






"""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


lowerCAmelCase_	:    str            =      get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')





@require_sentencepiece
@require_tokenizers
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					Optional[int]				=	GPTSwaTokenizer
  _A       :					List[str]				=	False
  _A       :					Union[str, Any]				=	True
  _A       :					str				=	False



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         super().setUp()

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

         tokenizer.save_pretrained(self.tmpdirname				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			"""This is a test"""
         UpperCAmelCase	    =			"""This is a test"""
         return input_text, output_text



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			"""<s>"""
         UpperCAmelCase	    =			1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__				) ,		snake_case__				)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__				) ,		snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """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(snake_case__				) ,		20_00				)



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



         """simple docstring"""





         self.assertEqual(self.get_tokenizer().vocab_size ,		20_00				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			GPTSwaTokenizer(snake_case__				)

         UpperCAmelCase	    =			tokenizer.tokenize("""This is a test"""				)
         self.assertListEqual(snake_case__ ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				) ,		[4_65, 2_87, 2_65, 6_31, 8_42]				)

         UpperCAmelCase	    =			tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
         # fmt: off
         self.assertListEqual(
             snake_case__ ,		["""▁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(snake_case__				)
         self.assertListEqual(
             snake_case__ ,		[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60] ,		)

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



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			GPTSwaTokenizer(snake_case__				)
         UpperCAmelCase	    =			["""This is a test""", """I was born in 92000, and this is falsé."""]
         UpperCAmelCase	    =			[
             [4_65, 2_87, 2_65, 6_31, 8_42],
             [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
         ]

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

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



  @slow
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """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""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 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=snake_case__ ,		model_name="""AI-Sweden/gpt-sw3-126m""" ,		sequences=snake_case__ ,		)








"""simple docstring"""







import math
from typing import Optional

import numpy as np

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


lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[Any]            =      {
    '''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   UpperCamelCase_ (       a_  ):
  _A       :					Dict				=	'encodec'



  def __init__(      self ,		snake_case__=[1.5, 3.0, 6.0, 12.0, 24.0] ,		snake_case__=2_40_00 ,		snake_case__=1 ,		snake_case__=False ,		snake_case__=None ,		snake_case__=None ,		snake_case__=1_28 ,		snake_case__=32 ,		snake_case__=1 ,		snake_case__=[8, 5, 4, 2] ,		snake_case__="weight_norm" ,		snake_case__=7 ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=2 ,		snake_case__=True ,		snake_case__="reflect" ,		snake_case__=2 ,		snake_case__=2 ,		snake_case__=1.0 ,		snake_case__=10_24 ,		snake_case__=None ,		snake_case__=True ,		**snake_case__ ,		)			->			List[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__(**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         if self.chunk_length_s is None:
                return None
         else:
                return int(self.chunk_length_s * self.sampling_rate				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """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 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





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



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)
