


"""simple docstring"""


import warnings

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


A     :   Optional[int]			    =			logging.get_logger(__name__)

A     :   Optional[Any]			    =			{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    int				      ="""xlnet"""
							__UpperCAmelCase						:    List[str]				      =["""mems"""]
							__UpperCAmelCase						:    Optional[Any]				      ={
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}

							def __init__(     self    ,   __a=3_20_00    ,   __a=10_24    ,   __a=24    ,   __a=16    ,   __a=40_96    ,   __a="gelu"    ,   __a=True    ,   __a="bi"    ,   __a=0.0_2    ,   __a=1e-1_2    ,   __a=0.1    ,   __a=5_12    ,   __a=None    ,   __a=True    ,   __a=False    ,   __a=False    ,   __a=-1    ,   __a=False    ,   __a="last"    ,   __a=True    ,   __a="tanh"    ,   __a=0.1    ,   __a=5    ,   __a=5    ,   __a=5    ,   __a=1    ,   __a=2    ,   **__a    ,   ):
										__lowerCAmelCase				    =			vocab_size
										__lowerCAmelCase				    =			d_model
										__lowerCAmelCase				    =			n_layer
										__lowerCAmelCase				    =			n_head
										if d_model % n_head != 0:
													raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0"	)
										if "d_head" in kwargs:
													if kwargs["d_head"] != d_model // n_head:
																raise ValueError(
																    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"	)
										__lowerCAmelCase				    =			d_model // n_head
										__lowerCAmelCase				    =			ff_activation
										__lowerCAmelCase				    =			d_inner
										__lowerCAmelCase				    =			untie_r
										__lowerCAmelCase				    =			attn_type

										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			layer_norm_eps

										__lowerCAmelCase				    =			dropout
										__lowerCAmelCase				    =			mem_len
										__lowerCAmelCase				    =			reuse_len
										__lowerCAmelCase				    =			bi_data
										__lowerCAmelCase				    =			clamp_len
										__lowerCAmelCase				    =			same_length

										__lowerCAmelCase				    =			summary_type
										__lowerCAmelCase				    =			summary_use_proj
										__lowerCAmelCase				    =			summary_activation
										__lowerCAmelCase				    =			summary_last_dropout
										__lowerCAmelCase				    =			start_n_top
										__lowerCAmelCase				    =			end_n_top

										__lowerCAmelCase				    =			bos_token_id
										__lowerCAmelCase				    =			pad_token_id
										__lowerCAmelCase				    =			eos_token_id

										if "use_cache" in kwargs:
													warnings.warn(
													    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
													    " instead."    ,   __a    ,   )
													__lowerCAmelCase				    =			kwargs["use_cache"]

										__lowerCAmelCase				    =			use_mems_eval
										__lowerCAmelCase				    =			use_mems_train
										super().__init__(pad_token_id=__a    ,   bos_token_id=__a    ,   eos_token_id=__a    ,   **__a	)

							@property
							def 			snake_case				(     self	):
										logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."	)
										return -1



							@max_position_embeddings.setter
							def 			snake_case				(     self    ,   __a	):
										# Message copied from Transformer-XL documentation
										raise NotImplementedError(
										    f"The model {self.model_type} is one of the few models that has no sequence length limit."	)








"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ):
						raise ValueError("iterations must be defined as integers"      )
			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ) or not number >= 1:
						raise ValueError(
						    "starting number must be\n                         and integer and be more than 0"      )
			if not iterations >= 1:
						raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"      )

			__lowerCAmelCase				    =			""
			while number <= iterations:
						if number % 3 == 0:
									out += "Fizz"
						if number % 5 == 0:
									out += "Buzz"
						if 0 not in (number % 3, number % 5):
									out += str(_UpperCamelCase      )

						# print(out)
						number += 1
						out += " "
			return out


if __name__ == "__main__":
	import doctest

	doctest.testmod()




