
'''simple docstring'''




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch





class 					A							(				unittest.TestCase		):
     __magic_name__          =						JukeboxTokenizer
     __magic_name__          =						{
         '''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      )					->							List[str]:






         """simple docstring"""






         import torch

         A			:  str   =		JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''      )
         A			:  Tuple   =		tokenizer(**self.metas      )['''input_ids''']
         # fmt: off
         A			:  Any   =		[
             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

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






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							List[str]       						=     logging.get_logger(__name__)

lowercase     :							Optional[int]       						=     {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''switch_transformers'''
     __magic_name__          =						['''past_key_values''']
     __magic_name__          =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(				self		,     SCREAMING_SNAKE_CASE=32128		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=0.01		,     SCREAMING_SNAKE_CASE="float32"		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=128		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0		,     SCREAMING_SNAKE_CASE=1		,     **SCREAMING_SNAKE_CASE		,     )					->							List[Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		vocab_size
         A			:  Any   =		d_model
         A			:  Dict   =		d_kv
         A			:  Optional[Any]   =		d_ff

         A			:  List[Any]   =		num_sparse_encoder_layers

         A			:  List[Any]   =		num_layers
         A			:  Optional[int]   =		(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A			:  List[str]   =		num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
             A			:  int   =		self.num_layers // self.num_sparse_encoder_layers
         else:
             A			:  int   =		self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
             A			:  str   =		self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
             A			:  Dict   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

         A			:  List[Any]   =		num_heads
         A			:  str   =		num_experts
         A			:  Dict   =		expert_capacity
         A			:  Optional[int]   =		router_bias
         A			:  Any   =		router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'      )
         A			:  Dict   =		router_dtype

         A			:  int   =		router_ignore_padding_tokens
         A			:  List[Any]   =		relative_attention_num_buckets
         A			:  Dict   =		relative_attention_max_distance

         A			:  Optional[int]   =		dropout_rate
         A			:  Dict   =		layer_norm_epsilon
         A			:  Optional[int]   =		initializer_factor
         A			:  Union[str, Any]   =		feed_forward_proj
         A			:  Any   =		use_cache
         A			:  Tuple   =		add_router_probs

         A			:  List[str]   =		router_z_loss_coef
         A			:  str   =		router_aux_loss_coef

         A			:  Union[str, Any]   =		self.feed_forward_proj.split('''-'''      )
         A			:  Optional[Any]   =		act_info[-1]
         A			:  Union[str, Any]   =		act_info[0] == '''gated'''

         if len(SCREAMING_SNAKE_CASE      ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE      ) > 2:
             raise ValueError(
                 F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                 '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                 '''\'gated-gelu\' or \'relu\''''      )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
             A			:  str   =		'''gelu_new'''

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE		,     eos_token_id=SCREAMING_SNAKE_CASE		,     is_encoder_decoder=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE		,     )




