


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


UpperCAmelCase   :    Optional[Any]     =		logging.get_logger(__name__)

UpperCAmelCase   :    Dict     =		{
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		"transfo-xl"
		UpperCamelCase     :					int			       =		["mems"]
		UpperCamelCase     :					int			       =		{
		    "n_token": "vocab_size",
		    "hidden_size": "d_model",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}







		def __init__( self		,      A=26_77_35		,      A=[2_00_00, 4_00_00, 20_00_00]		,      A=10_24		,      A=10_24		,      A=16		,      A=64		,      A=40_96		,      A=4		,      A=False		,      A=18		,      A=16_00		,      A=10_00		,      A=True		,      A=True		,      A=0		,      A=-1		,      A=True		,      A=0.1		,      A=0.0		,      A=True		,      A="normal"		,      A=0.01		,      A=0.01		,      A=0.02		,      A=1e-5		,      A=0		,      **A		,      )  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  []
					self.cutoffs.extend(A	)
					if proj_share_all_but_first:
								lowerCamelCase    		=  [False] + [True] * len(self.cutoffs	)
					else:
								lowerCamelCase    		=  [False] + [False] * len(self.cutoffs	)
					lowerCamelCase    		=  d_model
					lowerCamelCase    		=  d_embed
					lowerCamelCase    		=  d_head
					lowerCamelCase    		=  d_inner
					lowerCamelCase    		=  div_val
					lowerCamelCase    		=  pre_lnorm
					lowerCamelCase    		=  n_layer
					lowerCamelCase    		=  n_head
					lowerCamelCase    		=  mem_len
					lowerCamelCase    		=  same_length
					lowerCamelCase    		=  attn_type
					lowerCamelCase    		=  clamp_len
					lowerCamelCase    		=  sample_softmax
					lowerCamelCase    		=  adaptive
					lowerCamelCase    		=  dropout
					lowerCamelCase    		=  dropatt
					lowerCamelCase    		=  untie_r
					lowerCamelCase    		=  init
					lowerCamelCase    		=  init_range
					lowerCamelCase    		=  proj_init_std
					lowerCamelCase    		=  init_std
					lowerCamelCase    		=  layer_norm_epsilon
					super().__init__(eos_token_id=A		,      **A	)







		@property
		def 					__A   ( self	)  -> Dict:







					'''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 					__A   ( self		,      A	)  -> Union[str, Any]:







					'''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   :    Any     =		{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Union[str, Any]     =		[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		UpperCAmelCase   :    Tuple     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

