



'''simple docstring'''







import warnings

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


_UpperCamelCase					:  str        =logging.get_logger(__name__)

_UpperCamelCase					:  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 						_SCREAMING_SNAKE_CASE      ( UpperCamelCase						):






			"""simple docstring"""







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


			def __init__(   self       , _snake_case=3_20_00       , _snake_case=10_24       , _snake_case=24       , _snake_case=16       , _snake_case=40_96       , _snake_case="gelu"       , _snake_case=True       , _snake_case="bi"       , _snake_case=0.0_2       , _snake_case=1E-12       , _snake_case=0.1       , _snake_case=5_12       , _snake_case=None       , _snake_case=True       , _snake_case=False       , _snake_case=False       , _snake_case=-1       , _snake_case=False       , _snake_case="last"       , _snake_case=True       , _snake_case="tanh"       , _snake_case=0.1       , _snake_case=5       , _snake_case=5       , _snake_case=5       , _snake_case=1       , _snake_case=2       , **_snake_case       , ):







				"""simple docstring"""



				__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.'''       , __UpperCAmelCase       , )
					__lowerCamelCase		       =		kwargs['''use_cache''']

				__lowerCamelCase		       =		use_mems_eval
				__lowerCamelCase		       =		use_mems_train
				super().__init__(pad_token_id=__UpperCAmelCase       , bos_token_id=__UpperCAmelCase       , eos_token_id=__UpperCAmelCase       , **__UpperCAmelCase    )


			@property
			def 						_lowerCamelCase       (   self    ):







				"""simple docstring"""



				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 						_lowerCamelCase       (   self       , _snake_case    ):







				"""simple docstring"""



				raise NotImplementedError(
				    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''    )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


