


import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class    UpperCAmelCase_	(							unittest.TestCase):
       __UpperCamelCase      :     Optional[int]							=					JukeboxTokenizer
       __UpperCamelCase      :     List[str]							=					{
           "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  _lowercase   (     self						):




              """simple docstring"""




              import torch

              UpperCamelCase    :	Tuple   	=		JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''						)
              UpperCamelCase    :	Optional[int]   	=		tokenizer(**self.metas						)["input_ids"]
              # fmt: off
              UpperCamelCase    :	List[str]   	=		[
                  torch.tensor([[
                      0, 0, 0, 7_169, 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, 1_069, 11]]						),
                  torch.tensor([[0, 0, 0, 1_069, 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  _lowercase   (     self						):




              """simple docstring"""




              import torch

              UpperCamelCase    :	Tuple   	=		JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''						)
              UpperCamelCase    :	List[str]   	=		tokenizer(**self.metas						)["input_ids"]
              # fmt: off
              UpperCamelCase    :	int   	=		[
                  torch.tensor([[
                      0, 0, 0, 1_069, 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, 1_069, 11, -1, -1, -1, -1]]						),
                  torch.tensor([[0, 0, 0, 1_069, 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 torch

from transformers import AutoModel



class    UpperCAmelCase_	(							torch.nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"						):




              """simple docstring"""




              super(__SCREAMING_SNAKE_CASE ,					self						).__init__()

              UpperCamelCase    :	List[str]   	=		AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		torch.nn.CosineSimilarity(3 ,					1e-08						)
              UpperCamelCase    :	List[Any]   	=		torch.nn.Softmax(dim=1						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.bert(**__SCREAMING_SNAKE_CASE						).last_hidden_state






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return token_embeddings.sum(2 ,					keepdim=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=1						):




              """simple docstring"""




              return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		W_supports['''sizes'''].tolist()
              UpperCamelCase    :	Optional[int]   	=		W_supports['''start_token_id'''].item()
              UpperCamelCase    :	Any   	=		W_supports['''end_token_id'''].item()

              del W_supports["sizes"]
              del W_supports["start_token_id"]
              del W_supports["end_token_id"]

              UpperCamelCase    :	Union[str, Any]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		None
              UpperCamelCase    :	Any   	=		None

              UpperCamelCase    :	Optional[Any]   	=		W_supports['''input_ids'''] == start_token_id
              UpperCamelCase    :	Any   	=		W_supports['''input_ids'''] == end_token_id

              for i, size in enumerate(__SCREAMING_SNAKE_CASE						):
                     if i == 0:
                            UpperCamelCase    :	Optional[int]   	=		0
                     else:
                            UpperCamelCase    :	Tuple   	=		support_sizes[i - 1]

                     UpperCamelCase    :	Tuple   	=		S[s : s + size][start_token_masks[s : s + size]]
                     UpperCamelCase    :	List[str]   	=		S[s : s + size][end_token_masks[s : s + size]]

                     UpperCamelCase    :	Dict   	=		torch.matmul(q[i] ,					s_start.T						).sum(1						).softmax(0						)
                     UpperCamelCase    :	Tuple   	=		torch.matmul(q[i] ,					s_end.T						).sum(1						).softmax(0						)

                     if p_starts is not None:
                            UpperCamelCase    :	List[str]   	=		torch.vstack((p_starts, p_start)						)
                            UpperCamelCase    :	Union[str, Any]   	=		torch.vstack((p_ends, p_end)						)
                     else:
                            UpperCamelCase    :	str   	=		p_start
                            UpperCamelCase    :	Optional[int]   	=		p_end

              return p_starts, p_ends

