



import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch

class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       JukeboxTokenizer
			UpperCamelCase									=       {
			    """artist""": """Zac Brown Band""",
			    """genres""": """Country""",
			    """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
			}




			@require_torch
			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					import torch

					__UpperCAmelCase    :     int													=	JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
					__UpperCAmelCase    :     List[str]													=	tokenizer(**self.metas )['''input_ids''']
					# fmt: off
					__UpperCAmelCase    :     Optional[Any]													=	[
					    torch.tensor([[
					        0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
					        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
					        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
					        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
					        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
					        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
					        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
					        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
					        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
					        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
					        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
					        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
					        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
					        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
					        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
					        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
					        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
					        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
					        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
					        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
					        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
					        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
					        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
					        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
					        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
					        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
					        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
					        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
					        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
					        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
					        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
					        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
					        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
					        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
					        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
					        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
					        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
					        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
					        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
					        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
					        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
					        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
					        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
					        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
					        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
					        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
					        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
					        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
					        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
					        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
					        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
					        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
					        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
					        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
					        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
					        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
					        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
					        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
					        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
					        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
					        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
					        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
					        76, 76]] ),
					    torch.tensor([[0, 0, 0, 10_69, 11]] ),
					    torch.tensor([[0, 0, 0, 10_69, 11]] ),
					]
					# fmt: on
					self.assertTrue(torch.allclose(tokens[0]       ,       EXPECTED_OUTPUT[0] ) )
					self.assertTrue(torch.allclose(tokens[1]       ,       EXPECTED_OUTPUT[1] ) )
					self.assertTrue(torch.allclose(tokens[2]       ,       EXPECTED_OUTPUT[2] ) )




			@require_torch
			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					import torch

					__UpperCAmelCase    :     Optional[Any]													=	JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
					__UpperCAmelCase    :     Optional[int]													=	tokenizer(**self.metas )['''input_ids''']
					# fmt: off
					__UpperCAmelCase    :     str													=	[
					    torch.tensor([[
					        0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
					        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
					        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
					        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
					        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
					        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
					        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
					        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
					        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
					        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
					        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
					        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
					        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
					        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
					        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
					        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
					        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
					        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
					        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
					        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
					        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
					        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
					        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
					        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
					        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
					        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
					        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
					        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
					        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
					        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
					        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
					        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
					        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
					        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
					        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
					        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
					        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
					        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
					        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
					        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
					        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
					        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
					        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
					        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
					        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
					        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
					        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
					        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
					        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
					        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
					        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
					        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
					        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
					        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
					        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
					        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
					        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
					        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
					        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
					        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
					        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
					        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
					        77, 77, 77, 77, 77, 77]] ),
					    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
					    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
					]
					# fmt: on
					self.assertTrue(torch.allclose(tokens[0]       ,       EXPECTED_OUTPUT[0] ) )
					self.assertTrue(torch.allclose(tokens[1]       ,       EXPECTED_OUTPUT[1] ) )
					self.assertTrue(torch.allclose(tokens[2]       ,       EXPECTED_OUTPUT[2] ) )










import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A   			=logging.get_logger(__name__)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""input_ids""", """attention_mask"""]




			def __init__(   self							: List[str]       ,       a_							: List[Any]="</s>"       ,       a_							: str="<unk>"       ,       a_							: Tuple="<pad>"       ,       a_							: List[Any]=1_25       ,       a_							: Any=None       ,       **a_							: Tuple       ,       ):
					'''simple docstring'''





					if extra_ids > 0 and additional_special_tokens is None:
							__UpperCAmelCase    :     Optional[Any]													=	[F'<extra_id_{i}>' for i in range(a_ )]
					elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							__UpperCAmelCase    :     Optional[Any]													=	len(set(filter(lambda a_ : bool('''extra_id''' in str(a_ ) )       ,       a_ ) ) )
							if extra_tokens != extra_ids:
									raise ValueError(
									    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
									    ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
									    ''' extra_ids tokens''' )

					__UpperCAmelCase    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else pad_token
					__UpperCAmelCase    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else eos_token
					__UpperCAmelCase    :     int													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else unk_token

					super().__init__(
					    eos_token=a_       ,       unk_token=a_       ,       pad_token=a_       ,       extra_ids=a_       ,       additional_special_tokens=a_       ,       **a_       ,       )

					__UpperCAmelCase    :     Dict													=	extra_ids

					__UpperCAmelCase    :     Any													=	2**8  # utf is 8 bits

					# define special tokens dict
					__UpperCAmelCase    :     Dict[int, str]													=	{
					    self.pad_token: 0,
					    self.eos_token: 1,
					    self.unk_token: 2,
					}
					__UpperCAmelCase    :     Optional[int]													=	len(self.special_tokens_encoder )
					__UpperCAmelCase    :     Union[str, Any]													=	len(a_ )
					for i, token in enumerate(a_ ):
							__UpperCAmelCase    :     Union[str, Any]													=	self.vocab_size + i - n
					__UpperCAmelCase    :     Dict[str, int]													=	{v: k for k, v in self.special_tokens_encoder.items()}




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




			def  snake_case__     (   self							: Any       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None       ,       a_							: bool = False ):
					'''simple docstring'''





					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=a_       ,       token_ids_a=a_       ,       already_has_special_tokens=a_ )

					# normal case: some special tokens
					if token_ids_a is None:
							return ([0] * len(a_ )) + [1]
					return ([0] * len(a_ )) + [1] + ([0] * len(a_ )) + [1]




			def  snake_case__     (   self							: Any       ,       a_							: List[int] ):
					'''simple docstring'''





					if len(a_ ) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
							    ''' eos tokens being added.''' )
							return token_ids
					else:
							return token_ids + [self.eos_token_id]




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[self.eos_token_id]

					if token_ids_a is None:
							return len(token_ids_a + eos ) * [0]
					return len(token_ids_a + eos + token_ids_a + eos ) * [0]




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self._add_eos_if_not_present(a_ )
					if token_ids_a is None:
							return token_ids_a
					else:
							__UpperCAmelCase    :     List[str]													=	self._add_eos_if_not_present(a_ )
							return token_ids_a + token_ids_a




			def  snake_case__     (   self							: List[Any]       ,       a_							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[chr(a_ ) for i in text.encode('''utf-8''' )]
					return tokens




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int ):
					'''simple docstring'''





					if token in self.special_tokens_encoder:
							__UpperCAmelCase    :     Union[str, Any]													=	self.special_tokens_encoder[token]
					elif token in self.added_tokens_encoder:
							__UpperCAmelCase    :     Optional[Any]													=	self.added_tokens_encoder[token]
					elif len(a_ ) != 1:
							__UpperCAmelCase    :     int													=	self.unk_token_id
					else:
							__UpperCAmelCase    :     List[str]													=	ord(a_ ) + self._num_special_tokens
					return token_id




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Optional[int] ):
					'''simple docstring'''





					if index in self.special_tokens_decoder:
							__UpperCAmelCase    :     Dict													=	self.special_tokens_decoder[index]
					else:
							__UpperCAmelCase    :     List[Any]													=	chr(index - self._num_special_tokens )
					return token




			def  snake_case__     (   self							: int       ,       a_							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	b''''''
					for token in tokens:
							if token in self.special_tokens_decoder:
									__UpperCAmelCase    :     Optional[int]													=	self.special_tokens_decoder[token].encode('''utf-8''' )
							elif token in self.added_tokens_decoder:
									__UpperCAmelCase    :     List[str]													=	self.special_tokens_decoder[token].encode('''utf-8''' )
							elif token in self.special_tokens_encoder:
									__UpperCAmelCase    :     Tuple													=	token.encode('''utf-8''' )
							elif token in self.added_tokens_encoder:
									__UpperCAmelCase    :     int													=	token.encode('''utf-8''' )
							else:
									__UpperCAmelCase    :     Any													=	bytes([ord(a_ )] )
							bstring += tok_string
					__UpperCAmelCase    :     Dict													=	bstring.decode('''utf-8'''       ,       errors='''ignore''' )
					return string




			def  snake_case__     (   self							: Tuple       ,       a_							: str       ,       a_							: Optional[str] = None ):
					'''simple docstring'''





					return ()





