



"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case				:  Any =       {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Tuple =       [
	    'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ErnieForCausalLM',
	    'ErnieForMaskedLM',
	    'ErnieForMultipleChoice',
	    'ErnieForNextSentencePrediction',
	    'ErnieForPreTraining',
	    'ErnieForQuestionAnswering',
	    'ErnieForSequenceClassification',
	    'ErnieForTokenClassification',
	    'ErnieModel',
	    'ErniePreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ernie import (
		    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ErnieForCausalLM,
		    ErnieForMaskedLM,
		    ErnieForMultipleChoice,
		    ErnieForNextSentencePrediction,
		    ErnieForPreTraining,
		    ErnieForQuestionAnswering,
		    ErnieForSequenceClassification,
		    ErnieForTokenClassification,
		    ErnieModel,
		    ErniePreTrainedModel,
		)

else:
	import sys

	__snake_case				:  Dict =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

	SCREAMING_SNAKE_CASE        =      {
	    'num_attention_heads': 'attention_heads',
	    'hidden_size': 'd_model',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     decoder_start_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )