rate / hop_length       )







       @property
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )




