



'''simple docstring'''






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							lowercase       (  __lowerCAmelCase				):


 """simple docstring"""


 UpperCAmelCase							=  ['''image_processor''', '''tokenizer''']
 UpperCAmelCase							=  '''BlipImageProcessor'''
 UpperCAmelCase							=  ('''BertTokenizer''', '''BertTokenizerFast''')







 def __init__(							self  ,a_  ,a_							)		->   List[str]:
        _UpperCAmelCase  :						int			     = False
        super().__init__(lowerCAmelCase_  ,lowerCAmelCase_							)
        _UpperCAmelCase  :						Optional[Any]			     = self.image_processor







 def __call__(							self  ,a_ = None  ,a_ = None  ,a_ = True  ,a_ = False  ,a_ = None  ,a_ = None  ,a_ = 0  ,a_ = None  ,a_ = None  ,a_ = False  ,a_ = False  ,a_ = False  ,a_ = False  ,a_ = False  ,a_ = True  ,a_ = None  ,**a_  ,)		->   BatchEncoding:
        if images is None and text is None:
               raise ValueError("""You have to specify either images or text."""							)

        # Get only text
        if images is None:
               _UpperCAmelCase  :						int			     = self.tokenizer
               _UpperCAmelCase  :						str			     = self.tokenizer(
                   text=lowerCAmelCase_  ,add_special_tokens=lowerCAmelCase_  ,padding=lowerCAmelCase_  ,truncation=lowerCAmelCase_  ,max_length=lowerCAmelCase_  ,stride=lowerCAmelCase_  ,pad_to_multiple_of=lowerCAmelCase_  ,return_attention_mask=lowerCAmelCase_  ,return_overflowing_tokens=lowerCAmelCase_  ,return_special_tokens_mask=lowerCAmelCase_  ,return_offsets_mapping=lowerCAmelCase_  ,return_token_type_ids=lowerCAmelCase_  ,return_length=lowerCAmelCase_  ,verbose=lowerCAmelCase_  ,return_tensors=lowerCAmelCase_  ,**lowerCAmelCase_  ,)
               return text_encoding

        # add pixel_values
        _UpperCAmelCase  :						Optional[int]			     = self.image_processor(lowerCAmelCase_  ,return_tensors=lowerCAmelCase_							)

        if text is not None:
               _UpperCAmelCase  :						Tuple			     = self.tokenizer(
                   text=lowerCAmelCase_  ,add_special_tokens=lowerCAmelCase_  ,padding=lowerCAmelCase_  ,truncation=lowerCAmelCase_  ,max_length=lowerCAmelCase_  ,stride=lowerCAmelCase_  ,pad_to_multiple_of=lowerCAmelCase_  ,return_attention_mask=lowerCAmelCase_  ,return_overflowing_tokens=lowerCAmelCase_  ,return_special_tokens_mask=lowerCAmelCase_  ,return_offsets_mapping=lowerCAmelCase_  ,return_token_type_ids=lowerCAmelCase_  ,return_length=lowerCAmelCase_  ,verbose=lowerCAmelCase_  ,return_tensors=lowerCAmelCase_  ,**lowerCAmelCase_  ,)
        else:
               _UpperCAmelCase  :						List[str]			     = None

        if text_encoding is not None:
               encoding_image_processor.update(lowerCAmelCase_							)

        return encoding_image_processor







 def _snake_case (							self  ,*a_  ,**a_							)		->   Optional[Any]:
        return self.tokenizer.batch_decode(*lowerCAmelCase_  ,**lowerCAmelCase_							)







 def _snake_case (							self  ,*a_  ,**a_							)		->   Optional[int]:
        return self.tokenizer.decode(*lowerCAmelCase_  ,**lowerCAmelCase_							)







 @property
 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						List[Any]			     = self.tokenizer.model_input_names
        _UpperCAmelCase  :						List[Any]			     = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Tuple    			=		JukeboxTokenizer
 lowerCamelCase	:str    			=		{
     '''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      UpperCAmelCase							(					self							)  ->					Tuple:
        import torch

        _A        =  JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""							)
        _A        =  tokenizer(**self.metas							)["""input_ids"""]
        # fmt: off
        _A        =  [
            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      UpperCAmelCase							(					self							)  ->					List[str]:
        import torch

        _A        =  JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""							)
        _A        =  tokenizer(**self.metas							)["""input_ids"""]
        # fmt: off
        _A        =  [
            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]							)							)




