






import warnings

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


__lowerCamelCase :      str										=				logging.get_logger(__name__)

__lowerCamelCase :      Optional[int]										=				{
    """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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





		"""simple docstring"""






		a_     =		"xlnet"
		a_     =		["mems"]
		a_     =		{
		    "n_token": "vocab_size",  # Backward compatibility
		    "hidden_size": "d_model",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}



		def __init__( self					:  Tuple							,		__A					:  List[Any]=3_2_0_0_0							,		__A					:  Any=1_0_2_4							,		__A					:  Dict=2_4							,		__A					:  Dict=1_6							,		__A					:  Union[str, Any]=4_0_9_6							,		__A					:  int="gelu"							,		__A					:  Tuple=True							,		__A					:  Tuple="bi"							,		__A					:  List[Any]=0.0_2							,		__A					:  Union[str, Any]=1e-1_2							,		__A					:  Any=0.1							,		__A					:  Any=5_1_2							,		__A					:  Optional[Any]=None							,		__A					:  Optional[Any]=True							,		__A					:  Optional[Any]=False							,		__A					:  Dict=False							,		__A					:  int=-1							,		__A					:  str=False							,		__A					:  List[str]="last"							,		__A					:  List[Any]=True							,		__A					:  List[str]="tanh"							,		__A					:  Optional[Any]=0.1							,		__A					:  Dict=5							,		__A					:  int=5							,		__A					:  List[Any]=5							,		__A					:  Any=1							,		__A					:  int=2							,		**__A					:  int							,		):
			snake_case__			:				List[str]       =  vocab_size
			snake_case__			:				int       =  d_model
			snake_case__			:				Optional[int]       =  n_layer
			snake_case__			:				Tuple       =  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})'''	)
			snake_case__			:				Optional[int]       =  d_model // n_head
			snake_case__			:				Any       =  ff_activation
			snake_case__			:				Optional[int]       =  d_inner
			snake_case__			:				Union[str, Any]       =  untie_r
			snake_case__			:				Optional[int]       =  attn_type

			snake_case__			:				List[str]       =  initializer_range
			snake_case__			:				int       =  layer_norm_eps

			snake_case__			:				Optional[Any]       =  dropout
			snake_case__			:				Tuple       =  mem_len
			snake_case__			:				Union[str, Any]       =  reuse_len
			snake_case__			:				List[str]       =  bi_data
			snake_case__			:				List[str]       =  clamp_len
			snake_case__			:				Any       =  same_length

			snake_case__			:				Tuple       =  summary_type
			snake_case__			:				List[Any]       =  summary_use_proj
			snake_case__			:				List[str]       =  summary_activation
			snake_case__			:				Optional[Any]       =  summary_last_dropout
			snake_case__			:				Any       =  start_n_top
			snake_case__			:				Optional[Any]       =  end_n_top

			snake_case__			:				Union[str, Any]       =  bos_token_id
			snake_case__			:				Union[str, Any]       =  pad_token_id
			snake_case__			:				Optional[int]       =  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							,		)
				snake_case__			:				Tuple       =  kwargs["use_cache"]

			snake_case__			:				Optional[Any]       =  use_mems_eval
			snake_case__			:				List[str]       =  use_mems_train
			super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)



		@property
		def 	_lowercase					( self					:  Union[str, Any]	):
			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 	_lowercase					( self					:  str							,		__A					:  Dict	):
			# 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.'''	)











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



