
'''simple docstring'''






import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch

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



    @require_torch
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          import torch

          _lowerCamelCase				:   Tuple								=   JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"     )
          _lowerCamelCase				:   Optional[int]								=   tokenizer(**self.metas     )["input_ids"]
          # fmt: off
          _lowerCamelCase				:   List[str]								=   [
              torch.tensor([[
                  0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
                  7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
                  4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
                  4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
                  7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
                  3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
                  2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
                  4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
                  4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
                  1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
                  7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
                  7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
                  6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
                  3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
                  2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
                  3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
                  2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
                  4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
                  7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
                  4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
                  7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
                  3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
                  4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
                  2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
                  4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
                  3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
                  4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
                  7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
                  3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
                  3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
                  4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
                  3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
                  7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
                  7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
                  4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
                  2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
                  4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
                  7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
                  4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
                  4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
                  4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
                  7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
                  4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
                  2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
                  7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
                  7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
                  4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
                  7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
                  2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
                  7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
                  3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
                  4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
                  4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
                  7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
                  4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
                  3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
                  4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
                  2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
                  2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
                  7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
                  7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
                  7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
                  7_6, 7_6]]     ),
              torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]]     ),
              torch.tensor([[0, 0, 0, 1_0_6_9, 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]     )     )



    @require_torch
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          import torch

          _lowerCamelCase				:   Tuple								=   JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"     )
          _lowerCamelCase				:   List[str]								=   tokenizer(**self.metas     )["input_ids"]
          # fmt: off
          _lowerCamelCase				:   int								=   [
              torch.tensor([[
                  0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
                  3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
                  3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
                  4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
                  7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
                  7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
                  2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
                  3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
                  3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
                  7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
                  7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
                  7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
                  4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
                  7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
                  7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
                  7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
                  7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
                  6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
                  4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
                  4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
                  3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
                  3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
                  4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
                  7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
                  4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
                  4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
                  3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
                  3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
                  2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
                  4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
                  3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
                  3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
                  4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
                  7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
                  2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
                  3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
                  3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
                  3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
                  1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
                  3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
                  4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
                  3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
                  7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
                  1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
                  1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
                  4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
                  4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
                  4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
                  4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
                  2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
                  7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
                  3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
                  7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
                  3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
                  7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
                  4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
                  7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
                  4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
                  7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
                  7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
                  2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
                  7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
                  7_7, 7_7, 7_7, 7_7, 7_7, 7_7]]     ),
              torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]]     ),
              torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -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]     )     )

'''simple docstring'''






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      random.seed(_lowerCAmelCase							)
      np.random.seed(_lowerCAmelCase							)
      torch.manual_seed(_lowerCAmelCase							)
      torch.cuda.manual_seed_all(_lowerCAmelCase							)
      # ^^ safe to call this function even if cuda is not available



class  UpperCAmelCase__						:



    def __init__(	self      :       List[str],__A      :       Iterable[torch.nn.Parameter],__A      :       float = 0.9999,__A      :       float = 0.0,__A      :       int = 0,__A      :       bool = False,__A      :       Union[float, int] = 1.0,__A      :       Union[float, int] = 2 / 3,__A      :       Optional[Any] = None,__A      :       Dict[str, Any] = None,**__A      :       Optional[Any],):

          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   Any								=   (
                    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
                    "Please pass the parameters of the module instead."
                )
                deprecate(
                    "passing a `torch.nn.Module` to `ExponentialMovingAverage`","1.0.0",__A,standard_warn=__A,)
                _lowerCamelCase				:   Dict								=   parameters.parameters()

                # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                _lowerCamelCase				:   Optional[Any]								=   True

          if kwargs.get("max_value",__A     ) is not None:
                _lowerCamelCase				:   Optional[int]								=   "The `max_value` argument is deprecated. Please use `decay` instead."
                deprecate("max_value","1.0.0",__A,standard_warn=__A     )
                _lowerCamelCase				:   Optional[Any]								=   kwargs["max_value"]

          if kwargs.get("min_value",__A     ) is not None:
                _lowerCamelCase				:   Tuple								=   "The `min_value` argument is deprecated. Please use `min_decay` instead."
                deprecate("min_value","1.0.0",__A,standard_warn=__A     )
                _lowerCamelCase				:   Any								=   kwargs["min_value"]

          _lowerCamelCase				:   Optional[int]								=   list(__A     )
          _lowerCamelCase				:   List[Any]								=   [p.clone().detach() for p in parameters]

          if kwargs.get("device",__A     ) is not None:
                _lowerCamelCase				:   Optional[int]								=   "The `device` argument is deprecated. Please use `to` instead."
                deprecate("device","1.0.0",__A,standard_warn=__A     )
                self.to(device=kwargs["device"]     )

          _lowerCamelCase				:   Tuple								=   None

          _lowerCamelCase				:   Dict								=   decay
          _lowerCamelCase				:   List[Any]								=   min_decay
          _lowerCamelCase				:   Optional[Any]								=   update_after_step
          _lowerCamelCase				:   Any								=   use_ema_warmup
          _lowerCamelCase				:   Union[str, Any]								=   inv_gamma
          _lowerCamelCase				:   str								=   power
          _lowerCamelCase				:   Union[str, Any]								=   0
          _lowerCamelCase				:   Union[str, Any]								=   None  # set in `step()`

          _lowerCamelCase				:   List[str]								=   model_cls
          _lowerCamelCase				:   Dict								=   model_config



    @classmethod
    def   lowerCamelCase_  (	cls      :       Union[str, Any],__A      :       List[str],__A      :       Optional[int]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   model_cls.load_config(__A,return_unused_kwargs=__A     )
          _lowerCamelCase				:   Optional[Any]								=   model_cls.from_pretrained(__A     )

