





'''simple docstring'''



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


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

__magic_name__   :						Dict    =			{
    """tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
        """https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
    ),
}


class       __SCREAMING_SNAKE_CASE	(						lowercase_			):



					'''simple docstring'''






					UpperCAmelCase__       :						Union[str, Any]      =   '''gptsan-japanese'''
					UpperCAmelCase__       :						Optional[Any]      =   [
					    '''past_key_values''',
					]
					UpperCAmelCase__       :						List[str]      =   {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}

					def __init__(					self						,   lowerCamelCase=36_000						,   lowerCamelCase=1_280						,   lowerCamelCase=1_024						,   lowerCamelCase=8_192						,   lowerCamelCase=4_096						,   lowerCamelCase=128						,   lowerCamelCase=10						,   lowerCamelCase=0						,   lowerCamelCase=16						,   lowerCamelCase=16						,   lowerCamelCase=128						,   lowerCamelCase=0.0						,   lowerCamelCase=1e-5						,   lowerCamelCase=False						,   lowerCamelCase=0.0						,   lowerCamelCase="float32"						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=0.002						,   lowerCamelCase=False						,   lowerCamelCase=True						,   lowerCamelCase=35_998						,   lowerCamelCase=35_995						,   lowerCamelCase=35_999						,   **lowerCamelCase						,   ):
							_snake_case     		=				vocab_size
							_snake_case     		=				max_position_embeddings
							_snake_case     		=				d_model
							_snake_case     		=				d_ff
							_snake_case     		=				d_ext
							_snake_case     		=				d_spout
							_snake_case     		=				num_switch_layers
							_snake_case     		=				num_ext_layers
							_snake_case     		=				num_switch_layers + num_ext_layers
							_snake_case     		=				num_heads
							_snake_case     		=				num_experts
							_snake_case     		=				expert_capacity
							_snake_case     		=				dropout_rate
							_snake_case     		=				layer_norm_epsilon
							_snake_case     		=				router_bias
							_snake_case     		=				router_jitter_noise
							_snake_case     		=				router_dtype
							_snake_case     		=				router_ignore_padding_tokens
							_snake_case     		=				output_hidden_states
							_snake_case     		=				output_attentions
							_snake_case     		=				initializer_factor
							_snake_case     		=				output_router_logits
							_snake_case     		=				use_cache

							super().__init__(
							    separator_token_id=lowerCamelCase						,   pad_token_id=lowerCamelCase						,   eos_token_id=lowerCamelCase						,   **lowerCamelCase						,   )









'''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__)


