

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'gpt_neox_japanese'


			def __init__(			self					,					lowercase=32000					,					lowercase=2560					,					lowercase=32					,					lowercase=32					,					lowercase=4					,					lowercase="gelu"					,					lowercase=1.00					,					lowercase=10000					,					lowercase=2048					,					lowercase=0.02					,					lowercase=1e-5					,					lowercase=True					,					lowercase=31996					,					lowercase=31999					,					lowercase=0.1					,					lowercase=0.0					,					**lowercase					,					)     ->  Dict:
							'''simple docstring'''






							super().__init__(bos_token_id=lowercase					,					eos_token_id=lowercase					,					**lowercase				)
							A__						=	vocab_size
							A__						=	max_position_embeddings
							A__						=	hidden_size
							A__						=	num_hidden_layers
							A__						=	num_attention_heads
							A__						=	intermediate_multiple_size
							A__						=	hidden_act
							A__						=	rotary_pct
							A__						=	rotary_emb_base
							A__						=	initializer_range
							A__						=	layer_norm_eps
							A__						=	use_cache
							A__						=	attention_dropout
							A__						=	hidden_dropout







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

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