


import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  []
       for line in lines:
              a_					:		Any							=  re.sub(R'#.*'  ,    ''  ,    __A				)  # remove comments
              if line:
                     filtered_lines.append(__A				)
       a_					:		Tuple							=  '\n'.join(__A				)

       # Make a hash from all this code
       a_					:		Tuple							=  full_str.encode('utf-8'				)
       return shaaaa(__A				).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase_      : List[Any]  					=       {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase_      : Dict  					=       {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase_      : Optional[int]  					=       {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
UpperCAmelCase_      : Dict[str, List[str]]  					=       {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')






"""simple docstring"""



import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch







class _lowerCAmelCase				(      unittest.TestCase	):
   __UpperCAmelCase :       Union[str, Any]							  =  JukeboxTokenizer
   __UpperCAmelCase :       Union[str, Any]							  =  {
       '''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  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       import torch

       snake_case    :      Any      					=    JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"						)
       snake_case    :      Optional[Any]      					=    tokenizer(**self.metas						)["input_ids"]
       # fmt: off
       snake_case    :      Optional[int]      					=    [
           torch.tensor([[
               0, 0, 0, 7169, 507, 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, 1069, 11]]						),
           torch.tensor([[0, 0, 0, 1069, 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  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       import torch

       snake_case    :      Tuple      					=    JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"						)
       snake_case    :      Optional[Any]      					=    tokenizer(**self.metas						)["input_ids"]
       # fmt: off
       snake_case    :      List[Any]      					=    [
           torch.tensor([[
               0, 0, 0, 1069, 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, 1069, 11, -1, -1, -1, -1]]						),
           torch.tensor([[0, 0, 0, 1069, 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]						)						)





