'''simple docstring'''




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


__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger(__name__)

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

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						"gpt_neox_japanese"




      def __init__(       self						:				Any						,					A						:				str=32000						,					A						:				Tuple=2560						,					A						:				Dict=32						,					A						:				Optional[Any]=32						,					A						:				Any=4						,					A						:				Any="gelu"						,					A						:				Tuple=1.00						,					A						:				List[Any]=10000						,					A						:				Optional[Any]=2048						,					A						:				Optional[int]=0.02						,					A						:				Optional[int]=1E-5						,					A						:				Tuple=True						,					A						:				str=31996						,					A						:				Tuple=31999						,					A						:				Dict=0.1						,					A						:				Optional[Any]=0.0						,					**A						:				int						,					):
             super().__init__(bos_token_id=A						,					eos_token_id=A						,					**A       )
             _UpperCAmelCase     :  Optional[int]							      =    vocab_size
             _UpperCAmelCase     :  List[Any]							      =    max_position_embeddings
             _UpperCAmelCase     :  Dict							      =    hidden_size
             _UpperCAmelCase     :  Union[str, Any]							      =    num_hidden_layers
             _UpperCAmelCase     :  Tuple							      =    num_attention_heads
             _UpperCAmelCase     :  str							      =    intermediate_multiple_size
             _UpperCAmelCase     :  Tuple							      =    hidden_act
             _UpperCAmelCase     :  Union[str, Any]							      =    rotary_pct
             _UpperCAmelCase     :  Dict							      =    rotary_emb_base
             _UpperCAmelCase     :  Optional[Any]							      =    initializer_range
             _UpperCAmelCase     :  Tuple							      =    layer_norm_eps
             _UpperCAmelCase     :  Any							      =    use_cache
             _UpperCAmelCase     :  str							      =    attention_dropout
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_dropout


'''simple docstring'''




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Tuple     =  ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Dict     =  [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE       :     Any     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