          _lowerCamelCase				:   Union[str, Any]								=   cls(model.parameters(),model_cls=__A,model_config=model.config     )

          ema_model.load_state_dict(__A     )
          return ema_model



    def   lowerCamelCase_  (	self      :       str,__A      :       int     ):
          if self.model_cls is None:
                raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."     )

          if self.model_config is None:
                raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."     )

          _lowerCamelCase				:   Tuple								=   self.model_cls.from_config(self.model_config     )
          _lowerCamelCase				:   List[str]								=   self.state_dict()
          state_dict.pop("shadow_params",__A     )

          model.register_to_config(**__A     )
          self.copy_to(model.parameters()     )
          model.save_pretrained(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int     ):
          _lowerCamelCase				:   str								=   max(0,optimization_step - self.update_after_step - 1     )

          if step <= 0:
                return 0.0

          if self.use_ema_warmup:
                _lowerCamelCase				:   Tuple								=   1 - (1 + step / self.inv_gamma) ** -self.power
          else:
                _lowerCamelCase				:   List[str]								=   (1 + step) / (1_0 + step)

          _lowerCamelCase				:   Union[str, Any]								=   min(__A,self.decay     )
          # make sure decay is not smaller than min_decay
          _lowerCamelCase				:   Union[str, Any]								=   max(__A,self.min_decay     )
          return cur_decay_value



    @torch.no_grad()
    def   lowerCamelCase_  (	self      :       Any,__A      :       Iterable[torch.nn.Parameter]     ):
          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   Dict								=   (
                    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
                    "Please pass the parameters of the module instead."
                )
                deprecate(
                    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`","1.0.0",__A,standard_warn=__A,)
                _lowerCamelCase				:   Any								=   parameters.parameters()

          _lowerCamelCase				:   str								=   list(__A     )

          self.optimization_step += 1

          # Compute the decay factor for the exponential moving average.
          _lowerCamelCase				:   Dict								=   self.get_decay(self.optimization_step     )
          _lowerCamelCase				:   Optional[Any]								=   decay
          _lowerCamelCase				:   List[Any]								=   1 - decay

          _lowerCamelCase				:   List[Any]								=   contextlib.nullcontext
          if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                import deepspeed

          for s_param, param in zip(self.shadow_params,__A     ):
                if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                      _lowerCamelCase				:   List[Any]								=   deepspeed.zero.GatheredParameters(__A,modifier_rank=__A     )

                with context_manager():
                      if param.requires_grad:
                            s_param.sub_(one_minus_decay * (s_param - param)     )
                      else:
                            s_param.copy_(__A     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   list(__A     )
          for s_param, param in zip(self.shadow_params,__A     ):
                param.data.copy_(s_param.to(param.device     ).data     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Dict=None,__A      :       Any=None     ):
          _lowerCamelCase				:   int								=   [
              p.to(device=__A,dtype=__A     ) if p.is_floating_point() else p.to(device=__A     )
              for p in self.shadow_params
          ]



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return {
              "decay": self.decay,
              "min_decay": self.min_decay,
              "optimization_step": self.optimization_step,
              "update_after_step": self.update_after_step,
              "use_ema_warmup": self.use_ema_warmup,
              "inv_gamma": self.inv_gamma,
              "power": self.power,
              "shadow_params": self.shadow_params,
          }



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   [param.detach().cpu().clone() for param in parameters]



    def   lowerCamelCase_  (	self      :       int,__A      :       Iterable[torch.nn.Parameter]     ):
          if self.temp_stored_params is None:
                raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"     )
          for c_param, param in zip(self.temp_stored_params,__A     ):
                param.data.copy_(c_param.data     )

          # Better memory-wise.
          _lowerCamelCase				:   List[str]								=   None



    def   lowerCamelCase_  (	self      :       Dict,__A      :       dict     ):
          _lowerCamelCase				:   List[str]								=   copy.deepcopy(__A     )

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("decay",self.decay     )
          if self.decay < 0.0 or self.decay > 1.0:
                raise ValueError("Decay must be between 0 and 1"     )

          _lowerCamelCase				:   Dict								=   state_dict.get("min_decay",self.min_decay     )
          if not isinstance(self.min_decay,__A     ):
                raise ValueError("Invalid min_decay"     )

          _lowerCamelCase				:   Tuple								=   state_dict.get("optimization_step",self.optimization_step     )
          if not isinstance(self.optimization_step,__A     ):
                raise ValueError("Invalid optimization_step"     )

          _lowerCamelCase				:   Any								=   state_dict.get("update_after_step",self.update_after_step     )
          if not isinstance(self.update_after_step,__A     ):
                raise ValueError("Invalid update_after_step"     )

          _lowerCamelCase				:   List[Any]								=   state_dict.get("use_ema_warmup",self.use_ema_warmup     )
          if not isinstance(self.use_ema_warmup,__A     ):
                raise ValueError("Invalid use_ema_warmup"     )

          _lowerCamelCase				:   Tuple								=   state_dict.get("inv_gamma",self.inv_gamma     )
          if not isinstance(self.inv_gamma,(float, int)     ):
                raise ValueError("Invalid inv_gamma"     )

          _lowerCamelCase				:   Union[str, Any]								=   state_dict.get("power",self.power     )
          if not isinstance(self.power,(float, int)     ):
                raise ValueError("Invalid power"     )

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("shadow_params",__A     )
          if shadow_params is not None:
                _lowerCamelCase				:   str								=   shadow_params
                if not isinstance(self.shadow_params,__A     ):
                      raise ValueError("shadow_params must be a list"     )
                if not all(isinstance(__A,torch.Tensor     ) for p in self.shadow_params     ):
                      raise ValueError("shadow_params must all be Tensors"     )