





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


__A									=				logging.get_logger(__name__)

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



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"gpt_neox_japanese"

 def __init__(self				:   Dict       , UpperCAmelCase_				:   List[Any]=32_000       , UpperCAmelCase_				:   str=2_560       , UpperCAmelCase_				:   Optional[int]=32       , UpperCAmelCase_				:   Dict=32       , UpperCAmelCase_				:   Any=4       , UpperCAmelCase_				:   List[Any]="gelu"       , UpperCAmelCase_				:   Any=1.00       , UpperCAmelCase_				:   List[Any]=10_000       , UpperCAmelCase_				:   int=2_048       , UpperCAmelCase_				:   Tuple=0.02       , UpperCAmelCase_				:   str=1E-5       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   Any=31_996       , UpperCAmelCase_				:   Optional[int]=31_999       , UpperCAmelCase_				:   Any=0.1       , UpperCAmelCase_				:   Tuple=0.0       , **UpperCAmelCase_				:   Optional[int]       , )       ->Union[str, Any]:



   '''simple docstring'''



   super().__init__(bos_token_id=UpperCAmelCase_       , eos_token_id=UpperCAmelCase_       , **UpperCAmelCase_)
   lowerCamelCase__:      int			  =vocab_size
   lowerCamelCase__:      Optional[int]			  =max_position_embeddings
   lowerCamelCase__:      Tuple			  =hidden_size
   lowerCamelCase__:      List[str]			  =num_hidden_layers
   lowerCamelCase__:      List[Any]			  =num_attention_heads
   lowerCamelCase__:      str			  =intermediate_multiple_size
   lowerCamelCase__:      Dict			  =hidden_act
   lowerCamelCase__:      List[Any]			  =rotary_pct
   lowerCamelCase__:      Union[str, Any]			  =rotary_emb_base
   lowerCamelCase__:      Any			  =initializer_range
   lowerCamelCase__:      List[Any]			  =layer_norm_eps
   lowerCamelCase__:      Any			  =use_cache
   lowerCamelCase__:      Any			  =attention_dropout
   lowerCamelCase__:      Union[str, Any]			  =hidden_dropout








"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

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




