





'''simple docstring'''



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


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

__magic_name__   :						List[Any]    =			{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



					'''simple docstring'''






					UpperCAmelCase__       :						List[str]      =   '''biogpt'''

					def __init__(					self						,   lowerCamelCase=42_384						,   lowerCamelCase=1_024						,   lowerCamelCase=24						,   lowerCamelCase=16						,   lowerCamelCase=4_096						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=1_024						,   lowerCamelCase=0.02						,   lowerCamelCase=1e-12						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=0.0						,   lowerCamelCase=0.0						,   lowerCamelCase=1						,   lowerCamelCase=0						,   lowerCamelCase=2						,   **lowerCamelCase						,   ):
							_snake_case     		=				vocab_size
							_snake_case     		=				max_position_embeddings
							_snake_case     		=				hidden_size
							_snake_case     		=				num_hidden_layers
							_snake_case     		=				num_attention_heads
							_snake_case     		=				intermediate_size
							_snake_case     		=				hidden_act
							_snake_case     		=				hidden_dropout_prob
							_snake_case     		=				attention_probs_dropout_prob
							_snake_case     		=				initializer_range
							_snake_case     		=				layer_norm_eps
							_snake_case     		=				scale_embedding
							_snake_case     		=				use_cache
							_snake_case     		=				layerdrop
							_snake_case     		=				activation_dropout
							super().__init__(pad_token_id=A__						,   bos_token_id=A__						,   eos_token_id=A__						,   **A__	)









'''simple docstring'''



from typing import TYPE_CHECKING

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


__magic_name__   :						Any    =			{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

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


