


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


lowerCamelCase__							:						int							     =   logging.get_logger(__name__)

lowerCamelCase__							:						List[Any]							     =   {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Tuple	   =       'switch_transformers'
     __lowercase				:							Optional[int]	   =       ['past_key_values']
     __lowercase				:							Optional[int]	   =       {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

     def __init__(					self:Union[str, Any]     ,		_a:Tuple=3_21_28     ,		_a:Optional[Any]=7_68     ,		_a:Dict=64     ,		_a:Dict=20_48     ,		_a:Any=64     ,		_a:Optional[Any]=12     ,		_a:Tuple=3     ,		_a:List[Any]=12     ,		_a:Union[str, Any]=3     ,		_a:Optional[int]=12     ,		_a:Dict=8     ,		_a:Optional[Any]=False     ,		_a:Tuple=0.01     ,		_a:Tuple="float32"     ,		_a:Dict=False     ,		_a:List[str]=32     ,		_a:str=1_28     ,		_a:Tuple=0.1     ,		_a:List[str]=1e-6     ,		_a:List[Any]=0.001     ,		_a:Optional[Any]=0.001     ,		_a:List[Any]=1.0     ,		_a:Tuple="relu"     ,		_a:Any=True     ,		_a:Any=False     ,		_a:Dict=True     ,		_a:Any=0     ,		_a:int=1     ,		**_a:Optional[Any]     ,		):
       snake_case__						= vocab_size
       snake_case__						= d_model
       snake_case__						= d_kv
       snake_case__						= d_ff

       snake_case__						= num_sparse_encoder_layers

       snake_case__						= num_layers
       snake_case__						= (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       snake_case__						= 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:
         snake_case__						= self.num_layers // self.num_sparse_encoder_layers
       else:
         snake_case__						= 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:
         snake_case__						= self.num_decoder_layers // self.num_sparse_decoder_layers
       else:
         snake_case__						= self.num_decoder_layers  # HACK: this will create 0 sparse layers

       snake_case__						= num_heads
       snake_case__						= num_experts
       snake_case__						= expert_capacity
       snake_case__						= router_bias
       snake_case__						= 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}"""   )
       snake_case__						= router_dtype

       snake_case__						= router_ignore_padding_tokens
       snake_case__						= relative_attention_num_buckets
       snake_case__						= relative_attention_max_distance

       snake_case__						= dropout_rate
       snake_case__						= layer_norm_epsilon
       snake_case__						= initializer_factor
       snake_case__						= feed_forward_proj
       snake_case__						= use_cache
       snake_case__						= add_router_probs

       snake_case__						= router_z_loss_coef
       snake_case__						= router_aux_loss_coef

       snake_case__						= self.feed_forward_proj.split('''-'''   )
       snake_case__						= act_info[-1]
       snake_case__						= act_info[0] == '''gated'''

       if len(_a   ) > 1 and act_info[0] != "gated" or len(_a   ) > 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":
         snake_case__						= '''gelu_new'''

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






'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		     =  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Tuple  							=   GPTSwaTokenizer
							lowerCAmelCase			:       Tuple  							=   False
							lowerCAmelCase			:       Optional[Any]  							=   True
							lowerCAmelCase			:       Any  							=   False







							def 				__lowercase     (				self   :			Any						):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_a	: int								=				GPTSwaTokenizer(_UpperCAmelCase      ,eos_token='<unk>'      ,bos_token='<unk>'      ,pad_token='<unk>'						)

									tokenizer.save_pretrained(self.tmpdirname						)







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			int						):
									_a	: Any								=				'This is a test'
									_a	: Any								=				'This is a test'
									return input_text, output_text







							def 				__lowercase     (				self   :			str						):
									_a	: Optional[Any]								=				'<s>'
									_a	: str								=				1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)      ,_UpperCAmelCase						)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Dict								=				list(self.get_tokenizer().get_vocab().keys()						)

									self.assertEqual(vocab_keys[0]      ,'<unk>'						)
									self.assertEqual(vocab_keys[1]      ,'<s>'						)
									self.assertEqual(vocab_keys[-1]      ,'j'						)
									self.assertEqual(len(_UpperCAmelCase						)      ,2000						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.assertEqual(self.get_tokenizer().vocab_size      ,2000						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Union[str, Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)

									_a	: str								=				tokenizer.tokenize('This is a test'						)
									self.assertListEqual(_UpperCAmelCase      ,['▁This', '▁is', '▁a', '▁t', 'est']						)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)      ,[465, 287, 265, 631, 842]						)

									_a	: Any								=				tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      ,)
									# fmt: on

									_a	: Optional[Any]								=				tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
									self.assertListEqual(
									    _UpperCAmelCase      ,[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]      ,)

									_a	: List[str]								=				tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
									# fmt: on







							def 				__lowercase     (				self   :			str						):
									_a	: List[Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)
									_a	: Optional[Any]								=				['This is a test', 'I was born in 92000, and this is falsé.']
									_a	: Dict								=				[
									    [465, 287, 265, 631, 842],
									    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)

									# Test that decode_fast returns the input text
									for text, token_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				[
									    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
									    'Hey there, how are you doing this fine day?',
									    'This is a text with a trailing spaces followed by a dot     .',
									    'Häj sväjs lillebrör! =)',
									    'Det är inget fel på Mr. Cool',
									]

									# fmt: off
									_a	: Tuple								=				{'input_ids': [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase      ,model_name='AI-Sweden/gpt-sw3-126m'      ,sequences=_UpperCAmelCase      ,)




